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A history is a process that is the sum of the totality of processes taking place in the spatiotemporal region occupied by a material entity or site, including processes on the surface of the entity or within the cavities to which it serves as host. (axiom label in BFO2 Reference: [138-001])

ICEs are abstract entities that convey information about things in the world. ICEs represent the information encoded in physical artifacts such as photographs, books, or digital storage medium (as opposed to the physical medium itself). A particular ICE can be encoded simultaneously in many physical or digital mediums and formats. For example, only one instance of the first edition of "War and Peace" exists as an ICE, representing information conveyed in this work. But many instances of "War and Peace" exist at the level of their physical concretizations in printed books or digital devices.

MHB: A figure depicting a tabular representation of data, typically created to highlight a particular aspect or view of a larger data set. They may include derived statistical measures summarizing the primary data.

The concept described here represents a tables generated for purposes of documentation or publication of results, as opposed to database tables defined as structures for primary storage of source data in some information system.

MHB: A figure is a graphical presentation of information created as a vehicle to share with an audience, e.g. in a publication or report. Figures may include raw and/or summarized/derived data, or diagrammatic abstractions of results, conclusions, or hypotheses.

Figures often present data in a form that is summarized or abstracted for human consumption, using graphical conventions that may sacrifice detail of underlying data for ease or understanding.

The notion of a proposition, and its relationship to an assertion, derives from the domain of logic and philosophy [12]. Propositions are abstract entities that, like numbers, are independent of space and time. They represent only the meaning that is expressed in a particular agent’s assertion, and are ‘sharable’ in that the same proposition can be expressed in many different assertions. Propositions are primary bearers of truth value, in that they are true or false.

From http://plato.stanford.edu/entries/propositions/: "Propositions are the sharable objects of the attitudes and the primary bearers of truth and falsity. This stipulation rules out certain candidates for propositions, including thought- and utterance-tokens, which presumably are not sharable, and concrete events or facts, which presumably cannot be false."

Assertions as defined in SEPIO are the result of some logical inference made based on the interpretation of evidence. They put forth a proposition that may or may not be true - the validity of which ultimately rests on the quantity, quality, diversity, and concordance of evidence supporting it.

Statements directly reporting study results or observations are not considered assertions in this sense, as they merely report what was observed or recorded, but do not rely on a leap of logical inference (see 'study finding').

An evidence line is created through the interpretation of one or more pieces of information that collectively support a meaningful argument for or against a proposition. To qualify as an Evidence Line, this argument must be independently significant as evidence - i.e. it must be capable of affecting the probability of accepting the target proposition as true. This does not mean, however, that it is independently sufficient to establish belief in the proposition, as additional evidence lines may be required to ultimately accept the proposition as true. For example, in the ACMG framework for variant interpretations establishes "absence in population databases" as a type of evidence line that can argue for the pathogenicty of a particular variant. But this argument alone is not considered sufficient to establish a variant's pathogencity, as the other types of evidence are additionally required to establish the truth of this Proposition (e.g. a line of evidence demonstrating the variant to have a deleterious effect on protein function, or showing it to segregate with disease features in a family tree).

An assertion process includes the processes of interpreting data and evaluating it as evidence for the validity of a proposition. They are affected by a particular agents on a particular occasions, and can be specified by assertion methods or guidelines.

Data generation process are typically experimental studies or observations, but can include any process generating information used to evaluate a claim. This is an organizational class that groups more specific types of such processes that are most commonly used in generating data used as evidence to support claims. These processes produce informational artifacts such as measured data values, derived statistical calculations and confidence measures, or statements representing summaries or conclusions drawn from such data.

Assertion methods can be informal guidlines or heuristics that support manual evaluation of evidence, e.g. the ACMG Variant Classification Guidelines. Assertion methods can also be formally encoded as algorithms that allow computational or statistical analysis of data as evidence in support of making an assertion - e.g. multifactorial analysis algorithms that operate on specified data to generate a quantitative score indicating the validity of a particular proposition.

In the context of evidence and proveannce, agents are persons, groups, organizations, or software that are responsible for making assertions or generating the evidence and data that support them.

In the context of evidence and provenance for scientific claims, resources are entities that are used by agents to facilitate processes aimed at the generation of assertions or the evidence and data that support them. Examples include instruments, reagents, model systems, software, and protocols.

Ultimately even automated assertions require a human agent to set a confidence threshhold that determines when a computational output (e.g. a sequence similarity p-value) justifies an assesrtion being made.

