Introducing & integrating EU BON common tools M34

Milestone MS517 Version: 5.0

Date: 2016-09-16

Author: C. Rodriguez et al.

Document reference: Milestone_MS517

Introducing & integrating EU BON common tools

M34

STATUS: FINAL V1

Project acronym: EU BON

Project name: EU BON: Building the European Biodiversity Observation Network

Call: ENV.2012.6.2-2

Grant agreement: 308454

Project Duration: 01/12/2012 – 31/05/2017 (54 months)

Co-ordinator: MfN, Museum für Naturkunde - Leibniz Institute for Research on Evolution and Biodiversity,

Germany

Partners: UTARTU, University of Tartu, Natural History Museum , Estonia

UEF, University of Eastern Finland, Digitisation Centre, Finland

GBIF, Global Biodiversity Information Facility, Denmark

UnivLeeds, University of Leeds, School of Biology, UK

UFZ, Helmholtz Centre for Environmental Research, Germany

CSIC, The Spanish National Research Council, Doñana Biological Station, Spain

UCAM, University of Cambridge, Centre for Science and Policy, UK

CNRS-IMBE, Mediterranean Institute of marine and terrestrial Biodiversity and Ecology, France

Pensoft, Pensoft Publishers Ltd, Bulgaria

SGN, Senckenberg Gesellschaft für Naturforschung, Germany

SIMBIOTICA, Simbiotica S.L., Spain

FIN, FishBase Information and Research Group, Inc., Philippines

HCMR, Hellenic Centre for Marine Research, Greece

NHM, The Natural History Museum, London

BGBM, Botanic Garden and Botanical Museum Berlin-Dahlem, Germany

UCPH, University of Copenhagen: Natural History Museum of Denmark, Denmark

RMCA, Royal Museum of Central Africa, Belgium

PLAZI, Plazi GmbH, Switzerland

GlueCAD, GlueCAD Ltd. – Engineering IT, Israel

IEEP, Institute for European Environmental Policy, UK

INPA, National Institute of Amazonian Research, Brazil

NRM, Swedish Museum of Natural History, Sweden

IBSAS, Slovak Academy of Sciences, Institute of Botany, Slovakia

EBCC-CTFC, Forest Technology Centre of Catalonia, Spain

NBIC, Norwegian Biodiversity Information Centre, Norway

FEM, Fondazione Edmund Mach, Italy

TerraData, TerraData environmetrics, Monterotondo Marittimo, Italy

EURAC, European Academy of Bozen/Bolzano, Italy

WCMC, UNEP World Conservation Monitoring Centre, UK

UGR, University of Granada, Spain

http://www.imbe.fr/

This project has received funding from the European Union’s Seventh Programme for research,

technological development and demonstration under grant agreement No 308454.

Milestone report (MS517) EU BON FP7 - 308454

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EU BON EU BON: Building the European Biodiversity Observation Network

Project no. 308454

Large scale collaborative project

MS517

Introducing & integrating EU BON common tools

Milestone number MS517

Milestone name Introducing & integrating EU BON common tools

WP no. WP5

Lead Beneficiary (full name and

Acronym)

Spanish Council for Scientific Research (CSIC)

Nature Written report

Delivery date from Annex I (proj.

month)

2015-09-30 (M34) – additional MS to DoW

Delivered yes

Actual forecast delivery date 2016-09-30

Comments


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In case the report consists of the delivery of materials (guidelines, manuscripts, etc)

Delivery name Delivery name From Partner To Partner

Name of the Authors Name of the Partner Logo of the Partner

Rodriguez Carlos, García

Antonio, Diaz-Delgado, Ricardo

CSIC

Aaike de Wever RBINS

Patricia Mergen, Larissa

Smirnova

MRAC

Israel Peer GlueCAD

Veljo Runnel UTARTU

Christos Arvanitidis, Nicolas

Bailly, Emmanouella Panteri,

Dimitra Mavraki, Nikolopoulou

Stamatina, and Eva

Chatzinikolaou

HCMR

Stefan Stoll SGN

Bonet, Francisco, J. UGR

Quentin Groom NBGB

Giorgio Brunialti ; Marco

Calderisi

TerraData

Dirk Schmeller UFZ

Tim Vincent INPA


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Table of Contents

Summary of the Milestone ...................................................................................................................... 6

Introduction ............................................................................................................................................. 6

Metadata publishing ................................................................................................................................ 7

DEIMS ................................................................................................................................................ 8

GBIF IPT ........................................................................................................................................... 11

Metacat .............................................................................................................................................. 16

Overall evaluation ............................................................................................................................. 18

Other tools for data mobilization and sharing ....................................................................................... 19

Species occurrence ............................................................................................................................ 19

Anymals + Plants ........................................................................................................................... 19

Cuadernos de campo ..................................................................................................................... 20

Movement tracking platforms ........................................................................................................... 20

Movebank ....................................................................................................................................... 20

UvA-BiTS ....................................................................................................................................... 21

Progress towards objectives .................................................................................................................. 21

Achievements and current status........................................................................................................... 22

Challenges and further/future developments ........................................................................................ 22

References ............................................................................................................................................. 23

Acknowledgements ............................................................................................................................... 23

Appendices ............................................................................................................................................ 24

Annex 1 ............................................................................................................................................. 24

Annex 2 ............................................................................................................................................. 25

Annex 3 ............................................................................................................................................. 26

Annex 4 ............................................................................................................................................. 28


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Summary of the Milestone

In the first consolidated review report (September 2014), WP5 was explicitly asked to provide a

“schedule/plan for introducing and integrating EU BON (WP2, 3, 4) common tools into the work

process of the test sites”. This milestone is, therefore, devoted to partly address this issue. In

particular, we reviewed a number of tools that are being used by EU BON test sites (see Hoffmann et

al. 2014; Smirnova et al. 2016). They mainly deal with biodiversity monitoring data integration and

are available from different institutions and organizations. Such tools are expected to help test sites to

better deal with two challenging issues:

- Dataset documentation and sharing;

- Data integration, analysis and dissemination.

This milestone is therefore divided into two sections, the first is mainly dealing with dataset

documentation (or metadata creation), and the second one is informing on useful tools belonging to

different information platforms that may help the EU BON consortium and other existing platforms to

improve data integration, and sharing.

Introduction

Test sites are expected to contribute existing data sources regarding the biodiversity and environment.

