CRMsci

CRMsci

CRMsci: the Scientific Observation Model

Center for Cultural Informatics, Institute of Computer Science

Foundation for Research and Technology - Hellas

Martin Doerr, Chryssoula Bekiari,

Athina Kritsotaki, Gerald Hiebel, Maria Theodoridou

CIDOC 2014Dresden, September 9th, 2014

CRM sci Current situation

EU infrastructure projects aim to publish linked open data about scientific observations in geology, biology, archaelogical excavations, digital productions and medicine

Existing standards for scientific observation INSPIRE –earth science oriented promoted by EU

OBOE – life science oriented, support semantic annotation

SEEK – ecology oriented - framework

Darwin Core – a general use metadata scheme for biodiversity

Focus on : semantic annotation process of data sets

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Theories are formalized sets of concepts that organize observations and predict and explain phenomena and demand a solid empirical base of evidence

Raw data provided by the data sets per se are of little use

Scientific observation forms the basis for understanding the phenomena being studied and it is a process by which we advance our understanding of the world.

It common to all sciences the workflow of forming of a hypothesis to perform and explain observations that are made, the gathering of data, and the drawing of conclusions that confirm or deny the original hypothesis.

The difference between the types of sciences is in what is considered data, and how data is gathered and processed

The cultural discourse includes information from all sorts of sciences and product of sciences, i.e. digital productions, biological samples, specimen of physical objects (materials, fluids etc.).

Scientific data and metadata can be considered as historical records.

Epistemological Considerations

CRM sciCommon Workflow

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Form of a hypothesis to perform an observation (select parameters, properties,

signals and the way of converting these to data)

Perform the observations. (They are only concerned with objects or events that are

observable, either directly or indirectly )

Explain the observations made and the gathering of data

Draw conclusions based upon this data, (make a scientific hypothesis - tentative

explanations about the observations made)

Deduce the implications (test them through further observation, compare the results)

Confirm, deny, re-evaluate the original hypothesis

Formulate valid theories (allow others to repeat the observations)

CRM sciLimitations

Problems with the existing standards:

They model observation isolated from other actions that are preceding or following an observation event,

They leave out information that would allow for later assessing, the quality and precision of the results or for re-evaluating existing measured data due to new evidence which would not require redoing the measurement itself, if suitable raw data were provided.

Even though they are using the above standards to publish data in repositories, they typically lack the required information to facilitate effective long-term preservation and interpretation of data.

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CRM sciThe CRMsci – overview(1)

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has been developed bottom up from specific metadata examples such as water sampling in aquifer systems, earthquake shock recordings, landslides, excavation processes, species occurrence and detection of new species, tissue sampling in cancer research, 3D digitization,

takes into account relevant standards, such as INSPIRE, OBOE, Darwin Core, national archaeological standards for excavation, Digital Provenance models and others.

describes, together with the CIDOC CRM, a discipline neutral level of genericity, which can be used as a general ontology of human activity, things and events happening in spacetime

uses the same encoding-neutral formalism of knowledge representation as the CIDOC CRM, and can be implemented in RDFS, OWL, on RDBMS and in other forms of encoding

reuses, wherever appropriate, parts of CIDOC CRM, we consider as part of this model all constructs used from ISO21127, together with their definitions following the version 5.1.2 maintained by CIDOC.

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Metadata about:The human observer

The object of observation (a “thing”, “something”, a process or a state?),

The observation hypothesis (choice of parameters),

The identity of the object, if any,

The environment, time and location

The condition of the thing,

The instrumentation and method used

The identity, authenticity and transmission of the produced records

The inference making

The CRMsci – overview (2)

CRM sciEvents and Activities

S18 Alteration

S17 Physical Genesis

E63 Beginning of Existence

E12 Production

E5 Event

E11 Modification

E13 Attribute Assignment

S5 Inference Making S4 Observation

S6 Data Evaluation

S8 Categorical Hypothesis Building

S7 Simulation-Prediction

S1 Matter Removal

S2 Sample Taking

E7 Activity

E16/S21 Measurement

S40 Encounter Event

S3 Measurement by Sampling

E80 Part Removal

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CRM sciObservable Entity

S10 Material Substantial

S14 Fluid Body S11 Amount of Matter

E70 Thing

E18 Physical Thing

S15 Observable Entity

E2 Temporal Entity

S13 Sample

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S12 Amount of Fluid

E77 Persistent Item

E1 CRM Entity …comprises items(E77) or phenomena (E2) that can be observed such as physical things, their behavior, states and interactions or events, either directly by human sensory impression, or enhanced with tools and measurement devices,.

