Provenance Data Model RFC #2 Page


After the first RFC period (November 2018) during which no consensus was reached, the Provenance model has been revamped.

  • Based on a similar skeleton, this new version has significant differences with the former one.
  • The current document has been issued after some intensive sessions held in the Spring-19 meeting in Paris
  • After this, the TCG sugested to go in a new WG stage, shorten to 4 weeks, which actually ended on 2019/07/19.


The attached document presents the IVOA Provenance Data Model v1.0 which is proposed for review is accessible in ivoadoc.

This document describes how provenance information can be modeled, stored and exchanged within the astronomical community in a standardized way. We follow the definition of provenance as proposed by the W3C (, i.e. that "provenance is information about entities, activities, and people involved in producing a piece of data or thing, which can be used to form assessments about its quality, reliability or trustworthiness."

Such provenance information in astronomy is important to enable any scientist to trace back the origin of a dataset (e.g. an image, spectrum, catalog or single points in a spectral energy distribution diagram or a light curve), a document (e.g. an article, a technical note) or a device (e.g. a camera, a telescope), learn about the people and organizations involved in a project and assess the reliability, quality as well as the usefulness of the dataset, document or device for her own scientific work.

Reference Implementation

CTA implementations (M. Servillat)

The Cherenkov Telescope Array (CTA) is the next generation ground-based very high energy gamma-ray instrument. Contrary to previous Cherenkov experiments, it will serve as an open observatory providing data to a wide astrophysics community, with the requirement to propose self-described data products to users that may be unaware of the Cherenkov astronomy specificities. Because of the complexity in the detection process and in the data processing chain, provenance information of data products are necessary to the user to perform a correct scientific analysis.

Provenance concepts are relevant for different aspects of CTA:

  • Pipeline: the CTA Observatory must ensure that data processing is traceable and reproducible, making the capture of provenance information necessary.
  • Data diffusion: the diffused data products have to contain all the relevant context information as well as a description of the methods and algorithms used during the data processing.
  • Instrument Configuration: the characteristics of the instrument at a given time have to be available and traceable (hardware changes, measurements of e.g. a reflectivity curve of a mirror, ...)
In the context of CTA, two main implementations were developed:
  • a python Provenance class dedicated to the capture of provenance information of each CTA pipeline tool (including contextual information and connected to configuration information)
  • OPUS: a job controller that can execute predefined jobs on a work cluster and expose the results. This job controller follows the IVOA UWS pattern and the definition of jobs proposed in the IVAO Provenance DM (Description classes), so as to capture and expose the provenance information for each job and result via a ProvSAP interface.

Pollux Provenance: a simple access protocol to provenance of theoretical spectra (M. Sanguillon)

POLLUX is a stellar spectra database proposing access to high resolution synthetic spectra computed using the best available models of atmosphere (CMFGEN, ATLAS and MARCS), performant spectral synthesis codes (CMF_FLUX, SYNSPEC and TURBOSPECTRUM) and atomic line lists from VALD database and specific molecular line lists for cool stars.

Currently the provenance information is given to the astronomer in the header of the spectra files (depending on the format: FITS, ASCII, XML, VOTable, ...) but in a non-normalized description format. The implementation of the provenance concepts in a standardized format allows users on one hand to benefit from tools to create, visualize and transform to another format the description of the provenance of these spectra and on a second hand to select data depending on provenance criteria.

In this context, the ProvSAP protocol has been implemented to retrieve provenance information in different formats of the serialized data: PROV-N, PROV-JSON, PROV-XML, VOTABLE and to build diagrams in the following graphic formats: PDF, PNG, SVG. These serializations and graphics are generated using the voprov python package derived from the prov Python library (MIT license) developed by Trung Dong Huynh (University of Southampton).