They should also test the use of EU BON concepts, tools, and techniques for data integration and

analysis, especially in real settings, at local level. The progress towards these objectives strongly

depends on common tools that allow sites to document and share such resources. Documenting data

sets is an essential part of data integration within WP5. By describing the contents and context of data

files, metadata ensure the discoverability of data sets and allow early filtering options before data

analysis. In order to establish a common understanding of the meaning or semantics of the data,

metadata standards evolved within the discipline in which they are used and hence they may differ

among disciplines. In other words, different disciplines use different metadata standards (see

http://www.tdwg.org/standards). But even when there is an agreement on a common standard, still a

considerable level of ambiguity remains if no controlled vocabulary is used to describe data. So far,

there are few initiatives (e.g. EnvThes from the LTER community, Clement et al. 2005 from the

Florida International University) attempting to create such controlled vocabularies, or thesauri, as this

requires a high degree of network integration. As a first step into this direction, WP5 initially decided

on the list of terms that must be documented when describing site data (see MS513 and Table S1 in

Annex 1), and we used this list to evaluate the different tools for documenting data sets reviewed by

WP2, and highlighted in MS231 “Specification of data sharing tools” (see also Smirnova et al. 2016).

This comprehensive document describes a set of data and metadata sharing tools, most of them

already tested by test sites and other partners from the EU BON consortium.

Currently, all test sites share information and data sets regarding biodiversity using different systems

and platforms. Rhine-Main-Observatory, Sierra Nevada and Doñana belong to the LTER network,

where biodiversity information coming from these sites is being uploaded (https://data.lter-

europe.net/deims/), using EML (Ecological Metadata Language) to document data sets. Information

coming from Amvrakikos National Park, as well as other data sets regarding marine biodiversity are

being shared by HCMR via the MedOBIS network (http://lifewww-00.her.hcmr.gr:8080/medobis/)

which follows the guidelines of GBIF and uses Darwin Core (hereafter DwC) for data, and EML for

metadata. Additional information is being shared via regional and national networks as well as own-

developed/deployed systems such as Metacat and linked to their own institutional web portals (Sierra

Nevada and INPA). Irrespectively of the system used, the new information is shared and/or updated

on a regular basis.

The first part of this document consists of a review and an evaluation of the three sharing tools

selected by the EU BON consortium (see above) in order to assess the convenience of these tools to

manage metadata. It aims to facilitate solving future challenges in data set documentation not only by

http://www.tdwg.org/standards

https://data.lter-europe.net/deims/


http://lifewww-00.her.hcmr.gr:8080/medobis/


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EU BON test sites, but also to help new sites choosing an appropriate tool for data sharing. Instances

of each tool were deployed into a private test environment in the Doñana test site.

The second part of this document focuses on different tools used by test sites as data capture or

documenting tools, not coming from the exhaustive review done by WP2 but faced during the regular

monitoring work of test sites. Rather than being evaluated in the proper sense, these tools are

mentioned here mainly because of the potential utility of some routines they have implemented in

existing data sharing tools.

Metadata publishing

Three promising tools were selected from MS231 “Specification of data sharing tools” (see also

Smirnova et al. 2016):

1. LTER-DEIMS is the Drupal Ecological Information Management System of the Long Term

Ecological Research platform (LTER). It is a collaborative platform that provides a web

interface for scientists and researchers networks, projects and initiatives with a metadata

management and data sharing system. By using online forms, it allows data sets to be

documented, internally codifying this information in EML. The platform is used by LTER

Europe, and those test sites already belonging to this network (Rhine-Main-Observatory,

Sierra Nevada, and Doñana).

2. GBIF-IPT is the Integrated Publishing Toolkit developed by the Global Biodiversity

Information Facility (GBIF). It is a free and open source web application for publishing

biodiversity data and metadata. It allows publishing and sharing biodiversity data sets and

metadata through the GBIF network. Although it formerly allowed only publication of two

types of biodiversity data: primary occurrence data and species checklists and taxonomies,

recently it was updated to serve the documentation of the ecological studies. Hellenic Centre

for Marine Research (HCMR) and the Botanical Garden Meise are currently using this tool

for both marine (MedOBIS Data Repository: http://lifewww-00.her.hcmr.gr:8080/medobis/)

and terrestrial biodiversity data (GBIF Greece Data Repository: http://lifewww-

00.her.hcmr.gr:8080/gbifgreece/; Botanical Garden Meise;

http://www.gbif.org/publisher/a344ee9f-f1b7-4761-be2c-58ee6d741395).

3. Metacat is a flexible, open source metadata catalog and data repository that targets scientific

data, particularly from ecology and environmental science. Metacat uses EML for

representing the large number of metadata content standards that are relevant to ecology and

other sciences. Metacat could be considered as a generic EML database that allows storage,

query, and retrieval of arbitrary EML documents without prior knowledge of the EML

schema. This platform is being used by INPA (National Institute of Amazonian Research) and

Sierra Nevada.

For each of these tools we checked whether they are able to properly map those fields or terms

required by the test sites to document their data sets (see Table S1 in Annex 1). The criteria to

evaluate the tools were defined throughout the different versions of this document, and finally

established during the WP2/WP5 meeting held in Seville in April 2016 (see Table 1). They included

both descriptive information that is included in the text (see below), and evaluation points that were

arranged in a questionnaire (see Annex 2) that was sent to partners to gather their evaluations.

http://lifewww-00.her.hcmr.gr:8080/medobis/

http://lifewww-00.her.hcmr.gr:8080/gbifgreece/

http://lifewww-00.her.hcmr.gr:8080/gbifgreece/

http://www.gbif.org/publisher/a344ee9f-f1b7-4761-be2c-58ee6d741395


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DEIMS

Purpose: serves as repository for research sites and datasets belonging to the LTER Europe

(http://www.lter-europe.net/lter-europe).

Registration: required.

Data sharing capabilities: data and metadata.

Description: This Drupal-based application, licensed as free software, allows local use and further

customization. Installation of a regular instance of this software locally, or its customization is time

consuming and requires high technical expertise on Drupal 7 content management system, since

DEIMS is released as a set of Drupal modules, requiring to install and customize an entire Drupal site

from scratch. This report focuses on the customized version available through the LTER Europe

platform (https://data.lter-europe.net/deims/). It uses a regular online form separated in different

sections where information is entered in empty boxes (Fig. 1). This makes the form appear longer, but

all the information is appearing on the same page.

Figure 1. Data entry form for DEIMS. Lateral menu gives access to different sections where

information can be entered.

