Data Sharing

A research topic across the group’s papers.

Data sharing is the practice of making the data behind a publication available in a form that others can find, read and reuse. Funding agencies increasingly require data management plans, but they usually leave the means of sharing to the researcher, and conventional manuscripts convey data mainly as figures and tables that are not machine readable. Data sharing is closely tied to reproducibility and, in the Kitchin group’s practice, to writing papers as executable documents (literate programming).

Data embedded in Supporting Information (2015)

Kitchin describes two complementary ways to share data within the traditional journal workflow (Kitchin 2015). The first attaches data files to the Supporting Information PDF using the standard PDF attachment feature, which any author can do with PDF editors, the LaTeX attachfile package or Word; the group has attached JSON (all DFT geometries and parameters in boes2015a), CSV (curnan2014) and Excel files (hallenbeck2013). The second embeds data as named tables in the org-mode source of the Supporting Information, which is itself attached to the PDF, so the data is documented in place and can be parsed by any scripting language. Very large data sets (about 1.8 GB of DFT calculations for xu2015) are better deposited in a repository that assigns a DOI, such as Zenodo. The paper argues against waiting for standard formats or centralized catalysis databases: authors should share now in fit-for-purpose formats, and domain experts can later curate and convert distributed data. Summary: kitchin2015.

Data sharing beyond atomic positions in Surface Science (2016)

Writing for the Surface Science community, Kitchin argues that the journal’s 2010 policy requiring accessible atomic positions (and the ensuing debate over CIF as a standard) addresses only part of a broader need to share data and methods (Kitchin 2016). Using the JSON file attached to the Supporting Information of a Cu-Pd core-level shift paper (boes2015), he shows that a few lines of Python with the ASE database module can list the stored calculations, write a chosen structure out as a CIF file, and recover the VASP parameters, k-points, cell, positions and total energy needed to rerun it. A change in the ASE database interface broke the originally published code but not the data, which remained readable plain JSON; the paper uses this to argue for plain, documented formats and notes that embedded data in published manuscripts is likely to outlast external repositories, which are still recommended for large data sets (about 1.8 GB on Zenodo for xu2015). Summary: kitchin2016a.

Archiving high-throughput data (2016)

In discussing the outlook for high-throughput methods, Kitchin and Gellman note that both experimental and computational high-throughput work produce datasets too large for standard publications to archive, and point to repositories such as Zenodo (where the group archived a 1.8 GB DFT dataset), Figshare, institutional repositories and the Materials Project (Kitchin 2016). They also argue that the value of such data depends on trustworthy tools for modeling, fitting and visualization, and on training practitioners to use them. Summary: kitchin2016.

Sharing computational catalysis results and methods (2016)

Bligaard et al. argue that the adsorbate structure reported in a paper is far from enough to recreate an electronic structure calculation, and recommend publishing input and output files, explaining how derived quantities (adsorption energies, frequencies, barriers, entropies) were computed, sharing spreadsheets or codes and the inputs to downstream microkinetic models, and using version control for evolving method implementations (Bligaard et al. 2016). They note that data sharing correlates with higher citation rates, cite the JCAP HER/OER database as an example of shared benchmark data, and envision large curated benchmark databases to which smaller groups contribute. Summary: bligaard2016.

Automated data embedding at export time (2017)

Kitchin, Van Gulick and Zilinski show that embedding data in a publication can be automated rather than done by hand (Kitchin et al. 2016). A customized org-mode exporter writes every table to a CSV file and every code block to a source file, attaches them to the PDF (LaTeX attachfile) or links them with data URIs in HTML, and records the list of attached files in a custom PDF Info field (EmbeddedFiles) so that programs can discover them with tools such as pdftk and pdfdetach. The demonstration manuscript carries 16 embedded files, and a steganography example hides a CSV table inside an image to show the approach is general. Written with two university librarians, the paper also stresses that discoverability of embedded data will need coordination among publishers, researchers and search engines. Summary: kitchin2017.

Data and software availability statements at JCTC and JCIM (2026)

The JCTC/JCIM editorial frames data sharing through the FAIR principles and asks authors to deposit data, code and supporting material in machine-readable form in repositories that issue persistent identifiers (DOIs or accession numbers), preferably run by sustainable institutions committed to long-term preservation, and to say where everything is in a Data and Software Availability Statement (Amaro et al. 2026). Where code cannot be released, temporary, anonymity-preserving access for reviewers is suggested. The policy formalizes at the journal level the practices advocated in kitchin2015 and kitchin2016a. Summary: amaro2026.

Journal standards for data resources in JCIM (2026)

The JCIM editorial on Application Notes asks database and benchmark authors to document scope, update strategy, identifiers, provenance, curation and preprocessing, access mechanisms, known biases and recommended evaluation protocols, and it strongly encourages adherence to the FAIR principles with clear licensing (Nunes Alves et al. 2026). It moves the expectations Kitchin argued for in his earlier data-sharing papers into a journal’s formal review criteria. Summary: nunesalves2026.

An AI-ready data ecosystem for catalysis (2026)

A community roadmap for AI in heterogeneous catalysis treats shared data as the foundation of everything else (Xin et al. 2026). It argues that catalysis data must follow the FAIR principles and be tagged with domain ontologies, captured at the source in electronic lab notebooks and laboratory information management systems, and produced by provenance-tracking computational tools (ASE, pymatgen, FireWorks, AiiDA). It names Catalysis-Hub, the Open Catalyst datasets, NOMAD, ioChem-BD, DigCat and CatTestHub as existing building blocks, and stresses that negative and null results and industrial data are still rarely shared.

The paper proposes concrete ways to measure progress: inter-laboratory reproducibility thresholds, metadata completeness, time from measurement to public FAIR release, and the reduction in model error when training on standardized rather than siloed datasets. It also calls for sponsor or publisher requirements to give researchers incentives to share data beyond what is published. Summary: xin2026.

FAIR data and negative results in catalysis campaigns (2026)

TRACE-AI extends FAIR data stewardship to campaign-level traceability, linking dataset releases to model versions, agent decisions and tool use (Xin et al. 2026). It recommends depositing performance, synthesis and characterization data with persistent identifiers in repositories such as NOMAD, Catalysis-Hub, ioChem-BD, the Materials Project and JARVIS, using open formats (CSV, Zarr, JSON with schemas), starting from minimal machine-readable manifests, and sharing informative suboptimal and failed results to counter the positive bias of the literature. Summary: xin2026a.

Papers on this topic