SVOM Quick Analysis (L. Michel)

The SVOM satellite is a Sino-French variable object monitor to be launched in 2021. When a transient object is detected, a fast alert is sent to the ground station through a worldwide VHF network. Burst advocates and instrument scientists are in charge of evaluating the scientific relevance of the event. To complete the assement, scientists have at their disposal high level data products such as light curves or spectra generated by an automatic data reduction pipeline. In some case, they might need to reprocess raw data with refined parameters. To do so, scientific products (calib_leve >= 2) embedd their JSON provenance serialization in a specific extension. This provenance instance can be extracted, updated and then uploaded to a dedicated pipeline to reprocess the photon list with different parameters.

ProvHIPS CDS prototype service providing provenance metadata for HiPS datasets stored at CDS. ( F. Bonnarel, A. Egner)

This prototype is both an implementation of Provenance Data Model and of the DAL ProvTAP protocol.

ProvTAP is a proposal for providing Provenance metadata via TAP services. The current draft for this DAL protocol definition (TBC) can be found here). It is basically providing a TAP-schema mapping the IVOA Provenance model onto a relational schema.

  • A first implementation of a TAP service for Provenance metadata bound to ObsTAP metadata was done by F . Bonnarel, M. Louys and G. Mantelet in August 2018.
This first prototype of a ProvHiPS service was implementing this ProvTAP specification on top of a PostGres relational database with metadata for a few HiPS datasets generation as well as Schmidt plate digitization, image cutouts operations and RGB image construction. A full presentation of the prototype with a "slide demo" including a lot of ADQL queries can be found here.

HiPS is an all sky organisation of astronomical data (images, catalogs) based on Healpix tesselation of the sky, with iso-area, iso-latitude property of the tiles building up the mesh structure. (see HiPS description) Each HiPS data collection is obtained by the reprojection on the celestial sphere of original FITS images belonging to the same image collection. The hipsgen code fis used at CDS to generate the HiPS format of data stored at CDS image data base. These image collections are themselves produced in various ways and stages , from raw data, mosaiced, calibrated, normalized formats before they can be ingested into the HiPS processing activity.

For example in the case of HST images it is possible to trace back from HST HiPS datasets to stacked images collection, single calibrated exposure collections or raw exposure image collections In the case of HiPS obtained from Schmidt plate datasets, it is possible to go back to the plates through the digitized images. As an HiPS metadata tree also stores the identifiers of the progenitor images inside each tile it is also possible to trace the history of a tile. The whole HiPS dataset can appear both as an entity and as a collection of tiles.

We are currently creating a new version of our ProvHiPS database containing the history of the HiPS tiles for all the HST HIPS subsets (see here, here and there ) as well as the HiPS version of the DSS2 Schmidt plates survey. This is also globally illustrated by this viewgraph and the result of this work will be presented at next interop meeting in Groningen.

A triple Store implementation for an image database (M.Louys, F.X Pineau, L.Holzmann, F.Bonnarel).

We have implemented the IVOA provenance DM proposed using a BlazeGraph triplestore data base. It handles astronomical images as Entities with a succession of Activities that produce or consume them. Entities represent image files or photometric plates. Activities are described by an ActivityDescription instance which gives the template for execution of several different Activity instances based on the same template. / The Blazegraph prov-test prototype translates the classes via an Ontology of Object in OWL. The Prov_Owl diagram attached exposes the main objects of this ontology. This prototype implements:

  • Agent, Activity, Entity ( mainly DatasetEntity)
  • Used, wasGeneratedBy
  • wasAssociatedTo, wasAttributedTo
  • wasDerivedFrom
  • hasParameter
  • ActivityDescription
  • describedBy
  • describes
  • UsageDescription
  • GenerationDescription
  • ParameterDescription
  • Parameter
ActivityConfiguration in our case implements only the Parameter Class and no ConfigFile instances. Therefore the ActivityConfiguration is implemented using a direct relation called hasParameter attached to the Activity instance. This hasParameter predicate is an implementation of wasConfiguredBy/artefactType=Parameter in our implementation. It supports the same cardinality for the Activity <--> parameter relation as shown in the Proposed REC UML diagram.