General evaluation: The platform of LTER Europe is comprehensive, versatile, and user-friendly

enough to cover the highly heterogeneous data coming from biodiversity monitoring. It offers the

possibility of properly document not only the data set but also the associated monitoring protocols in a

very precise way, addressing all important information previously requested by partners (see Table

S1 in Annex 1). It is being predominantly used as a metadata base, but many forms of actual datasets

(lists, tables, spatial information) can be uploaded directly or linked to the metadata sets. Furthermore,

the LTER Europe platform has implemented an environmental thesaurus (EnvThes;

http://vocabs.ceh.ac.uk/evn/tbl/envthes.evn#http%3A%2F%2Fvocabs.lter-

europe.net%2FEnvThes%2Fmsa0524) that restricts options for entries and suggests keywords (see

Fig. 2) to describe datasets. It is an advanced version based on the US LTER controlled vocabulary,

which is also going to be adopted by the US LTER. The evaluation shown in this document was done

by one technician with regular experience in documenting data set and a scientist that sporadically

perform data set documentation.

http://www.lter-europe.net/lter-europe


http://vocabs.ceh.ac.uk/evn/tbl/envthes.evn#http%3A%2F%2Fvocabs.lter-europe.net%2FEnvThes%2Fmsa0524

http://vocabs.ceh.ac.uk/evn/tbl/envthes.evn#http%3A%2F%2Fvocabs.lter-europe.net%2FEnvThes%2Fmsa0524


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Figure 2. Keywords section showing how the auto-complete utility suggests terms from the EnvThes

when the user starts typing.

Taxonomic coverage is entered in a dedicated tab where the system allows the user to select from

kingdom to species, enter the scientific name and the common name. This section currently has no

taxonomic backbone, so there is neither standardization nor quality control on the taxonomic coverage

(Fig. 3). The system allows for auto-complete, but permits entering only one taxon at a time, which is

time-consuming when taxonomic coverage is high. In addition, this feature is apparently based on the

information entered in a previous version of the platform that allowed entering a list of species in a

single step. For that reason, the auto-complete tool may show this list which disturbs the process a bit

(see Fig. 3). Apparently, the system is being prepared to offer a proper taxonomic backbone with

regularly updated species lists, such as for example WoRMs (World Register of Marine Species) for

marine taxa or CoL (Catalogue of Life) for terrestrial taxa. Whenever this utility is implemented in the

tool, this would constitute a significant improvement.

Figure 3. Section where taxonomic coverage is entered. The figure shows the list that popped-up

when “Falco” was typed.


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In DEIMS, there is no dedicated box indicating whether the documented data set belongs to any

series. Similarly, the section for sampling description is restricted to three pre-defined fields: spatial

scale, sampling time span, and minimum sampling unit. The user is therefore forced to use the

“method and instrumentation” section to include any additional information on the sampling (see Fig.

5). Despite, there is no dedicated box for references documenting the monitoring protocol, this can be

included as a URL in the method section (Fig. 4).

Figure 4. Method section.

Figure 5. Sampling section.


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GBIF IPT

Purpose: to publish and share biodiversity datasets through the GBIF network, including 813 data

publishers, and 29353 data sets.


Data sharing capabilities: full harmonized data and metadata.

Description: An instance of the GBIF IPT (2.3.1) has been hosted by GBIF in order to support the

research work of the EU BON project. This (http://eubon-ipt.gbif.org/) has been specially configured

to publish sample-based data sets. The default IPT instance can be used to publish three types of

biodiversity data sets: species occurrence, species checklist, and metadata-only. More recently, IPT-

GBIF is capable of publishing occurrence data stemming from “sample-based” ecological studies,

which allow addressing one of the main biodiversity information sources: biodiversity monitoring.

The IPT is a web-based tool, accessible as a GBIF cloud service. A local installation of an IPT

instance can be done; nevertheless it requires some advanced technical skills. In either way, new users

require an account to be setup by the IPT administrator in order to login and start publishing data sets.

The tool allows to create a metadata-only data set by entering a short name (used internally as an

identifier for the data set) and by filling in at least the mandatory metadata elements (Basic Metadata

Section: title, description, contacts) in the metadata editor (see Fig. 6).

http://eubon-ipt.gbif.org/


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Figure 6. Basic metadata section.


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The metadata editor allows for a very comprehensive documentation of the data set, including all

relevant fields previously identified by test sites. Documenting the taxonomic coverage is time

consuming, but not to the extent it does for DEIMS. This is mainly because GBIF IPT allows for a list

of taxa to be uploaded (Fig. 7). Afterwards it requires editing them one by one to specify whether taxa

refer to species, subspecies, etc. However, scientific names that are difficult to type could be entered

by copying them from an existing list. It has a taxonomic backbone largely based on the Catalogue of

Life. The new backbone is being currently tested: http://www.gbif-uat.org/dataset/d7dddbf4-2cf0-

4f39-9b2a-bb099caae36c. After all the metadata have been entered, the user can decide to publish the

data set. The output format of publishing a metadata-only dataset is an XML file written in the GBIF

Metadata Profile based on EML.

Figure 7. Taxonomic coverage section. The empty box below “Taxon list” allows a list to be pasted.

By clicking the “add” button, the list and transferred to the required number of “scientific name”

boxes.

The input format to publish an occurrence, event or checklist data set is a DwC Archive (DwC-A),

zipped file containing: 1) the XML metadata file, 2) the data mapped to DwC, and 3) an XML

metafile describing the data content and relationships between files. To map one’s own data set to

DwC, the user must map its source fields to DwC terms. This was the less intuitive and more time-

consuming part of our testing, but ensures that data are more easily comparable and suitable for direct

use. In this part, prior exposure to the DwC standard would minimize these limitations. Nonetheless

The IPT facilitates this mapping with its built-in automatic mapping feature and mapping tool. A

comprehensive user manual and help text within the interface exists to guide first-time users. There

http://www.gbif-uat.org/dataset/d7dddbf4-2cf0-4f39-9b2a-bb099caae36c

http://www.gbif-uat.org/dataset/d7dddbf4-2cf0-4f39-9b2a-bb099caae36c


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are also user guide for GBIF spreadsheet templates available and an Excel template for sampling

event data: https://github.com/gbif/ipt/wiki/samplingEventData

General evaluation: In general terms, the platform of GBIF is comprehensive and versatile, covering

the high heterogeneity of data coming from biodiversity monitoring. It offers the possibility of

properly document not only the data set, but also the associated monitoring protocols in a very precise

way addressing all important information previously requested by partners (Fig. 8). The evaluation

shown in this document was mainly done by scientists with previous experience in documenting data

set, although only half of them do it on a regular basis.

https://www.google.es/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&uact=8&ved=0ahUKEwiVx4bHlfbPAhUEnRoKHax6BQQQFgguMAE&url=http%3A%2F%2Fwww.gbif.org%2Fsystem%2Ffiles_force%2Fgbif_resource%2Fresource-80643%2Fdwca-spreadsheet-processor-guide.pdf%3Fdownload%3D1&usg=AFQjCNGMUy4Bwdnh5Ejjtr94kdiB9FEqvQ

https://github.com/gbif/ipt/wiki/samplingEventData


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Figure 8. Methods section showing the information provided in each of the boxes.


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Metacat

Purpose: serves as metadata catalog and data repository of scientific data.


