Sustaining Dryad: Reconfiguring relationships and 'thinking like a business' to maintain open data infrastructures

Kathleen Gregory; Dorothea Strecker

Home/Archive/arXiv

arXiv · 2604.27580

Sustaining Dryad: Reconfiguring relationships and 'thinking like a business' to maintain open data infrastructures

Kathleen Gregory·Dorothea Strecker

View original source ↗Read source document ↗Export metadata ↓

Abstract

Open data infrastructures underpin how open research is practiced across communities. While critical, the longevity of such infrastructures is far from certain, as they grapple with challenges related to technologies, dominant ideologies, and continued funding streams. This paper draws on a mixed-methods study of Dryad, a well-known open data infrastructure, to deeply explore one of these challenges: the quest for stable funding. Our analysis shows how Dryad has carefully reconfigured different forms of relationships and revenue models throughout its history to work towards financial sustainability. We identify four types of relationships with customers, collaborators, and competitors that have been critical to Dryad's financial evolution: reinforcing, forging, positioning, and excluding relationships. We argue that while implementing strategic, business thinking is a critical strategy, it is also one which shapes other factors important in sustainability: interpretations of value(s), community, and governance. We conclude by highlighting emerging tensions that provide insight for other open data infrastructures working to become financially sustainable. As a whole, our analysis focuses not just on financial mechanisms for funding open data infrastructures (although those emerge) but on the relationships which enable them.

Explore related subjects

cs.DL

Keep this discovery

BibTeX RIS

Kathleen Gregory, Dorothea Strecker. 2026-08-31. Sustaining Dryad: Reconfiguring relationships and 'thinking like a business' to maintain open data infrastructures. https://arxiv.org/abs/2604.27580

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

cs.DL subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

arXiv2026

A Fairness Audit of the Duckworth-Lewis-Stern Method: Format-Specific and Gender-Differential Bias, with an Interpretable Calibration Layer for Cricket Target Revision

The Duckworth-Lewis-Stern (DLS) method has been the international standard for revising target scores in rain-interrupted limited-overs cricket since 1999. Despite over two decades of operational use, no large-scale empirical audit of its prediction bias has been published. We conduct such an audit on 8,150 international matches (3,095 ODIs, 5,055 T20Is) from Cricsheet, generating 233,550 synthetic interruption scenarios with temporal splits. We document two structured biases. First, DLS prediction error spans a 137-run range across (overs-remaining, wickets-lost) match-state buckets. Second, DLS exhibits a gender-differential bias on ODIs that has not previously been quantified: on the training split, mean over-prediction is +1.51 runs for men but +7.63 runs for women, a gap of +6.13 runs (F = 195.16, p < 10^-43). We benchmark DLS against five modern alternatives: Bi-LSTM, XGBoost, an enriched XGBoost variant, a deep context-aware model, and a stacking ensemble, and propose DLS-Cal, a lightweight interpretable calibration layer (27K parameters) outputting a state-conditioned correction added to DLS. DLS-Cal reduces absolute bias by 31% on ODI and 19% on T20I, and a gender-aware variant reduces women's ODI residual bias from +6.19 to +0.65 runs while leaving men's calibration unchanged. We release code, models, and data.

cs.LG↗

arXiv2026

Are Widely Known Findings Easier to Retract?

Failures of retraction are common in science. Why do they occur? And what determines whether a retraction is successful? We use data from citation records and Altmetrics to test proposed answers to these questions. LaCroix et al. employ network models to argue the social spread of information helps explain failures of retraction. One prediction is that widely known results, surprisingly, should be easier to retract, since their retraction is more relevant. Our results support this conclusion. We find highly cited papers show more significant reductions in citation after retraction and garner more attention to their retractions as they occur.

cs.DL↗

arXiv2026

Filling holes in science draws collective attention, but most higher-order holes remain unexplored

Much scientific discovery involves filling holes between ideas and arguments that unleash techno-scientific advance. Representing knowledge as high-dimensional concept embeddings, we use persistent homology to detect holes of increasing order, from gaps between disconnected ideas to higher-order cavities, and identify the research works that fill them. We find two empirical asymmetries. Researchers who fill anticipated holes are poised to draw collective attention by staging outsized novelty and foresight, indicating that bridging holes anticipates where science will converge, most strongly in empirical fields and least in formal and design fields. Yet as knowledge grows, higher-order holes explode while the fraction science fills collapses, leaving most higher-order combinations unexplored. These results call for a richer science of holes, and mark a frontier where contemporary AI might help fill the high-dimensional gaps human science opens.

cs.CY↗