For scientific assertions/claims, this is the process history leading to the asseertion being made, including processes through which evidence is evaluated, and processes through which information used as evidence is created. The transitive nature of provenance means that the provenance history of informtion used as supporting data becomes provenance for an assertion that uses this information as evidence. Provenance metadata describes these processes, including accounts of who conducted these processes, what entities participated in them, and when/where they occurred.

A research study is considered broadly to be any scientific activity aimed at answering a research question. Studies can be simple or complex, depending on the scope of the question being explored and the extent of resources deployed in doing so. They may include a full research investigation, a set of experiments, or a single experiment or assay. Regardless, that act of summarizing any results as a finding statement is considered part of the study.

The strength of a line of evidence typically depends on the type and quality of the study(ies) that generate its supporting evidence items.

1. Type of Study: In a particular domain of research, certain types of studies are commonly held to produce data that is more meaningful and reliable than others, as evidence for a particular type of assertion. These more reliable studies tend to draw on experimental systems that more accurately replicate the natural phenomenon being studied, and experimental designs that reduce confounding variables and biases. For example, in vivo studies of gene function provide stronger evidence than in vitro systems that may not replicate all aspects of the biology of a living system, and randomized controlled clinical trials are held to be more reliable than retrospective or case control studies that are more susceptible to confounding factors.

2. Quality of Study: A given study can be performed to varying degrees of rigor and comprehensiveness, irrespective of the type of study it represents. This is another important factor in assessing the overall strength of evidence it may produce. Higher quality studies will have carefully defined controls, and be replicated numerous times to ensure reproducibility. Other factors that are often used as secondary indicators of the quality of research data include the quality of the journal in which it is published, and the reputation of the lab or institution performing the study.

In SEPIO, the notion of evidence quality is captured in an attribute separate from the notion of evidence strength. And the notion of study type can be represented in the type assigned to a study instance, or the evidence code used to type an evidence line (which are often based on the type of study that produced the evidence data). Evidence strength typically relies on assessment of one or both of these more fundamental attributes of evidence.

'Evidence Item' is a broad term covering any information interpreted as evidence in the act of making an assertion.

Classes representing different types of evidence items are imported here from ontologies such as OBI, IAO, and STATO, and include: - measured data values - derived data values such as statistical calculations and confidence measures - figures presenting or describing such data - author statements representing summaries of or conclusions drawn from such data - established facts in a field of research

Note that publications and reports are considered separately from 'evidence items', as a they contain information (data values, figures author statements) that can be used as evidence. But on its own a publication or report is merely a proxy for this information.

Contribution instances organize information about who, when, where, how (e.g. what roles were played), and on whose behalf these actions were performed. The scope of a Contribution instance includes only the actions of a single agent in contributing to a particular artifact - which may be performed in one continuous effort, or multiple discrete sessions of work. The contributing agent can be a single individual, or an organization of multiple individuals acting together.

A study finding is a statement that summarizes the immediate results of a particular experiment or study. It describes only what was directly observed, measured, or calculated, and optionally the experimental context of these observations. It does not describe more general conclusions that may have been inferred from such results. As such, the scope of what a finding describes is limited to the direct participants in the study - i.e. it is about only the instances observed or measured in the study. It makes no broader inference or conclusion about types or classes to which these instances belong.

This is not to say that the onserved findings are necessarily accurate or correct - only that they were indeed made in a particular study. For example, the finding that "Sequencing of DNA from patient X revealed an A->T mutation at position 2143 in gene Y" is a matter of fact - this was the outcome of the assay, even if the finding is an artifact of low sequencing coverage. Metadata about the finding (e.g. sequencing methods/reagents used, coverage or read depth) are recoded so users can judge for themselves whether the objectively reported finding accurtely reflects the biology it describes.

Statements are sentences (or, more precisely, the information content of sentences) that declare a definitive or proposed fact - expressing something about the world or one's experience of it that may or may not be true. The identity of a particular Statement is dependent upon (1) what it reports as true (its semantic content), (2) the Agent stating it, and (3) the occasion on which the statement is made.

The phrase "the pink elephant" describes an entity, but is not a 'statement' as it has no truth value. By contrast, the phrase "the pink elephant is in the room" is a statement as this can be evaluated for its truth.