Data sharing capabilities: data and metadata.

Description: This tool is widely used in biodiversity information networks, but information is not

centralized (or not necessarily) in a single host/institution. It allows different Metacat servers to share

data (both xml and data files) between each other. We initially installed an instance of Metacat in

servers of the Doñana Biological Station (CSIC) but we experienced difficulties while installing some

components based on perl scripts through a CGI bridge. Therefore, we tested the instance of Metacat

installed in the platform of KNB (https://knb.ecoinformatics.org/). KNB is powered by the Metacat

data management system and it is optimized for handling data sets described using EML.

General evaluation: The tool is comprehensive, versatile and friendly enough to cover the high

heterogeneity of data coming from biodiversity monitoring. It offers the possibility to properly

document not only the data set, but also the associated monitoring protocols in a very precise way

addressing all important information previously requested by partners (see Table S1 in Annex 1).

Despite the tool suggests using the , such vocabulary is not implemented in the tool and therefore the

user needs to download it beforehand. The evaluation shown in this document was done by scientists

with previous experience in documenting data sets, but one third declared to do it sporadically.

Geographic coverage is entered either as a point or bounding box by typing the coordinates using

dedicated boxes for degrees, minutes and seconds, respectively. Despite a map is shown when the

process is completed (this way allowing for a quick checking), no graphical tool was available during

the edition process. Nonetheless, the tool checks whether the information entered in each of the boxes

matches the required format data range (Fig. 9).

Figure 9. Geographic section within the KNB tool.

https://knb.ecoinformatics.org/


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Taxonomic coverage is entered in a dedicated section where the system allows the user to type the

taxonomic rank, and the related taxon. Neither auto-complete and/or predefined options are available.

Taxa are entered one by one until completing the taxonomic coverage. This section has no taxonomic

backbone, so there is neither standardization nor quality control on the taxonomic coverage (Fig. 10).

Figure 10. Taxonomic coverage section.

The tool allows for documenting sampling methods, and includes a dedicated box where the sampling

design of the study could be described (Fig. 11). It is also possible to document whether the data set

belong to any series or whether changes in the sampling frequency could be found. On the other hand,

there is no dedicated box for references documenting the monitoring protocol used.

Figure 11. Sampling description section.


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Overall evaluation

Although all tools are being improved and new functionalities may arrive in the short-medium term,

this evaluation refers to the state of the art of the tools in 2016. In this regard, we found that all tools

allow a very comprehensive documentation of data sets, including monitoring protocols, taxonomic

coverage and many other details. As documented before, there were some subtle differences between

tools in their ability to document particular details of data sets, but we only found taxonomy as an

important difference, with IPT-GBIF being the one that best deals with the topic. In addition to the

taxonomic backbone only available through this platform, IPT-GBIF has the more straightforward

method of entering taxonomy, which may play an important role in documenting speed when

taxonomic coverage is high. According to the opinion of users, none of the tools are especially user-

friendly or easy to use at the first time, so all of them may also benefit from using tutorials or any

other training (see Table 1). Because the purpose and data sharing capabilities of each of the tools is

not the same, they could not be directly compared. On the other hand, the information provided in this

evaluation may help sites to choose the tool that best suit to their goals. In this sense, it is important to

remark that IPT-GBIF not only deals with metadata, but also with harmonized data that make data

more easily comparable and suitable for direct use.

Table 1. Criteria used to evaluate the tools (see questionnaire (link) in the Annex 2)

Criteria Tools

LTER-DEIMS IPT-GBIF METACAT

User-friendliness (access and general use of

the tool)

Difficult Intermediate Difficult

Additional skills required for a data manager Notions on

metadata

Previous

exposure to

DwC

Notions on

metadata

Training required 50% in favour

of training

50% in favour

of training

67% in favour

of training

Documenting capabilities Very high (4.5

out of 5)

Very high (4.7

out of 5)

Very high (4.3

out of 5)

Taxonomic backbone No Yes No

Associated thesaurus Yes No No

Complexity (Simple/Complex) Intermediate (3

out of 5)

Intermediate

(2.7 out of 5)

Intermediate (3

out of 5)

Documenting speed 20 min (range

10-30)

25 min (range:

10-40)

18 min (range:

<10-30)

Helpdesk available? No Yes No

Number of evaluators (Institutions) 2 (1) 6 (5) 3 (3)


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Other tools for data mobilization and sharing

Technology is advancing fast and its capabilities are being incorporated to different working routines

in the form of online workflows and mobile applications with a wide range of options, from online

forms that register basic information, to cloud computing that speed-up complex statistical analysis.

Such plethora of tools prevented us to conduct a comprehensive evaluation of their utility in the

context of the EU BON project. On the other hand, they are so widespread in biodiversity information

facilities and they are so useful that they deserve to be mentioned in this document. Nonetheless, it

should be noted that there were no particular reason or selection criteria behind the set of tools

described below, but just the hazard of having been tested by test sites. Among them two data capture

tools and two platforms for data sharing are described mainly focusing on the features that are worth it

to be adopted and the ones that resulted limiting and are suggested to be avoided in future

developments

Species occurrence

One of the most frequent app types is devoted to documenting information on species occurrence (e.g.

Anymals+plants, iNaturaList, Cuadernos de campo, eBird, Map of Life). We checked two of these

applications (Anymals+Plants and Cuadernos de campo, see Annex 3), and below we have listed the

main pros and cons we found when using them.

Anymals + Plants app

Pros

Connected to the GBIF network where data can be shared.

It allows using the taxonomic backbone of the platform.

Access to the Wikipedia web page on the species you are prone to record, including images.

Possibility of using it offline.

It is also possible to contribute to this initiative by using the web page as data sharing tool. The

page allows for geo-referenced pictures to be uploaded on the web page and the metadata of the

picture are used to fill the information regarding date, time and location. The user is only asked for

the species name (to be selected from the GBIF taxonomic backbone), number of individuals and

degree of certainty.

Cons

It takes very long to refresh the GPS position. This limits its use by moving observers, forcing

them to wait until the position is acquired.

When entering the new sighting, a species browsing is required and depending on the connection,

or the pre-selected list of species, this may take long.

Overall, the application is well designed for their sporadic use when visiting the field during the spare

time, offering the best when connected to the internet, and motionless. On the other hand, it is less

suited to professional biodiversity monitoring, frequently done while moving across transects or

sampling points with no access to the internet.