The translation of Association classes like wasAttributedTo or wasAssociatedTo in the ontology requires a kind of predicate extension. These two association classes contain a role attribute , which relates to an agent instance when it is in relation to an Activity (associatedTo) or in relation with an Entity ( wasAttributedTo) . this role value depends only on the relation , and not on the two nodes objects. Therefore the ontology realizes this by adding a new predicate : holdsRoleinTime on the agent instance when it is active in one of these two relations. Description links are represented by the describedBy predicate and represents the generic link from left classes in the UML diagram to their description companion class in the right part.

The Blazegraph prov-test database contains all the triples extracted from the initial PostGres DB. It answers queries formed in SPARQL, compliant with the defined ontology. Examples of queries available with this prototype implementation are given in the attached document ProvQuerytest-3store.pdf

The list shows how the user can query for provenance metadata . Some queries focus on the management aspect of these metadata, as seen from the data provider's point of view. These can help to re-organise and optimise the image data base and their provenance metadata. It shows the various queries an astronomer can build in order to appreciate data quality from the recording of Activities, applied methods, parameter values, dataset dependency, typically the link to a progenitor Entity implemented using the wasDerivedFrom relation.

MuseWise Provenance: Implementation of ProvTAP, ProvSAP, W3C, and visualisation (O. Streicher)

MUSE is an integral field spectrograph installed at the Very Large Telescope (VLT) of the European Southern Observatory (ESO). It consists of 24 spectrographs, providing a 1x1arcmin FOV (7.5" in Narrow Field Mode) with 300x300 pixel. For each pixel, a spectrum covering the range 465-930nm is provided. MuseWise is the data reduction framework that is used within the MUSE collaboration. It is built on the Astro-WISE system, which has been extended to support 3D spectroscopic data and integrates data reduction, provenance tracking, quality control and data analysis. The MUSE pipeline is very flexible and offers a variety of options and alternative data flows to fit to the different instrument modi and scientific requirements.

MuseWISE hast provenance "built-in", i.e. it stores all relevant provenance information during the execution of the pipeline. Our implementation presents this provenance information conform to the upcoming standard. Activity configuration is modelled as Entities, to enable accessing the provenance of the configuration, and for W3C compatibility. The provenance data are primarily presented as a relational database which is conform to the IVOA Provenance model and close to the upcoming ProvTAP standard. It can be queried via SQL and ADQL queries. The database covers the full processing chain from the exposure to the science-ready product, including all necessary entity, activity, usage, and generation descriptions.

On top of the relational representation, we developed a few tools for alternative acess methods:

1. A complete ProvSAP sever that translates REST queries into relational queries for a provenance database, converts the result into the W3C Provenance model and presents it in W3C formats (XML, Prov-N, OWL2, JSON). The part of the IVOA model that is not W3C compatible (ActivityConfiguration package) is not queried. The result can be processed and stored by any W3C compatible tool and service.

2. A tabular vizualisation prototype of the provenance based on the W3C model (from the first tool), to present the data to the user. This page shows an example output for the complete chain of one data product. We also tested this prototype on other available data that we locally stored in databases with the same structure as our MuseWISE implementation: HIPS generation, CTA, and cube segmentation.

APPLAUSE Provenance: publically available implementation via TAP (A. Galkin)

German astronomical observatories own considerable collection of photographic plates. While these observations lead to significant discoveries in the past, they are also of interest for scientists today and in the future. In particular, for the study of long-term variability of many types of stars, these measurements are of immense scientific value. There are about 85000 plates in the archives of Hamburger Sternwarte, Dr. Karl Remeis-Sternwarte Bamberg, and Leibniz-Institut für Astrophysik Potsdam (AIP). The plates are digitized with high-resolution flatbed scanners. In addition, the corresponding plate envelopes and observation logbooks are digitized, and further metadata are entered into the database.

The APPLAUSE implementaion of the provenance model is the only publically accessable imlementation and is accessable through the TAP protocol on

Currently, the provenance schema for the APPLAUSE Data Release 3 has 528082 entities, 425421 activities, 327 agents, 799097 used relations, 471888 wasGeneratedBy relations and 138552 wasAttributedTo relations.