An observation reflects only what an agent observed or device recorded. It is therefore considered a matter of fact, rather than a propositional assertion that may or may not be true. This does not mean that the observations or recordings were necessarily accurate or correct - only that they were indeed made by an agent. For example, the observation that "I counted six ducks in the pond" is a matter of fact, even if there are actually seven because I missed one that was hiding in the reeds.

Evidence Statements can be 'findings' that summarize data and outcomes from a particular study, or evidence-based 'assertions' that make a broader claim, as inferred from the interpretation and evaluation of previously generated information.

Instances of this class can include elemental 'data items' such as experimental measurements or derived statistical calculations or scores (e.g. a 548.5 mg/dl blood glucose level, or a 1.3951e-24 p-value), data sets containing several such values and possibly metadata about them, or tables/figures that render such data for human consumption.

The notion of 'evidence data' is meant to be contrasted with the notion of an 'evidence statement', which is a statement used as evidence. (Statements here are logical sentences with truth value that are typically supported by interpretation of more fundamental data).

Source: ClinVar (" 'Phenotyping only' is intended for variants that are submitted to ClinVar to provide individual observations with detailed phenotype data, such as submissions from clinicians or patient registries, without an interpretation from the submitter. The interpretation from the testing laboratory may be provided in a separate field.") ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/release_notes/20170803_data_release_notes.pdf

Source: ClinVar (" 'Provider interpretation' is intended for variants that were interpreted by a clinical provider. For example, the clinician may reclassify the variant using detailed patient phenotype information that was unavailable to the testing laborator") ftp://ftp.ncbi.nlm.nih.gov/pub/clinvar/release_notes/20170803_data_release_notes.pdf

An information content entity that specifies instructions for how a particular objective can be achieved.

The data items comprising a study data set may include primary measurements, derived calculations, metadata about study participants and methods, or parameters of execution.

A research study is considered broadly to be any scientific activity aimed at answering a research question. Studies can be simple or complex, depending on the scope of the question being explored and the extent of resources deployed in doing so. They may include a full research investigation, a set of experiments, or a single experiment or assay.

Opinions are matters of subjective judgement or taste, and typically not able to be scientifically validated as objectively true based on empirical evidence. In this way, they are distinct from statements that have inherent truth value.

The quality of an evidence item is a measure of the rigor applied in performing the study and anlysis tasks used to produce it. Higher quality studies will have carefully defined controls, and be replicated numerous times to ensure reproducibility. Other factors that are often used as secondary indicators of the quality of research data include the quality of the journal in which it is published, and the reputation of the lab or institution performing the study. Evidence qualitty is an important factor in assessing the overall strength of evidence it may produce.

Recommendations are another type of statement besides assertions that can be supported by evidence. They don't put forth a proposed facts as true, but instead describe a recommended action on the foundation of evidence that it will be beneficial.

A hypothesis is another type of statement that, like an assertion, can be supported by evidence. It puts forward a possible fact as true, but does not express an agents belief in this fact. Rather, it is a possible fact whose truth will be explored via reasoning or experimentation.

'Record' here is broadly defined to include any document holding (typically) structured information about a particular entity. This can include individual records from a database or knowledgebase that hold information about the subject of the record.

Specific examples include: - A single row of a table in a relational database. - A JSON or XML document holding information describing some entity. - A VCF File describing varaints in a patient, or a single row in this VCF file describinng a particular variant. - A Uniprot knowldgebase record describing the BRCA2 protein (http://www.uniprot.org/uniprot/P51587) - A ClinVar knwoledgebase record describing an assertion about the pathogenicity of the NM_000059.3(BRCA2):c.10G>T variant (https://www.ncbi.nlm.nih.gov/clinvar/variation/51063/) - An EHR record for a hospital patient.

Agents typically persons, organizations, or software that perform tasks, including making assertions, or generating information used as evidence. In the execution of such tasks, an 'agent role' is realized that inheres in the agent.

Software agents are autonomous computer programs that carries out tasks. They designed to execute algorithm(s) that receive some input information and direct its processing to achieve a specified goal. Artificial intelligence is not required to be considered a software agent - only that it can autonomously execute programmed tasks.

The Group class represents a collection of individual agents (and may itself play the role of a Agent, ie. something that can perform actions).

This concept is intentionally quite broad, covering informal and ad-hoc groups, long-lived communities, organizational groups within a workplace, etc. Some such groups may have associated characteristics which could be captured in RDF (perhaps a homepage, name, mailing list etc.).