{"id":3849,"date":"2026-02-04T09:09:24","date_gmt":"2026-02-04T09:09:24","guid":{"rendered":"https:\/\/blogs.imperial.ac.uk\/openaccess\/?p=3849"},"modified":"2026-02-04T09:09:24","modified_gmt":"2026-02-04T09:09:24","slug":"2025-plos-open-science-indicators-dataset-analysis","status":"publish","type":"post","link":"https:\/\/blogs.imperial.ac.uk\/openaccess\/2026\/02\/04\/2025-plos-open-science-indicators-dataset-analysis\/","title":{"rendered":"Can we measure research in a more meaningful way? A quick look at the 2025 PLOS Open Science Indicators dataset"},"content":{"rendered":"
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Why measuring open research matters<\/h2>\n

Open access\u00a0publishing\u00a0is sometimes mistakenly thought of as the be-all-and-end-all\u00a0of\u00a0open research (or open science).\u00a0But\u00a0while\u00a0open\u00a0access\u00a0is\u00a0important,\u00a0it is not enough to capture the full\u00a0breadth\u00a0of research\u00a0contributions,\u00a0outputs\u00a0and impacts.\u00a0There are other indicators\u00a0that can tell us more about\u00a0the\u00a0openness and quality of\u00a0research\u00a0than journal-related metrics do.<\/span>\u00a0<\/span><\/p>\n

The research community is adopting more open research practices.\u00a0After all,\u00a0research is about more than just publishing the\u00a0final\u00a0outcome.\u00a0It involves the processes that produce it.\u00a0Measuring the\u00a0open access\u00a0level of publications\u00a0gives\u00a0a\u00a0narrow\u00a0insight\u00a0about\u00a0research. It\u00a0should ideally\u00a0cover other pillars\u00a0\u2013 such\u00a0as sharing data and code, embracing preprints, and adopting transparent research protocols\u00a0\u2013\u00a0for reliable and\u00a0high-quality research.<\/span><\/p>\n

Measuring\u00a0the level of\u00a0open research adoption\u00a0could\u00a0provide\u00a0valuable insight into how far the community has progressed on the path toward openness.\u00a0It would also allow us to advocate what practices researchers can implement\u00a0to make their work\u00a0more reliable.\u00a0Perhaps, most\u00a0importantly, open research indicators can\u00a0offer a better alternative to\u00a0unfruitful \u2013 even\u00a0unhealthy\u00a0\u2013\u00a0metrics that have\u00a0created a\u00a0research culture\u00a0of perverse incentives,\u00a0like\u00a0<\/span>Journal Impact Factor<\/span><\/a>\u00a0and\u00a0<\/span>h-index<\/span><\/a>.\u00a0Measuring\u00a0open research is not easy\u00a0due to the\u00a0unsystematic\u00a0adoption\u00a0of practices\u00a0and\u00a0their\u00a0application\u00a0on publications, such as not including code used for research or a dataset made available on request.\u00a0Unlike\u00a0open access, these practices are not yet fully mature.\u00a0Researchers may also overlook making their open research activities visible, even when they may already be engaging in them.\u00a0Therefore, understanding and\u00a0demonstrating\u00a0the extent of adoption is important for raising awareness and fostering broader engagement.\u00a0<\/span><\/p>\n

Measuring open research is hard, but PLOS proves it can be done<\/h2>\n

The academic publisher\u00a0PLOS\u00a0has been\u00a0working on\u00a0this challenging task of\u00a0measuring\u00a0open research principles.\u00a0Partnering with\u00a0<\/span>DataSeer<\/span><\/a>\u00a0(an AI-based tool that checks manuscripts for identifiers of open practices),\u00a0they have been releasing\u00a0the comprehensive\u00a0<\/span>PLOS Open Science Indicators<\/span><\/a>\u00a0(OSI)\u00a0dataset\u00a0since late 2022\u00a0containing\u00a0open research indicators, such as\u00a0data availability\/share, code availability\/share, and preprint adoption.<\/span><\/p>\n

The latest version of the OSI dataset has a total of 138,995 PLOS research articles published between 2018 and March 2025\u00a0(see\u00a0<\/span>here<\/span><\/a>\u00a0for their\u00a0methodology).\u00a0The 2025 OSI dataset expands on\u00a0previous\u00a0versions by including two new indicators:\u00a0\u00a0protocol-sharing and\u00a0study\u00a0(pre)registration.\u00a0Previous\u00a0versions only had data availability\/sharing, code availability\/sharing\u00a0and preprint posting.\u00a0In this blog, I\u00a0analyse\u00a0publications of Imperial College London\u00a0within the OSI dataset. It aims to 1) understand\u00a0the level of open research adoption at Imperial\u00a02)\u00a0outline a simple\u00a0methodology\u00a0that other institutions can easily implement for their own analyses.<\/span><\/p>\n

Methods to identify Imperial research outputs in the OSI dataset<\/h2>\n

A simple\u00a0methodology\u00a0of matching research outputs of Imperial College London within the OSI dataset through DOIs is used.\u00a0By using the internal Power BI report\u00a0which is connected to Imperial\u2019s current research information system, \u2018journal article\u2019 type items with a\u00a0DOI published between 2018-2025 were downloaded\u00a0(DOI is\u00a0preferred\u00a0as it allows\u00a0matching outputs\u00a0with a unique identifier).\u00a01,368 out of 94,384 outputs matched the PLOS OSI dataset,\u00a0representing\u00a01.45% of Imperial’s total journal articles\u00a0and accounting for\u00a0nearly 1%\u00a0of the entire PLOS dataset.<\/span>\u00a0<\/span><\/p>\n