The possibility of contributing to the initiative by means of a web page where observations could be

uploaded is very interesting and some of the solutions offered by the web page are very useful. This

includes the automatic recognition of date, time and position when uploading a picture of the

specimen to be recorded. On the other hand, several utilities could be added to the web page to

improve the user experience. This would include a bulk uploading tool that allow the user uploading

several pictures in a single step, implement basic grouping tools that allow summarizing sightings

(create species list, calculate individuals per species, introduce area or time filters), and offer the

possibility of downloading observations in a file (csv or txt).

http://www.anymals.org/


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Cuadernos de campo

Pros

Perfectly suited to gather data on bird presence or abundance to be further collated in a bird atlas, it

includes the breeding classification of the European Ornithological Atlas Committee.

The application allows working offline and the information is stored locally.

The species are selected from a predefined list.

Once the user has access to the internet, it is possible to share captured information by either

sending the csv file to a specific e-mail address or uploading the information to a web page where

the information could be verified, stored and published.

Cons

It is predefined to Spain, so it is not possible to include observations from any other country.

Indecisive information to be verified afterwards is not allowed

It is not flexible enough to be easily adapted to any approach different from the one it is designed

for (Atlas). For instance, the user must enter information on the technician in charge, the locality,

region, province, etc. as master information to be applied to the set of sightings to be entered. The

user cannot skip this initial step.

Movement tracking platforms

Biodiversity monitoring is benefiting from technological development in many ways. Tracking animal

movement, for instance, is not only benefiting from new tracking devices, but also from

miniaturization (making devices suitable for smaller animals), advances in communication protocols,

etc. Several platforms have been built to facilitate data sharing among users of these technologies (see

Annex 4), some of them offering technological solutions that could solve some of the limits we found

when testing applications for species occurrence (see above). The examples of well solved challenges

are listed below.

Movebank

It makes an automatic mapping of part of the uploaded information. The tool automatically

understands (conduct a pre-mapping) of some of the fields (lat, long, date…) of the user’s database,

and asks the user to verify this pre-mapped fields and map the rest. In addition, the mapping profile

can be saved, allowing the user to apply it to forthcoming files with the same structure, thus

simplifying the mapping step that is normally time-consuming. Since data mapping is one of the main

problems we found when using the metadata editor of the IPT-GBIF, we strongly encourage GBIF to

apply a similar solution.

It offers an appropriate data model for the kind of data that is being uploaded. The user could

eventually forget about how to store the information gathered, thereby avoiding the arduous task of

designing and implementing a data model on a database management system (DBMS).

It offers a data summary.

It offers a duplicate finder tool, which is very useful when uploading information from several

devices looking very similar to each other.

It offers defining different roles for the access and use of the uploaded information. This improves

the collaborative working, and facilitates data sharing for scientists (increasingly demanded by

scientific journals).

It offers environmental information that may be linked to animal movement data. This consists in

global environmental data-sets, like weather models, primary productivity and satellite imagery,

among others.


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It offers a task scheduler that could be programmed to get information from the tracking data

provider. Although this is quite specific of the animal tracking community, task programming can

be very useful (e.g. send me an e-mail with my records when they get 1000) and it is something

that could be easily provided to the user.

UvA-BiTS

Ability of working offline.

It offers an appropriate data model for the kind of data that is being uploaded. This happens as

soon as an internet connection is established.

It offers a safe storage environment that due to internal backup processes, makes virtually

impossible to erase or modify information. A copy of raw data could be always retrieved.

It offers access to a PostgreSQL database using specific tools such as phpPgAdmin (web based) or

pgAdmin III (desktop application).

It offers visualizing utilities for the data such as 3D simulations of the movements, allows building

KMZ files (a zipped version of KML, which is the standard used by Google Earth mapping

application) with the movement of your tracked individuals.

It offers standards of tested movement patterns (flapping, resting, soaring) to compare with,

allowing this info (type of movement) to be added to the KMZ files.

It offers software to synchronize the information from the accelerometer to a video file, thus

linking the observed movement pattern with the information given by the accelerometer.

It allows getting information of environmental variables that may help the user to link animal

movement data with information from global environmental datasets.

Progress towards objectives

The work process of test sites within the EU BON consortium includes both providing well-

documented information on biodiversity and testing EU BON tools, services and concepts. This

document mainly reviews tools that help achieving the first goal, but several of the ideas and

suggestions derived from the above mentioned tools may improve the required networking within the

EU BON project.

All the metadata publishing tools evaluated in this document allow a very comprehensive

documentation of data sets, considering key aspect such as using referenced taxonomies or controlled

vocabularies. On the other hand, they require some previous training or assistance, and this may have

prevented some biodiversity monitoring teams to have contributed more strongly to metadata sharing.

Some of the other tools described in this document demonstrated that there are affordable solutions to

circumvent part of the challenging issues (e.g. data mapping) found in this evaluation. Implementing

such solutions may help monitoring teams to increase their contribution to the different platforms

where information on biodiversity is being shared.

Regarding data capture tools, the numerous limitations we found when trying to apply them to

monitoring field work highlight the importance of considering expectations and usability by the user

when building tools for sharing biodiversity information. Both biodiversity information facilities and

apps aiming to gather biodiversity information should think on what to offer to the user to get her/him

involved in the long term. In other words, the advantages found when using the tool offered to the

user should be so good and numerous that users would select it when encountering something to be

reported. After testing these two apps while doing field work, and some others in a less exhaustive

way, we built a set of criteria that may be considered as further improvements to be implemented into

data capture tools.

1. Using common vocabularies and taxonomies helps keeping international standards in

essential portions of the information captured. Because of the difficulty of typing scientific

names, the use of autocomplete options is advised.


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2. Because internet connection is not available everywhere and many biodiversity observations

are done in remote locations, the ability of working offline must be considered.

3. Either because of the aim of incorporating apps to working routines, or because the hazardous

nature of sightings, data recording should be quick. We found several app’s features that help

achieving this goal:

a. The possibility of using icons or images can speed up data recording.

b. Keep as simple as possible the data acquisition process. Some information such as

user, date, time and position are automatically taken by devices, and this could be

internally incorporated to data.

4. Allow the user to keep track of his/her activity (list observations) and ideally group them to

get a quick overview of the activity (individuals per species, or space and time filters).

5. Allow entering indecisive information. This could be due to movement (“get the position

where I made a sighting despite I have no time to complete all information right now”), or

lack of proper identification guide when performing the record (“let me check the species at

home”). These are frequent situations during monitoring that need to be considered.

6. Publishing information in international networks helps data to be discoverable, and be useful

for a wider range of users.

7. Last but not least, it should be kept in mind that very specific aims normally restrict the

ability of the app of being used for multiple purposes (see BMS Spain in Annex 3 as an

example of the first type). Very likely the app designed to capture data on a specific group or

area would not work as well when applied to a different subject or place.

