Search strategy

This page records every search, as PRISMA-S requires (Rethlefsen et al. 2021). Each query gets one row in the table, and each database gets one entry under search strings. The table counts are the records each database returned before deduplication. 01-deduplication writes the identification counts for the flow diagram from the exports in data/raw/search_exports/ once the exports are in.

Queries

Four queries run in each database for 2015 to 2025. All four build on Chawinga and Zinn (2019). Q1 takes their core term, “research data sharing”, and serves as the base RDS query. Q2 to Q4 extend that term with the vocabulary the field uses around it. Each pairs one key term with “research data” or “data sharing” using AND, so a record must name both the concept and the data practice in its title or abstract. The date range brackets the 2019 review and picks up where it left off. The queries use no health terms. All terms are searched in titles and abstracts.

Databases and searches

Records retrieved per query and database, 2015 to 2025, before deduplication. Percentages give each query’s share of the database’s summed hits.
Query PubMed N (% of col.) Scopus N (% of col.) WoS N (% of col.)
Q1 research data sharing 64 (6.1%) 135 (7.8%) 109 (7.7%)
Q2 open data 352 (33.6%) 644 (37.4%) 564 (39.7%)
Q3 data governance 219 (20.9%) 348 (20.2%) 255 (18.0%)
Q4 open science 414 (39.5%) 596 (34.6%) 492 (34.6%)
Column total 1,049 1,723 1,420

Platforms and search dates are to be added.

Seed

The base RDS query (Q1) alone serves as the seed for the screening ranking. It returned 308 records against 4,192 for the four queries combined (7.3%), modestly above the conventional 5%. A seed derived from a fixed keyword is reproducible, and its size stays near where a seed sample should land.

Candidate terms

Candidate terms from earlier drafts of the query were checked against the seed. Table 1 shows how often each term appears in the records the base RDS query returned, which guided the four queries above.

Table 1: Candidate terms ranked by records matched across the three databases. Ties share a rank (=).
Rank Term Total PubMed WoS Scopus
1 policies 88 16 31 41
2 challenge* 73 21 23 29
3 policy 69 14 22 33
4 repositor* 68 15 20 33
5 ethic* 62 19 20 23
6 privacy 52 15 15 22
7 infrastructure 46 9 14 23
8 barrier* 45 10 16 19
9 perception* 42 9 15 18
10 open science 41 5 15 21
11 motivat* 37 5 15 17
12 interoperab* 36 10 11 15
13 “open data” 34 7 12 15
14 open access 32 3 9 20
15 consent 31 9 10 12
16 open research 27 6 9 12
17 licens* 25 3 5 17
18 attitude* 23 3 9 11
19 incentive* 21 3 6 12
20 mandate* 17 4 6 7
21 willingness 15 3 5 7
22 obstacle* 12 4 3 5
23= “data reuse” 10 0 4 6
23= stewardship 10 3 3 4
25 “data management plan*” 9 2 3 4
26= “data curation” 8 2 3 3
26= facilitator* 8 2 3 3
28 “data re-use” 5 1 1 3
29 enabler* 4 1 1 2
30 reluctan* 3 1 1 1
31 “digital curation” 0 0 0 0

Search strings

The generic queries are the database-neutral core of the strings below. Each database adds its own field tags, date limits, and document type filters. All terms are searched in titles and abstracts.

Generic queries

-- Q1: research data sharing (base RDS query)
"research data sharing" OR "research data-sharing"

-- Q2: open data
"open data"
AND ("research data" OR "data sharing")

-- Q3: data governance
"data governance"
AND ("research data" OR "data sharing")

-- Q4: open science
"open science"
AND ("research data" OR "data sharing")

Scopus (1,723 records)

-- Q1 (135 records)
TITLE-ABS ( "research data sharing" OR "research data-sharing" )
AND PUBYEAR > 2014 AND PUBYEAR < 2026
AND DOCTYPE ( ar OR re )

-- Q2 (644 records)
TITLE-ABS ( "open data" )
AND TITLE-ABS ( "research data" OR "data sharing" )
AND PUBYEAR > 2014 AND PUBYEAR < 2026
AND DOCTYPE ( ar OR re )

-- Q3 (348 records)
TITLE-ABS ( "data governance" )
AND TITLE-ABS ( "research data" OR "data sharing" )
AND PUBYEAR > 2014 AND PUBYEAR < 2026
AND DOCTYPE ( ar OR re )

-- Q4 (596 records)
TITLE-ABS ( "open science" )
AND TITLE-ABS ( "research data" OR "data sharing" )
AND PUBYEAR > 2014 AND PUBYEAR < 2026
AND DOCTYPE ( ar OR re )

Web of Science (1,420 records)

-- Q1 (109 records)
(
  TI=( "research data sharing" OR "research data-sharing" )
  OR AB=( "research data sharing" OR "research data-sharing" )
)
AND PY=(2015-2025)
AND DT=(Article OR Review)

-- Q2 (564 records)
(
  TI=( "open data" )
  OR AB=( "open data" )
)
AND
(
  TI=( "research data" OR "data sharing" )
  OR AB=( "research data" OR "data sharing" )
)
AND PY=(2015-2025)
AND DT=(Article OR Review)

-- Q3 (255 records)
(
  TI=( "data governance" )
  OR AB=( "data governance" )
)
AND
(
  TI=( "research data" OR "data sharing" )
  OR AB=( "research data" OR "data sharing" )
)
AND PY=(2015-2025)
AND DT=(Article OR Review)

