CRMsci: the Scientific Observation Model Martin Doerr, Chryssoula Bekiari, Athina Kritsotaki, Gerald Hiebel, Maria Theodoridou Center for Cultural Informatics, Institute of Computer Science Foundation for Research and Technology - Hellas CIDOC 2014 Dresden, September 9 th, 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 2
CRM sci Epistemological Considerations ü Theories are formalized sets of concepts that organize observations and predict and explain phenomena and demand a solid empirical base of evidence ü Raw data provided by the data sets per se are of little use ü 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. ü 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. ü The difference between the types of sciences is in what is considered data, and how data is gathered and processed ü 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. ). ü Scientific data and metadata can be considered as historical records. 3
CRM sci Common Workflow Ø Form of a hypothesis to perform an observation (select parameters, properties, signals and the way of converting these to data) Ø Perform the observations. (They are only concerned with objects or events that are observable, either directly or indirectly ) Ø Explain the observations made and the gathering of data Ø Draw conclusions based upon this data, (make a scientific hypothesis - tentative explanations about the observations made) Ø Deduce the implications (test them through further observation, compare the results) Ø Confirm, deny, re-evaluate the original hypothesis Ø Formulate valid theories (allow others to repeat the observations) 4
CRM sci Limitations 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. 5
CRM sci The CRMsci – overview(1) Ø 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, 3 D digitization, Ø takes into account relevant standards, such as INSPIRE, OBOE, Darwin Core, national archaeological standards for excavation, Digital Provenance models and others. Ø 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 Ø 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 Ø reuses, wherever appropriate, parts of CIDOC CRM, we consider as part of this model all constructs used from ISO 21127, together with their definitions following the version 5. 1. 2 maintained by CIDOC. 6
CRM sci The CRMsci – overview (2) 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 7
CRM sci Events and Activities E 5 Event E 7 Activity E 63 Beginning of Existence E 13 Attribute Assignment S 18 Alteration S 5 Inference Making S 4 Observation S 17 Physical Genesis E 11 Modification S 8 Categorical Hypothesis Building S 6 Data Evaluation S 7 Simulation-Prediction S 1 Matter Removal E 12 Production E 16/S 21 Measurement E 80 Part Removal S 2 Sample Taking S 40 Encounter Event 8 S 3 Measurement by Sampling
CRM sci Observable Entity E 1 CRM Entity Inspired by OBOE S 15 Observable Entity E 2 Temporal Entity E 77 Persistent Item …comprises items(E 77) or phenomena (E 2) 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, . E 70 Thing S 16 State E 5 Event S 10 Material Substantial E 3 Condition State S 14 Fluid Body S 11 Amount of Matter E 18 Physical Thing E 55 Type S 20 / E 26 Physical Feature S 12 Amount of Fluid S 9 Property Type E 53 Place S 13 Sample E 25 Man-Made Feature E 27 Site S 22 Segment of Matter 9
CRM sci Matter Removing and Sampling S 19 Observable Entity E 7 Activity E 2 Temporal Entity E 77 Persistent Item E 55 Type S 1 Matter Removal O 20 sampled from type of part E 70 Thing O 1 diminished E 57 Material E 3 Condition State P 45 consists of P 46 is composed of P 44 has condition O 3 sampled from S 2 Sample Taking S 10 Material Substantial O 2 removed O 4 sampled at E 53 Place O 5 removed S 11 Amount of Matter O 7 contains or confines S 14 Fluid Body E 18 Physical Thing P 156 occupies S 13 Sample O 15 occupied 10
CRM sci Monitoring observation activities E 7 Activity P 2 has type E 13 Attribute Assignment S 5 Inference Making E 55 Type S 4 Observation O 10 observed O 11 observed. Property S 9 Property Type O 16 described S 15 Observable Entity S 6 Data Evaluation P 39 measured E 5 Event O 14 assigned dimension E 70 Thing S 19 Encounter Event E 16 Measurement O 17 has dimension P 40 observed dimension E 54 Dimension S 10 Material Substantial O 32 has found object E 18 Physical Thing 11
CRM sci S 19 Encounter Event E 18 Physical Thing Sphaero-levantina-003 O 32 has found object Inspired by Darwin Core E 21 Person Sarah Faulwetter P 14 carried out by S 19 Encounter Event urn: catalog: IOL: POLY: Sphaero syllis-levantina-ALA-IL-7 Oct. 2009 E 53 Place O 21 has found at(witnessed) Haifa Bay Ecosystem Station 1 E 55 Type P 2 has type P 4 has timespan Ecosystem Type E 52 Timespan P 125 used object of type Israel O 7 contains or confines (is contained or confined) sandy - muddy sediments 7 October 2009 Equipment Type WA 265/SS 214 P 127 has broader term Equipment Type Van Veen Grab 12
CRM sci S 5 Inference Making E 1 CRM Entity P 15 was influenced by (influenced ) P 17 was motivated by (motivated) E 7 Activity P 16 used specific object (was used for) E 29 Design or Procedure 010 Assigned dimension (dimension was assigned by) S 5 Inference Making assumptions developed by “induction” from finite numbers of observation of particular thing. Based on inference rules and theory E 70 Thing P 33 used specific technique (was used by) E 13 Attribute Assignment S 8 Categorical Hypothesis Building 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. S 6 Data Evaluation E 54 Dimension 011 described ( was described by) S 19 Observable Entity concluding propositions on a respective reality from observational data by making evaluations based on mathematical inference rules and calculations using established hypotheses S 7 Simulation-Prediction executing algorithms or software for simulating the reality or not by using mathematical models 13
CRM sci n Informed by the IAM model (argumentation) n Applications EU FP 7 - PSP In. Geo. Clouds o n EU FP 7 -INFRASTRUCTURES-2012 -1 ARIADNE o n European Space Agency: satellite data Supermodel for CRMarchaeo EU - FP 7 - CP & CSA i. Marine o o Informs and complements Marine. TLO Extended Marine. TLO used in Life. Watch Greece, being promoted to Life. Watch 14
CRM sci Conclusions 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 15
CRM sci Thank you !!! 16
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