Achievements and current status

All sites in EU BON and nearly all other long-term biodiversity monitoring sites in Europe from

which a European biodiversity observation network (European BON) could be recruited are currently

sharing information using different methods (see introduction). Many of them are members in LTER

Europe, the Critical Zone Observatory Network (CZO), Ecoscope, Environmental Change Network

(ECN), Arctic BON, Global Lake Ecology Observation Network (GLEON), and many other topical

networks. All these sites use the tools that their networks provide or endorse, and there is no need to

develop and promote additional alternative tools for metadata and data storage. In contrast, the

approach of EU BON is to build a meta-network based on sites that can be part of other networks at

the same time. This co-location of monitoring efforts is desired as it helps to create synergies in data

acquisition and handling. Accordingly, EU BON aims to bring together biodiversity information from

all these scattered communities in a single platform where it can be discovered. EU BON’s work

package 5 is co-operating with work package 2 in order to connect the information sources, either data

or metadata to the software infrastructure of EU BON, and ensure that data sets are discoverable. For

this reason, standardized endpoints are currently being requested from test sites. These endpoints are

services, public URLs that can be consumed by the EU BON brokering system, unifying the search

functionality in a single point. Those endpoints are typically defined by data sharing tools (see

MS231, Smirnova et al. 2016), based on standards for describing either the information or the sharing

protocol (see D2.1, section 2). For sites belonging to LTER-EU that uses DEIMS (http://data.lter-

europe.net/deims/) as metadata catalogue and data sharing tool, sites are providing a list of EML files

to be harvested. Sites using a Metacat instance should provide EML harvest lists or configure the

optional Metacat OAI-PMH data provider.

Challenges and further/future developments

Despite the integration of metadata from test sites will enhance the discoverability of this information;

a single repository may become inefficient or even inoperative in the near future. Ensuring

http://data.lter-europe.net/deims/

http://data.lter-europe.net/deims/

https://knb.ecoinformatics.org/knb/docs/oaipmh.html


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interoperability between the EU BON portal and data providers would improve the persistence of the

system.

We started to document all the data and metadata bases that EU BON test sites currently use. We

documented their topical focus, coverage, standards and user friendliness. As additional data sources

get connected to the EU BON portal, all these data sources will get evaluated as well in order to give a

comparative oversight of which data repository system may be most efficient for different users.

Regarding data integration, analysis and dissemination, this is currently being done by peer-to-peer

communication with task leaders, but proper workflows should be established in order to make the

process more transparent to all partners.

References

Clement, G. P., Everglades Online Thesaurus: A Standard Vocabulary for the South Florida

Ecosystem. 2005.Works of the FIU Libraries. Paper 1.

GBIF 2011. GBIF Metadata Profile, Reference Guide, Feb 2011, (contributed by O Tuama, E., Braak,

K., Copenhagen: Global Biodiversity Information Facility, 19 pp. Accessible

at http://links.gbif.org/gbif_metadata_profile_how-to_en_v1

Hoffmann, A., Penner, J., Vohland, K., Cramer, W., Doubleday, R., Henle, K., Kõljalg, U., Kühn, I.,

Kunin, W.E., Negro, J.J., Penev, L., Rodríguez, C., Saarenmaa, H., Schmeller, D.S., Stoev, P.,

Sutherland, W.J., O Tuama, E., Wetzel, F., Häuser, C.L. 2014. Improved access to integrated

biodiversity data for science, practice, and policy - the European Biodiversity Observation

Network (EU BON). Nature Conservation 6: 49-65. doi:

http://natureconservation.pensoft.net/articles.php?id=1349

Smirnova, L. et al. 2016. Data sharing tools adopted by the European Biodiversity Observation

Network project. Research Ideas and Outcomes 2:e9390, doi: 10.3897/rio.2.e9390

Wynhoff, I., Janss, G., Paz, D., Román, J., Stefanescu, C., van Swaay, C., Munguira, M.L. (2013)

Improving Doñana’s contribution to Butterfly Conservation Europe (BCE Doñana). Report

VS2013.008, Butterfly Conservation Europe & De Vlinderstichting/Dutch Butterfly

Conservation, Wageningen.

Acknowledgements

Kyle Braak helped in editing the GBIF-IPT section; Jesús Hernandez-Pliego and Rafa Silva helped in

editing the Movebank and uva-bits sections; Jacinto Roman and Daniel Fuentes helped in editing the

BMS section.

http://natureconservation.pensoft.net/articles.php?id=1349


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Appendices

Annex 1

Table S1. List of fields required for describing data sets as agreed by EU BON test sites during WP5

kick-off meeting (see MS513).

FIELD CONTENT EML MOST RELEVANT TAGS

Source Information about the provider (ministry,

National Park, Research institute) in the

regular form (i.e. electronic and physical

address, fax and telephone number)

creator

Site A hierarchical classification (e. g.

Continent/Country/Name of the park, etc)

of the site where the information was

gathered + map where the polygon of the

study site could be delineated

description, geographicCoverage

Person of contact +

deputy

Information about user (up loader/creator)

is normally provided in the form of e-mail

address, but including a deputy person

could be useful

contact, creator, associatedParty

Name The name used by the provider to identify

the dataset

title

Description/

Abstract/ Keywords

The description given by the provider. Note

that machines do not understand texts, but

look for keywords

title, description, funding,

studyAreaDescription,

designDescription, abstract,

keywordSet, keyword

Human/ Sensor/

Model/GIS

To distinguish between these ways of

gathering data

Custom metadata tag/keywordSet

methods/DwC import

Geometry Whether information comes from point-

based or polygon-based monitoring

geographicCoverage, spatialVector,

spatialRaster

Beginning of data First date of data collection in the data set eml-coverage module -> beginDate

End of data Last date of data collection in the data set eml-coverage module -> endDate

Number of data Rows in the database numberOfRecords

Frequency Whether data are collected annually,

monthly, etc.

dataset->MaintUpFreqType

Target (individual/

population/site)

The study object: ecosystem, population,

individual…

description

Precision Whether the information is gathered in a

particular spatial context (e.g. UTM 1x1

km for censuses). Leave empty if not

precision, attributeDefinition, eml-

spatialReference module

Resolution Whether the information was originally

taken at one point, at several ones, the one

of the satellite imagery, etc..

precision, attributeDefinition, eml-

spatialReference module,

geographicCoverage

Protocol (Y/N) Whether the data come from well-described

monitoring protocols in each of the sites. A

methods, protocols


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subordinated question to answer “Y” may

be whether the study matches exactly a

referenced protocol. In this case the

reference of this study should be provided

(the doi if available)

Protocol (description) A brief description of the protocol is

required + the upload of a document in

English where the protocol is properly

described if answer to protocol was “N”

methods, protocols

Type of Variable Whether observations are counts,

presence/absence, measurements

dateTime, measurementScale,

numericDomain, nominal, ordinal,

interval, ratio

Fields Name of the fields included in the data set

Fields attributes.

Description

Brief description in English.