S16 State

E3 Condition State

E55 Type

S9 Property Type

S20 / E26 Physical Feature

E53 Place

S22 Segment of MatterE27 SiteE25 Man-Made Feature

E5 Event

Inspired by OBOE

CRM sci


S14 Fluid BodyS11 Amount of Matter

E70 Thing

E18 Physical Thing


S1 Matter Removal

O5 removed

S2 Sample Taking

S13 Sample

E7 Activity

O3 sampled from

O1 diminished

O2 removed

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E53 Place

O4 sampled at

E55 Type

O20 sampled from type of part

E2 Temporal EntityE77 Persistent Item

O7 contains or confines

O15 occupied

P156 occupies

Matter Removing and Sampling

E3 Condition State

P44 has condition

E57 Material P45 consists of P46 is composed of

CRM sciMonitoring observation activities


S5 Inference Making S4 Observation

S6 Data Evaluation

E7 Activity

E16 Measurement

S19 Encounter Event

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E55 Type

E54 Dimension

P40 observed dimension

P2 has type

O11 observedProperty

S9 Property Type

O14 assigned dimension


E70 Thing

E18 Physical Thing


E5 Event

O10 observed

O17 has dimension

O32 has found object

O16 described

P39 measured

CRM sci

S19 Encounter Eventurn:catalog:IOL:POLY:Sphaerosyllis-levantina-ALA-IL-7-Oct.2009

E18 Physical ThingSphaero-levantina-003O32 has found object

E21 PersonSarah Faulwetter

E52 Timespan7 October 2009

E53 Place Haifa Bay Ecosystem Station 1

Equipment TypeWA265/SS214

Equipment TypeVan Veen Grab

E55 Type

E53 PlaceIsrael

P14 carried out by

O21 has found at(witnessed)

P4 has timespanP2 has type

P125 used object of type

P127has broader term

O7 contains or confines (is contained or confined)

Ecosystem Typesandy - muddy

sediments

S19 Encounter Event

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Inspired by Darwin Core

CRM sciS5 Inference Making

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S5 Inference Making

S6 Data EvaluationS8 Categorical

Hypothesis Building

S7 Simulation-Prediction

E7 Activity

comprises the action of making propositions and statements about particular states of affairs in reality or in possible realities or categorical descriptions of reality by using inferences from other statements based on hypotheses and any form of formal or informal logic.

E54 Dimension010 Assigned dimension (dimension was assigned by)

S19 Observable Entity011 described ( was described by)

E1 CRM Entity

E70 Thing

P33 used specific technique

(was used by)

P16 used specific object(was used for)

P15 was influenced by(influenced )

P17 was motivated by(motivated)

E29 Design or Procedure

assumptions developed by “induction” from finite numbers of observation of particular thing.Based on inference rules and theory

executing algorithms or software for simulating the reality or not by using mathematical models

concluding propositions on a respective reality from observational data by making evaluations based on mathematical inference rules and calculations using established hypotheses

CRM sciApplications

Informed by the IAM model (argumentation)

EU FP7 - PSP InGeoCloudso European Space Agency: satellite data

EU FP7-INFRASTRUCTURES-2012-1 ARIADNEo Supermodel for CRMarchaeo

EU - FP7 - CP & CSA iMarineo Informs and complements MarineTLOo Extended MarineTLO used in LifeWatch Greece, being promoted to

LifeWatch

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CRM sciConclusions

Our aim is :

• to open the discussions in CIDOC about subjects concerning the conceptual modelling about products of human activities.

• to suggest to CIDOC to approve that modelling scientific activities is a valid scope for CIDOC and could be a working item for the CRM-SIG WG

Needed: Still to be done: Specializations into analytical methods and reference data sets

Links: http://www.ics.forth.gr/isl/CRMext/CRMsci.rdfs

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Thank you !!!

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Documents

Transcript of CRMsci