Joined Up Philanthropy data standards: seeking simplicity, and depth

[Summary: technical notes on work in progress for the Open Philanthropy data standard]

I’m currently working on sketching out a alpha version of a data standard for the Open Philanthropy project(soon to be 360giving). Based on work Pete Bass has done analysing the supply of data from trusts and foundations, a workshop on demand for the data, and a lot of time spent looking at existing standards at the content layer (eGrant/hGrantIATISchema.orgGML etc) and deeper technical layers (CSV, SDFXMLRDF,JSONJSON-Schema and JSON-LD), I’m getting closer to having a draft proposal. But – ahead of that – and spurred on by discussions at the Berkman Center this afternoon about the role of blogging in helping in the idea-formation process, here’s a rough outline of where it might be heading. (What follows is ‘thinking aloud’ from my work in progress, and does not represent any set views of the Open Philanthropy project)

Building Blocks: Core data plus

Joined Up Data Components

There are lots of things that different people might want to know about philanthropic giving, from where money is going, to detailed information on the location of grant beneficiaries, information on the grant-making process, and results information. However, few trusts and foundations have all this information to hand, and very few are likely to have it in a single system such that creating an single open data file covering all these different areas of the funding process would be an easy task. And if presented with a massive spreadsheet with 100s of columns to fill in, many potential data publishers are liable to be put off by the complexity. We need a simple starting point for new publishers of data, and a way for those who want to say more about their giving to share deeper and more detailed information.

The approach to that should be a modular, rather than monolithic standard: based on common building blocks. Indeed, in line with the Joined Up Data efforts initiated by Development Initiatives, many of these building blocks may be common across different data standards.

In the Open Philanthropy case, we’ve sketched out seven broad building blocks, in addition to the core “who, what and how much” data that is needed for each of the ‘funding activities’ that are the heart of an open philanthropy standard. These are:

  • Organisations – names, addresses and other details of the organisations funding, receiving funds and partnering in a project
  • Process – information about the events which take place during the lifetime of a funding activity
  • Locations – information about the geography of a funded activity – including the location of the organisations involved, and the location of beneficiaries
  • Transactions – information about pledges and transfers of funding from one party to another
  • Results – information about the aims and targets of the activity, and whether they have been met
  • Classifications – categorisations of different kinds that are applied to the funded activity (e.g. the subject area), or to the organisations involved (e.g. audited accounts?)
  • Documents – links to associated documents, and more in-depth descriptions of the activity

Some of these may provide more in-depth information about some core field (e.g. ‘Total grant amount’ might be part of the core data, but individual yearly breakdowns could be expressed within the transactions building block), whilst others provide information that is not contained in the core information at all (results or documents for example).

An ontological approach: flat > structured > linked

One of the biggest challenges with sketching out a possible standard data format for open philanthropy is in balancing the technical needs of a number of different groups:

  • Publishers of the data need it to be as simple as possible to share their information. Publishing open philanthropy must be simple, with a minimum of technical skills and resources required. In practice, that means flat, spreadsheet-like data structures.
  • Analysts like flat spreadsheet-style data too – but often want to be able to cut it in different ways. Standards like IATI are based on richly structured XML data, nested a number of levels deep, which can make flattening the data for analysts to use it very challenging.
  • Coders prefer structured data. In most cases for web applications that means JSON. Whilst someexpressive path languages for JSON are emerging, ideally a JSON structure should make it easy for a coder to simply drill-down in the tree to find what they want, so being able to look foractivity.organisations.fundingOrganisation[0] is better than having to iterate through all theactivity.organisation nodes to find the one which has “type”:”fundingOrganisation”.
  • Data integrators want to read data into their own preferred database structures, from noSQL to relational databases. Those wanting to integrate heterogeneous data sources from different ‘Joined Up Data’ standards might also benefit from Linked Data approaches, and graph-based data using cross-mapped ontologies.