Field attributes.

Format

DD/MM/YYYY for date, etc.

Field attributes. Units If weight is measured, units will be grams attribute->unit->standardUnit

Original format Despite information will be stored in a

sharable format (txt, csv…), it may come

from different formats (xls, dbf, odt…)

physical

Availability Whether data could be shared resource->distribution

Accessibility Whether data are accessible: online, upon

request, use constraints, etc

resource->online/offline/inline

Intellectual rights Self explanatory eml access module, resource-

>intellectualRights

Metadata date Self explanatory resource->pubData

Dataset language Self explanatory xml:lang?, entity->additionalInfo?

Data set File to be browsed in user’s computer for

uploading

entity, dataTable, physical

Annex 2

Link to the questionnaire the participants had to answer:

https://docs.google.com/forms/d/e/1FAIpQLSe6L920HByxx2KAZv8WUokuYWfqDPXFH3dJfEjHty

qIXJLCVQ/viewform?c=0&w=1

https://docs.google.com/forms/d/e/1FAIpQLSe6L920HByxx2KAZv8WUokuYWfqDPXFH3dJfEjHtyqIXJLCVQ/viewform?c=0&w=1

https://docs.google.com/forms/d/e/1FAIpQLSe6L920HByxx2KAZv8WUokuYWfqDPXFH3dJfEjHtyqIXJLCVQ/viewform?c=0&w=1


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Annex 3

Anymals + Plants

Appearing in the online play store (Current Version 0.1_19) store as “find & log animals and plants.

Code for nature” it was developed by GBIF and the Museum fur Naturkunde of Berlin and it is

connected to the GBIF network. The great advantage and the great disadvantage of this app come

from the same side: it requires to be connected to the internet. This allows the app offering

information on the species you are prone to record and to upload and share this information

immediately. This limitation has been recently solved, offering an offline version, which is useful

when doing field work or visiting natural areas without reception.

The app was installed in 4 different devices that were used during both professional monitoring and

recreational activities averaging around 100 sightings per device.

In particular, we found the following points that depreciate its utility.

1. It takes very long to refresh the GPS position. This is surprising because taking a geo-referenced

picture with the same device is quick. It may happen that the app is trying to get a very accurate

location, but this limits its use by moving observers, forcing them to wait until the desired accuracy is

achieved.

2. The “new sighting” button is normally hidden when entering the screen, so it is required to check a

previous sighting, and then go back to see the “new sighting” button on top.

3. When entering the new sighting, a species browsing is required and depending on the connection,

this takes long. An alternative system based on a local data base would be able of autocompleting the

information much quicker and offline. This alternative would be slower until getting a sufficient

number of species that speed-up the recording process, but after that it would be much quicker.

In summary, when reporting species occurrence, the app is slow and the user may find easier and

quicker to use a different tool (e. g camera or voice recorder). In this sense, the performance of the

associated web page (www.anymals.org) was better than the app. The page allows for geo-referenced

pictures to be uploaded on the web page where the metadata of the picture are used to fill the

information regarding date, time and location. The user is only asked for the species name, number

and degree of certainty. As being a web page, the bandwidth often allows for a quick search of the

focus species.

The main lesson we learnt from this application is that working offline must be considered when

building applications for recording species occurrence. Unless the observer is stationary and well

connected to the internet, working online presents several limitations. In this particular case, where a

web page is available for uploading and editing the information provided by the app, it would be

easier to keep the mobile app working offline, and use the capabilities of the web page to check the

sightings coming from the app. For instance, the web tool could check whether the scientific name is

right or whether the marked position makes sense to the observer (a map is already shown when

uploading sightings). Despite the better performance of the web page (when compared with the

mobile app), some utilities are here suggested as further improvements of the tool: 1) list the sighted

species (check lists are common among bird and butterfly watchers). 2) Add grouping tools that allow

the user to summarize sightings in the form of basic statistics (individuals per species, area or time

filters). 3) Add a downloading tool to get your observation in a file (csv or txt).

Cuadernos de campo

Developed by a small Spanish company (Jayvser), it is mainly devoted to gather information from

Spanish ornithologists (it includes the breeding classification of the European Ornithological Atlas

Committee), although it may be used to record other taxa (butterflies, moths, dragonflies). This local

scope is the main advantage and disadvantage of the app since it is very useful if your recording

system matches the predefined one (for instance if you want to cover one cell of the Atlas), but it is

not flexible enough to be easily adapted to other kind of information recording such as occasional

http://www.javyser.net/


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sightings. The user must enter information on the technician in charge, the locality, region, province,

etc. as master information to be applied to the set of sightings to be entered. The user cannot skip this

initial step. In addition, the local database only includes Spain, so it is not possible to enter

information from any other country. The application allows working offline and the information is

stored locally. The species are selected from a list, and it is not possible to enter indecisive

information to be verified afterwards. Once you have reception, you can share your information by

either sending the csv file to a specific e-mail address or uploading the information to a web page

where the information could be verified, stored and published.

The app was tested by at least two people from CSIC, with few sporadic entries.

There are two important lessons to learn from this app: the first is to evaluate to what extent local

applications to record species occurrences in a country or region could be scalable to be used in a

more global context by using common reference tables (thesauri). The second is to keep the field work

procedure as simple and flexible as possible. Regarding this app, it is suggested to implement auto-

complete routines for entering taxonomic information, and use the web page to check sightings on the

basis of GPS position, user’s profile, and standardized references such as scientific names, and enter

additional information that could be easily retrieved after the field work (in this case master

information regarding observations).

BMS - Butterfly Monitoring Schemes

Butterfly Conservation Europe is encouraging the collaboration of all European countries in

developing country-based Butterfly Monitoring Schemes. As part of the Spanish contribution to this

goal, it was decided to build a web-based data platform for the recorders because it would

significantly facilitate the duties of the country-coordinator. This was done under the umbrella of the

EU BON project and, in a similar way than UFZ-Leipzig is doing with the BMS in Germany, Doñana

Biological Station (CSIC) is working as ICT core of the process of data gathering in Spain. Next to

easy online data input the website should provide for communication with the volunteers during the

counting season. The aim is getting data for early analysis as soon as possible to detect and

communicate trends in the development of the flight period of certain species, deviations from earlier

years in numbers or flight periods and invasions of migrating butterflies (Wynhoff et al. 2013). Such

online tool was developed during 2014, and it was already gathering information during 2015, and

2016 involving 71 volunteers that visit 73 transects scoring 1232 censuses, and 52400 records of 128

species.

The following lines belong to the evaluation of the web-based platform of BMS Spain. In this tool,

uploading information is divided into two steps: 1) sign up and declaration of transects, and 2) species

sighting (Fig. S1).