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Figure 1: PLOS OSI dataset represents 1% of Imperial publications<\/figcaption><\/figure>\n

Findings: open research practices at Imperial<\/h2>\n

97% of the Imperial articles have generated data. It is not surprising to see the high percentage of data-sharing (nearly 85% of articles that generated data) in Imperial outputs in the OSI dataset because of Imperial\u2019s active data management policies<\/a> and publisher\/funder data availability requirements. Data sharing is nearly 9% higher among Imperial articles compared to the overall PLOS dataset.<\/p>\n

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Figure 2: Data sharing is the most adopted open research practice at Imperial (83% of journal articles published by Imperial authors from 2018 to 2025 included a link to shared data)<\/figcaption><\/figure>\n

Accessing code which is the basis of research data generated within the output is vital for research integrity and reproducibility. Although there has been some improvement, codesharing isn\u2019t as developed as data-sharing.<\/p>\n

Nearly half of Imperial\u2019s publications in the OSI dataset involved code generation.\u00a0 However, among those that did use code, only 56% have shared it, meaning that readers cannot ask the right questions about the reliability of the code underpinning the analysis in almost half the dataset.<\/p>\n

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Figure 3: The practice of sharing code in research outputs is increasing (2025 – incomplete year)<\/figcaption><\/figure>\n

Posting preprints (non-peer-reviewed manuscripts posted to online servers before journal submission) has become a norm in many disciplines. Preprints improve research quality and increase visibility by enabling rapid dissemination and collaborative and timely feedback. It also a valuable indicator of bridge-building between research stages. Measuring how many publications originate from preprints can provide valuable insight into the adoption of open research practices. However, linking a preprint to its final published version can be challenging. The OSI dataset\u2019s methodology promises to overcome this challenge.<\/p>\n

35% of Imperial outputs in the PLOS dataset were associated with a preprint. Except for the incomplete year of 2025, preprint adoption has increased over recent years, jumping from 13% in 2018 to nearly 50% in 2024. The preprint match for Imperial publications is twice the global average in the PLOS set.<\/p>\n

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Figure 4: Preprint match of Imperial publications is higher than the global average<\/figcaption><\/figure>\n

The latest version of the OSI dataset includes two new open research indicators: protocol and study registration. Although these two concepts have been incorporated in research process for some time, publishing and sharing them in outputs is relatively new. Implementing and sharing them is just as important as sharing data, as both are crucial for enhancing transparency and reproducibility, and ultimately for building trustworthiness in research.<\/p>\n

Compared to other open research practices, the adoption of protocol sharing and study registration remains low. Only 10% of Imperial articles in the dataset included an available protocol and study registration, similar to the global average. The results here should be interpreted with caution since protocols are more common in certain fields than others.<\/p>\n

\"\"
Figure 5: Protocol sharing and study registration rank lowest among open research practices<\/figcaption><\/figure>\n

Do open research practices reinforce one another? The answer is yes.<\/h2>\n

Given the comprehensiveness of the PLOS dataset, it would be interesting to look at the relationship between the open research indicators. When considering the outputs that shared data, the percentage of all other open research indicators increases (e.g. code sharing rises from 25% to 32%, preprint match from 35% to 38%). The change becomes even more noticeable when focusing on publications that shared code. Among the Imperial articles that shared code, data-sharing jumped from 83% to 98% and preprint matching increased from 35% to 59%. The same pattern is true for preprints: outputs matching a preprint are significantly superior in adopting other open research indicators, especially data and code sharing.<\/p>\n

\"\"
Figure 6: Outputs linked to preprints show higher adoption of other open research practices<\/figcaption><\/figure>\n

Conclusion with reminding limitations and caveats<\/h2>\n

While the number of publications is significant, it only represents a tiny amount of the publication universe. The dataset includes open access outputs from one publisher that predominantly publishes in certain disciplines. Some fields, for instance, do not produce data as we understand it in the context of science. Also, preprinting may not be common where the journal article is not the main output type in some disciplines. It is also important to highlight that PLOS dataset puts journal articles in the centre and works backwards, meaning that we cannot confidently say whether open research practices were followed from the beginning of the research process or just at the time of publication. Generalising these conclusions to the entire research ecosystem without keeping the nuances in mind would be invalid and would risk creating gamification and perverse incentives in the same way that journal-based metrics have done for years. Despite its caveats, the PLOS Open Science Indicators dataset is a great starting point for measuring research in a more meaningful way, especially in the presence of unhealthy metrics. The analysis of the dataset gives us good insights into where we should focus our open research advocacy.<\/p>\n

This brief analysis shows that while we seem to be in a strong position when it comes to sharing data, there is still room for improvement in other areas, such as sharing code, releasing preprints, making research protocols available, and registering studies before they begin, especially if they are underpinning elements of research. Everyone in this ecosystem, from researchers and institutions to funders and publishers, has a role to play in making this happen.<\/p>\n","protected":false},"excerpt":{"rendered":"

Why measuring open research matters Open access\u00a0publishing\u00a0is sometimes mistakenly thought of as the be-all-and-end-all\u00a0of\u00a0open research (or open science).\u00a0But\u00a0while\u00a0open\u00a0access\u00a0is\u00a0important,\u00a0it is not enough to capture the full\u00a0breadth\u00a0of research\u00a0contributions,\u00a0outputs\u00a0and impacts.\u00a0There are other indicators\u00a0that can tell us more about\u00a0the\u00a0openness and quality of\u00a0research\u00a0than journal-related metrics do.\u00a0 The research community is adopting more open research practices.\u00a0After all,\u00a0research is about more than […]<\/p>\n","protected":false},"author":1396,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3849","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\nCan we measure research in a more meaningful way? 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