Implementations Validators

There is no validator for the model as such. Validation tools should be applied to specific implementations of the model.

RFC Review Period: 2019 July 23 - 2019 September 03

Comments from WG members

Comment from -- LaurentMichel - 2019-08-26

  • suggest to put a special mention in the acknowwledgements for Kristin Riebe, as former editor, and another for Mark Cresitello Ditmar and Janet Evans who were so much involved in the project.
Comment from -- LaurentMichel - 2019-08-26 on behalf of Harry Enke <>
  • Intro,para2: (p1.)
    "The provenance of scientific data is a part of the open publishing policy for science data and follows some of the FAIR principles for data sharing."

    I suggest to substitute this sentence with:
    "Provenance of scientific data is one components of the FAIR principles: "(4.3) Published data should refer to their sources with rich enough metatdata and provenance to enable proper citation". (Force11, FAIR Principles)
    This provenance model goes beyond the FAIR principles, its intent is to make the generation of astronomical data accessible. "

    => This sentence is confusing and slightly misleading: there is no "open publishing policy", because policies require a body which gives this policy to itself and
    implements some enforcement measures. And why not pointing to the direct reference in FAIR ? It's good we have some.
  • "In astronomy, such entities are generally datasets composed of VOTables, FITS files, database tables or files containing values (spectra, light curves), any value, logs, documents, as well as physical objects such as instruments, detectors or photographic plates."

    I suggest to clarify:
    "In astronomy, such entities are generally datasets composed of VOTables, FITS files, database tables or files containing values (spectra, light curves), any value, logs, documents, as well as descriptions of physical objects such as instruments, detectors or photographic plates, or information about software."
    => (see paragraph above: 'information about...)
    a) not the machinery itself, but the description can be included as provenance information
    b) I think that software (even though it's not explicitly addressed) also could have provenance information
  • "General Remarks:" (p8)
    "Another important usage of provenance information is to assess the pertinence of a product for scientific objectives, which can be facilitated through the selection of the relevant provenance information attached to an entity that is delivered to a science user."
    I suggest:
    "Provenance information delivers additional information about the scientific data set to enable the scientist to evaluate its relevance for his work. "
    => This is hard to understand,if one substitutes "pertinence" with
    "relevance" (because they are synonyms) you get a kind of tautology
    (relevance of a product => select relevant provenance information)
  • Best Practices: (p.9)
    "The following additional points are recommended when managing provenance information within the VO context:"
    should be
    "The following additional points are recommended when providing provenance information within the VO context:"
    => since all following statements are clearly for providers of provenance infomation
  • 13. Role ... (p. 10)
    "The IVOA Provenance Data Model is structuring and adding metadata to
    trace the original process followed during the data production for providing astronomical data. "
    should read:
    "The IVOA Provenance Data Model is structuring and adding metadata to
    trace the processes of the data production for providing astronomical data."
    =>It's neither reasonable nor required to restrict the provenance to 'original processes'.
-- LaurentMichel - 2019-08-23

Comments from Markus Demleitner

Revisiting the two reviews I've written on this before, let me say that despite the points below, this version of the document is much improved. Thank you for that. Some concerns remain, however. In the list below, I've marked some I consider show stoppers with (!). Everything else is somehwere on the spectrum between woulnd't-it-be-nice and mark-my-words.

In my review of a previous version 2017-09, I made the point that the document has no guidance for how to replace the plethora of FITS keywords with a with a ProvDM-compatible representation. I still think having a section in the introduction or an Appendix on this would make the document much easier to digest to the wider community. And if you adopt VOTable types (please! please!), you can scratch current Appendix C, so the document wouldn't even get longer.

In my review of the previous PR 2018-10, I wrote: "I'd strongly vote for having the core model put into REC frist and only then going for the extended model" on grounds that it's difficult enough to get the core right. While I'd still vote for this if it were an option -- in particular because once we have interoperable clients that consume this kind of thing, we're far less likely to waste a lot of work on things clients won't or can't look at --, I'll grudgingly shut up on this and trust that people have made sure there's no way to have a VO-useful model without the extended part.