It’s pretty hard to see how a single format for representing data can meet the needs of all these different parties: if we go with a flat structure it might be easier for beginners to publish, but the standard won’t be very expressive, and will be limited to use in a small niche. If we go with richer data structures, the barriers to entry for newcomers will be too high. Standards like IATI have faced challenges through the choice of an expressive XML structure which, whilst able to capture much of the complexity of information about aid flows, is both tricky for beginners, and programatically awkward to parse for developers. There are a lot of pitfalls an effective, and extensible, open philanthropy data standard will have to avoid.

In considering ways to meet the needs of these different groups, the approach I’ve been exploring so far is to start from a detailed, ontology based approach, and then to work backwards to see how this could be used to generate JSON and CSV templates (and as JSON-LD context), allowing transformation between CSV, JSON and Linked Data based only on rules taken from the ontology.

In practice that means I’ve started sketching out an ontology using Protege in which there are top entities for ‘Activity’, ‘Organisation’, ‘Location’, ‘Transaction’, ‘Documents’ and so-on (each of the building blocks above), and more specific sub-classed entities like ‘fundedActivity’, ‘beneficiaryOrganisation’, ‘fundingOrganisation’, ‘beneficiaryLocation’ and so-on. Activities, Organisations, Locations etc. can all have many different data properties, and there are then a range of different object properties to relate ‘fundedActivities’ to other kinds of entity (e.g. a fundedActivity can have a fundingOrganisation and so-on). If this all looks very rough right now, that’s because it is. I’ve only built out a couple of bits in working towards a proof-of-concept (not quite there yet): but from what I’ve explored so far it looks like building a detailed ontology should also allow mappings to other vocabularies to be easily managed directly in the main authoritative definition of the standard: and should mean when converted into Linked Data heterogenous data using the same or cross-mapped building blocks can be queried together. Now – from what I’ve seen ontologies can tend to get out of hand pretty quickly – so as a rule I’m trying to keep things as flat as possible: ideally just relationships between Activities and the other entities, and then data properties.

What I’ve then been looking at is how that ontology could be programatically transformed:

  • (a) Into a JSON data structure (and JSON-LD Context)
  • (b) Into a set of flat tables (possibly described with Simple Data Format if there are tools for which that is useful)

And so that using the ontology, it should be possible to take a set of flat tables and turn them into structure JSON and, via JSON-LD, into Linked Data. If the translation to CSV takes place using the labels of ontology entities and properties rather than their IDs as column names, then localisation of spreadsheets should also be in reach.

Rough work in progress... worked example coming soon

Rough work in progress. From ontology to JSON structure (and then onwards to flat CSV model). Full worked example coming soon…

I hope to have a more detailed worked example of this to post shortly, or, indeed, a post detailing the dead-ends I came to when working this through further. But – if you happen to read this in the next few weeks, before that occurs – and have any ideas, experience or thoughts on this approach – I would be really keen to hear your ideas. I have been looking for any examples of this being done already – and have not come across anything: but that’s almost certainly because I’m looking in the wrong places. Feel free to drop in a comment below, or tweet @timdavies with your thoughts.

3 thoughts on “Joined Up Philanthropy data standards: seeking simplicity, and depth

  1. Pingback: Joined Up Philanthropy: seeking simplicity, and depth – Tim’s Blog | Public Sector Blogs

  2. Michael Lenczner

    Hi Tim,

    We haven’t met, but through conversations with one of your pals, Michael Roberts, our company has been looking into the question of a creating reporting standard for domestic grantmaking over the past several months. We were planning on working on it during the upcoming IATI code-a-thon here in Montreal, so since you’ll be here, hopefully we’ll have the chance to sit down with you and talk about it.
    We’ve been collecting funder data from foundations, corporations and varying levels of governments for 4 years, so we have good knowledge about current practices. Mainly our thinking is about promoting adoption and use-cases, so including corporations and government funders is key for us.

    Looking forward to discussing it with you.

  3. Pingback: Link roundup

Leave a Reply

Your email address will not be published. Required fields are marked *