Figure S1. Online form of the BMS-Spain


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When registering in the platform, the user contacts the area coordinator (there is a central coordinator

and several area’s coordinators) and defines the transect that will be monitored. At this step, the

position (shp, kml file), and habitat characterization of the transect is uploaded. For that, both the

EUNIS classification and the SACRE classification are used. The first one is the reference system of

the European Environmental Agency and the European Environmental Information and Observation

Network, with more than 5000 habitat types organized in a hierarchical structure of 8 nested levels. In

the platform, the user is asked to give information on at least the 3 first EUNIS levels, although

additional levels (up to the 5) are allowed. The user is provided with the key of this classification

system (http://eunis.eea.europa.eu/habitats-key.jsp). The second classification system is based on the

SACRE program, which is the Spanish contribution to the CBMS (Common Birds Monitoring

Scheme). We consider this system because it adds structural information to the habitat classification

made by EUNIS and it may be useful when analyzing results. Up to our knowledge, birds and

butterflies are among the best monitored animal groups in Europe, having their own European

monitoring programs. Using similar habitat classifications may help analyzing and understanding

population trends of both groups. Once the user is registered and the transect is declared and divided

into sectors (according to BMS method), the user is given a password to start entering information.

The entering form is based on the paper form used by volunteers, so it is straightforward to digitize

the sightings. In other words, the application was fully adapted to the current working routine of

volunteers. Autocomplete is available while typing so the user does not need to type the full species

name but just select it from the suggestions made by the system when start writing. This saves time

and diminishes the number of typing errors. Once the information is entered, the user could download

both full data in MS Excel xls format and the queries resulting from the different filtering options

available. The xls file incorporates all master information (habitat classification, weather

conditions…) along with sightings. Apart from allowing the user to easily digitize the information, the

summarizing and downloading options constitute useful tools that users appreciated when the tool was

introduced to them.

What we learnt while building this application was that developers must fit their work to users’

demands rather than building applications based on their current standards and forcing users to get

used to them. In this case, for instance, users were very reluctant to change the way they perform

censuses (using voice recorders and paper forms), and this happens very often. Developers only

focused on the digitizing process, providing the users with useful tools that help them completing

their activities without needing to ask for any new step or requirement.

Annex 4

Movebank

It is a free open web platform on animal tracking hosted by the Max Planck Institute for Ornithology

(Seewiesen, Germany). The PostgreSQL/PostGIS spatial database running behind offers the user the

possibility of storing, sharing and publishing information on animal tracking, browsing information

from other researchers, and manage data gathering from satellite tracking. Despite being a quite

specialized initiative, it solves some of the challenges found in the evaluation of applications devoted

to track species occurrence.

Uploading the information to the platform is very intuitive. The tool automatically understands (map)

some of the fields (lat, long, date…) of the user’s database, and asks the user to verify this pre-

mapped fields and map the rest. In addition, the mapping profile can be saved, allowing the user to

apply it to forthcoming files with the same structure, thus simplifying the mapping step that is

normally time-consuming. Since data mapping is one of the main problems we found when using the

metadata editor of the IPT-GBIF, we strongly encourage GBIF to apply a similar solution.

In a second step, data are transferred to the repository, which already has an appropriate data model.

This is not straightforward because the relationship between individuals and tracking devices is not

one-to-one, and the time line is essential to set such link. This is the first important feedback the tool

is giving to the user, who could eventually forget about how to store the information gathered, thereby


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avoiding the arduous task of designing and implementing a data model on a database management

system (DBMS). Instead, users can rely fully on the platform to do it. It uses a relational data base

that can be accessed through the web page but also from GIS (Geographic Information Systems) and

DBMS (Data Base Management system) software allowing the use of programming languages for

analytical and modelling of spatial data such as R, PL/R or Python. In fact the access to the database

is the only utility where we experienced problems at trying unsuccessfully to access the host.

Once the user’s data are in the platform, they are summarized to the user, offering her/him a very

detailed overview of the data that are already uploaded. It also has a duplicate finder tool, which is

very useful when uploading information from several devices looking very similar to each other. The

user could also define roles for the access and use of the uploaded information. This is the second

important feedback: giving support for collaborative working, and data discoverability (increasingly

demanded by scientific journals).

In addition, the tool has a set of available environmental information that may help the user to link

animal movement data with information from global environmental datasets, like weather models,

primary productivity and satellite imagery, among others. This is the third important feedback offered

by this platform.

Last but not least, the platform offers a data manager that could be programmed to get information

from the tracking data provider. In the most common case: people using as tracking satellite the

ARGOS system and their transmitter terminals (PTTs), must necessarily download data within a ten-

days window. During this time, the user should access the Argos’ server and download this

information. Otherwise, the information could be only retrieved by paying additional money. The

ovebank platform has a live feeds component that allows the user to download automatically this

information not only limited to Argos, but also from other GSM data providers such as e-obs,

Microwave, Celltracktech, Fleetronic or Ecotone. Although this is quite specific of the animal

tracking community, task programming can be very useful (e.g. send me an e-mail with my records

when they get 1000) and it is something that could be easily provided to the user.

UvA-BiTS

UvA-BiTS is a bird tracking system developed by the University of Amsterdam, and used worldwide

to track different bird species. Though this is not a biodiversity information platform, but a service

provider, it also solves some of the challenges found in the evaluation of applications devoted to track

species occurrence.

The system can transfer information as soon as an internet connection is established. Before, it is

stored locally in the form of codified txt files. Data are then automatically incorporated to a

PostgreSQL/PostGIS/ spatial database. At that point, original data are safely stored and due to internal

backup processes, it is virtually impossible to erase or modify them. All the following interactions

with your data are done using a virtual lab for bird movement modeling from where it is possible to

reschedule the tracking devices or generate 3D simulations of the tracks. Of course it is also possible

to transfer a copy of the raw data through Dropbox, as well as, access to database through specific

tools such as phpPgAdmin or pgAdmin III. The platform allows building KMZ files (a zipped version

of KML, which is the standard used by Google Earth mapping application) with the movement of

your tracked individuals and using the information from the accelerometer and the already tested

movement patterns (flapping, resting, soaring) to add this information to the KMZ files. Similarly, the

platform offers software to synchronize the information from the accelerometer to a video file, thus

linking the observed movement pattern with the information given by the accelerometer. It also allows

getting information of environmental variables that may help the user to link animal movement data

with information from global environmental datasets. Like Movebank, the spatial database is built

under the PostGIS/postgreSQL architecture and data could be also accessed directly from DBMS

tools such as pgAdmin or even from GIS and statistical software (e.g. R, QGIS). Unlike Movebank,

the access to the host was straightforward.

https://en.wikipedia.org/wiki/PostgreSQL#pgAdmin

Introducing & integrating EU BON common tools M34

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