(1) Personally, I'm not a big fan of the List of Figures and the List of Tables. If you're not terribly attached of them, consider dropping them. One less page, 2% less scary spec.

(2) ivoatex uses non-French spacing by default, which means that after a full stop there is additional white space. The result is that if you have a period character that is not a sentence end (e.g., "i.e." or "e.g."), you should do something. The preferable way is to have a comma after abbreviations like "i.e.", which is what style guides still tell you to have. Where no comma is appropriate, use a tilda (as in "M.~Demleitner"). Also typographically, there are a few naked double quotes (") left in the text, and it would be nice if they could be replaced by typographical quotes (e.g., Use Case C: "well", "unbounded" in Table 14). There's also a typo in 2.5.3: "GenerationDescription classes[,] that are meant to store" -- the comma doesn't belong there.

(3) In use case (B), there's "Find out who was on shift for data taking for a given dataset", which differs in style from the other bullet points, which are questions. Why not write "Who was on shift while the data was taken?"?

(4) In use case (C) you write "Is the dataset produced or published by a person/organisation I can trust?" -- that's a bit marginal, and I'd prefer if it was withdrawn. If you take that use case seriously, you'll have to derive the requirement that the provenance itself becomes trustworthy, which then entails signatures and a public key infrastructure, which is something we certainly don't want to go into here. So, if you're not too attached to this particular thing, I'd prefer if it went away.

(5) in use case (D), third bullet point, there's "coding language version", which I think at least lacks commas but would certainly profit from a tiny bit more verbosity.

(6) in "General Remarks", you talk about it being "possible to enable the reproducibility", which opens a can of worms ("workflows") that I'd rather keep out of Provenance. Again, if you're not terribly attached to that passage, I'd rather see it removed.

(7) I'm a bit confused by the distinction between "Model requirements" and "Best practices", in particular because quite a few of the "best practices" are actually required if you want to cover your use cases. Why did you create these two classes? And what's the relationship of the "best practices" to the modelling? It seems to me that something like "an entity should be linked to the activity that generated it" could simply be written as an actual requirement like "The model must link entities with the activities that generated it". That would, at least to me, indicate that a constraint on the model is being derived.

(8) In sect. 1.4, the transition from "...was slowing down." to "The IVOA Data Model Working Group first gathered various..." is a bit sudden. Perhaps something like "To regain momentum" or so would improve the narrative here?

(9) For the record, I still think the model's implementability would greatly profit from dropping the loop relations (wasDerivedFrom and wasInformedBy) -- they induce a combinatorial explosion of possible vertices to inspect when following a provenance chain (in an 11-entity provenance chain, you have to look at 21 vertices without them, and about 1045 ones with them). To me, that's a bad deal for saving on possibly empty Activities (or Entities). But well, I guess I can only hope I won't have to deal with this. Oh, and I mention in passing that when drop the loop relations, you also get to drop WasAttributedTo. How good can a deal get?

(10) I haven't been able to work out what "optional" means in this specification, and I'd suggest explaining it somewhere in the introduction. Does it mean a client can ignore specifications in optional elements and still be compliant? If it means something else, what is it? And what about writers? Do writers have to write all "non-optional" elements?

(11) In 2.5.1, you write "An activity is then a concrete case (instance) with a given start and stop time, and it refers to a description for further information." -- given that startTime, stopTime and description are optional as per Table 2, that is a misleading statement (and of course "description" is called "comment" in Activity). Can't you just write "ActivityDescription in this sense is a template defining the metadata of a set of slots filled using an AcitivityConfiguration to build a concrete Activity"? [disclaimer: I may not have quite gotten the plan straight on this first reading]

(12) If there is some systematics behind the apparent inconsistency that human-readable annotation is in the "description" attribute in ActivityDescription but in the "comment" attribute in Activity? If so, it would help adopters if it were explained.

(13) I can't say I like "doculink" too much. Is there a strong reason not to use the term "reference URL" that's well established for what I take this to be?

(14) Table 13 and 14: There's multiplicity there, but it's not really clear what should be there if it were, say, "zero or one", or "exactly two", or really anything else than an unbounded number of entities. The text on p. 24 also suggests to write multiplicity=*, which isn't mentioned at all in the tables. (!)

(15) On p. 24, there's the sentence "For example: when the input bias files..." that than peters off without saying what happens then. I can marginally see that with more effort a reader might be able to figure out the "then" part, but I guess it would be a nice service to say "the EntityDescription referencing the UsageDescription would declare..." (or whatever; I admit I've not tried very hard to figure it out myself).

(16) In table 18, I'm deeply unhappy to see that you're requesting a VO-DML type to describe the data type. In both Registry and DAL (TAP), we've regretted trying shortcuts from VOTable types -- most of the values we're talking about are (at least optionally) going to end up in VOTables, so generating code in all likelihood already knows about VOTable types. Nobody will have thought about VO-DML types. Also, the VOTable is rich and implies serialisation -- you really don't want to go into all the intricacies of timestamps and polygons and all that again. So, please, please use datatype, arraysize and xtype as in VOTable here.

I notice in A1 that the move to VO-DML types apparently was a change made towards this PR. Why was it taken? [I guess this is a good example why it pays to have public discussions on post-WD changes].

(17) In table 18, you say the possible values are "comma separated" -- but what happens if a value contains a comma? You might get by referencing the CSV standard that has escaping rules, but really: my advice is to just get rid of min, max, and options -- I don't see overwhelming use cases, and their semantics becomes really hard once you start digging (e.g., arrays: min, max of all components? by component? in some metric? cf. also the discussions on MAX interpretation in Geometries with SODA). (!)

(18) I can marginally see why you might want a ConfigFile class, though I'd much prefer if a configuration file were simply modeled as another input (i.e., Entity); I don't see what would be lost, and I can easily imagine quite a few cases when the line between a configuration file and something you would probably call an Entity is really blurred (e.g., a catalogue of sources).

What I really object to, tough, is ConfigFileDescription -- this is nowhere near specified well enough to enable any sort of interoperable use. Also, the statement that ConfigFiles contain a "list of (key,value) pairs" is either meaningless (when you let value be just about anything and allow for arbitrarily complex keys) or too restrictive (when key is unique and value atomic). There's no way to write a client that will reliably understand any of this. In consequence, if a value should be available to a provenance reader, it needs to go into the provenance literally anyway. So, you don't lose anything you can do right now if you drop ConfigFileDescription, but you save pages and implementor confusion ("What the heck should I do with an ConfigFile that is application/x-votable+xml?").

If you absolutely have to have ConfigFile, then at least say it's opaque (and essentially just a special kind of entity), and drop the language on keys and values, as well as ConfigFileDescription. Side benefit: the uglyness of requiring name in ConfigFileDescription to match name in ConfigFile (which I take exception with, too) can go.

-- MarkusDemleitner - 2019-09-02

Comments from TCG Members

Early Comments from the Semantics WG

(1) The Provenance DM currently comes with the vocabulary (though that is not stated in the spec as it is). This is nice and certainly the way to go, but the vocabulary itself is severely lacking at this point. My main concern is that the various terms are not really well specified, and some of them quite obviously overlap (creator vs. author, provider vs. publisher vs. curator). Since the vocabulary would become normative (meaning: no terms can be removed) when ProvDM becomes REC, this is bad enough that Semantics can't approve this as is.

Ideally, there should be a use case for each term. Which essentially means: Only include terms people have actually used in their existing descriptions, and then extend the vocabulary as actual provenance descriptions require (this is going to be a reasonably easy and fast process; see for the current draft).

If that's impossible for lack of existing descriptions, my initial list would be to have the terms:

  • creator -- a person who significantly contributed to the execution of an activity or the creation of an entity, such as an article's author, the operator of a pipeline that created the entity, or a researcher who selected and combined flatfields into a master flat.
  • publisher -- a person or institution making an entity available. For entities available on the web, this would be the server operator.
  • funder -- an institution who provided financial means for executing an activity or creating the entity.
  • contact -- a person or institution that can respond to questions regarding an entity or an activity.
This happens to be essentially what Dublin Core says; it's lacking contributor (which at least in Registry was never properly defined and ends up being useless in the current Registry; see below for how to make it useful), and has, in addition, funder (which is a resigned bow towards the high priests of metrics).

If you absolutely have to have a more comprehensive list of roles, consider adopting contributorType from DataCite ( Note, however, that from the Registry experience I'm strongly advising against. We've had a large vocabulary of content levels, for instance, with the result that annotation was wildly inconsistent the the field essentially unusable. Only the restriction to three possible terms made the thing operationally valuable (as, e.g., in Less is more in vocabularies; the more terms you have, the less clear it is what clients would want to look for, and the more difficult it is for annotators to do a good and reproducable job.

Further, there's the link between spec and vocabulary; at this point there's Table 9 in 2.4.4; if you want to use a vocabulary here (which I'd highly recommend), I'd say 2.4.4 should be something like: "The value of the role attribute SHOULD be chosen from the vocabulary; the terms defined at the time of writing are given in Table 9, but the vocabulary can and will be extended with time." Similar language would apply when you adopt vocabularies for other attributes (which, again, I'd highly recommend).

(2) I suspect that it would help a lot to have agreed-upon labels for roles as per 2.3.3 (e.g., letting a machine work out which inputs came from a the observation and which ones were (possibly common) calibration data. This would suggest using one or more vocabularies here. What if you suggested, for starters, to use terms from vocabulary (which have things like dark frame and such) if pertinent? This wouldn't keep people from using free text labels if that's not good enough, and later versions could add more vocabularies, but it would set clients on a right track as to using these roles.

(3) ActivityDescription/@type looks like another good candidate for offloading into a vocabulary. Let me know and I'll translate Table 11 to one (where I wonder whether Reduction, Calibration, and Reconstruction should be child terms of Analysis). One side benefit might be that you can do without ActivityDescription/subtype because the vocabulary gives you a hierarchy for free. And then you can have a controlled vocabulary there, as well, without having another free-text field.

(4) XDescription/@type seems like yet another case where using a vocabulary (i.e., basically translating Table 15) would help the general level of interoperability. Just browsing the terms, however, I'd say you should again be using (and extending as necessary) datalink/core here. The overlap between the lists of terms is rather compelling, and having two different term lists so close to each other would quite certainly confuse our users. Full disclosure: I also believe Datalink would profit if maintenance efforts on Provenance were to improve datalink/core.

-- MarkusDemleitner (with input from Carlo Maria Zwölf) - 2019-09-02

Topic attachments
I Attachment Action Size Date Who Comment
JPEGjpg ImageProvHiPS.jpg manage 92.2 K 2019-08-20 - 14:19 FrancoisBonnarel HST HiPS and tiles provenance metadata
PNGpng OntologyProvenance2019-07-22.png manage 124.6 K 2019-07-22 - 20:07 MireilleLouys list of owl objects in the Prov-test ontology M.Louys
PNGpng OwlOntologyPanel1.png manage 297.9 K 2019-07-24 - 16:22 MireilleLouys list of owl objects, properties and predicates in the Prov-test ontology M.Louys
PDFpdf ProvQuerytest-3store.pdf manage 45.1 K 2019-07-22 - 19:57 MireilleLouys List of example queries for CDS Triplestore implementation. M.Louys
PNGpng VisuProvenance.png manage 443.8 K 2019-08-20 - 14:41 FrancoisBonnarel full provenance chain HiPS HST 1
PNGpng VisuProvenance1.png manage 622.9 K 2019-08-20 - 14:41 FrancoisBonnarel full provenance chain HiPS HST 2
PNGpng VisuProvenance2.png manage 560.9 K 2019-08-20 - 14:42 FrancoisBonnarel full provenance chain HiPS HST 3
Topic revision: r16 - 2019-09-03 - LaurentMichel
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