-- Q4 (492 records)
(
  TI=( "open science" )
  OR AB=( "open science" )
)
AND
(
  TI=( "research data" OR "data sharing" )
  OR AB=( "research data" OR "data sharing" )
)
AND PY=(2015-2025)
AND DT=(Article OR Review)

PubMed (1,049 records)

-- Q1 (64 records)
( "research data sharing"[tiab] OR "research data-sharing"[tiab] )
AND 2015:2025[dp]
AND "Journal Article"[pt]

-- Q2 (352 records)
"open data"[tiab]
AND ( "research data"[tiab] OR "data sharing"[tiab] )
AND 2015:2025[dp]
AND "Journal Article"[pt]

-- Q3 (219 records)
"data governance"[tiab]
AND ( "research data"[tiab] OR "data sharing"[tiab] )
AND 2015:2025[dp]
AND "Journal Article"[pt]

-- Q4 (414 records)
"open science"[tiab]
AND ( "research data"[tiab] OR "data sharing"[tiab] )
AND 2015:2025[dp]
AND "Journal Article"[pt]

Other sources

Describe each supplementary method and how records were identified: citation searching of the included sources, registries, websites, and contacts. Name export files for these sources with the prefix other-.

Known-item test

The final strings are tested against these sources. The first two groups are in scope, so the query should retrieve them. The last two groups are comparators or adjacent work, and retrieving them is not required.

Health research data sharing (tier 1)

  • Rowhani-Farid, Allen, and Barnett (2017). What incentives increase data sharing in health and medical research? A systematic review. Research Integrity and Peer Review 2: 4. doi:10.1186/s41073-017-0028-9
  • Devriendt, Shabani, and Borry (2021). Data sharing in biomedical sciences: a systematic review of incentives. Biopreservation and Biobanking 19(3): 219-227. doi:10.1089/bio.2020.0037
  • Kalkman et al. (2019). Responsible data sharing in international health research: a systematic review of principles and norms. BMC Medical Ethics 20: 21. doi:10.1186/s12910-019-0359-9
  • Hamilton et al. (2023). Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data. BMJ 382: e075767. doi:10.1136/bmj-2023-075767
  • Hutchings et al. (2020). A systematic literature review of researchers’ and healthcare professionals’ attitudes towards the secondary use and sharing of health administrative and clinical trial data. Systematic Reviews 9: 240. doi:10.1186/s13643-020-01485-5
  • Saulnier et al. (2019). Benefits and barriers in the design of harmonized access agreements for international data sharing. Scientific Data 6: 297. doi:10.1038/s41597-019-0310-4
  • Gudi et al. (2022). Regulatory frameworks for clinical trial data sharing: scoping review. Journal of Medical Internet Research 24(5): e33591. doi:10.2196/33591
  • Swales, Gooden, and Thaldar (2024). The anatomy of a data transfer agreement for health research. Frontiers in Pharmacology 15: 1332700. doi:10.3389/fphar.2024.1332700
  • Andanda and Mlotshwa (2024). Streamlining the ethical-legal governance of cross-border health data sharing during global health emergencies. Research Ethics 20: 812-834. doi:10.1177/17470161241261907
  • Inau et al. (2023). Initiatives, concepts, and implementation practices of the findable, accessible, interoperable, and reusable data principles in health data stewardship: scoping review. Journal of Medical Internet Research 25: e45013. doi:10.2196/45013
  • Scheibner et al. (2023). Does legislation impede data sharing in Australia across institutions and jurisdictions? A scoping review. Journal of Medical Systems 47(1): 116. doi:10.1007/s10916-023-02009-z

Cross-disciplinary reviews (tier 2)

  • Fecher, Friesike, and Hebing (2015). What drives academic data sharing? PLoS ONE 10(2): e0118053. doi:10.1371/journal.pone.0118053
  • Zuiderwijk, Shinde, and Jeng (2020). What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption. PLoS ONE 15(9): e0239283. doi:10.1371/journal.pone.0239283
  • Perrier, Blondal, and MacDonald (2020). The views, perspectives, and experiences of academic researchers with data sharing and reuse: a meta-synthesis. PLoS ONE 15(2): e0229182. doi:10.1371/journal.pone.0229182
  • Chawinga and Zinn (2019). Global perspectives of research data sharing: a systematic literature review. Library & Information Science Research 41(2): 109-122. doi:10.1016/j.lisr.2019.04.004
  • Thøgersen and Borlund (2022). Researcher attitudes toward data sharing in public data repositories: a meta-evaluation of studies on researcher data sharing. Journal of Documentation 78(7). doi:10.1108/JD-01-2021-0015

Personal health data and data subjects (adjacent)

  • Alam et al. (2024). Stakeholders’ perceptions of personal health data sharing: a scoping review. PLOS Digital Health 3(11): e0000652. doi:10.1371/journal.pdig.0000652
  • Baines et al. (2024). Patient and public willingness to share personal health data for third-party or secondary uses: systematic review. Journal of Medical Internet Research 26: e50421. doi:10.2196/50421
  • Cascini et al. (2024). Health data sharing attitudes towards primary and secondary use of data: a systematic review. eClinicalMedicine 71: 102551. doi:10.1016/j.eclinm.2024.102551

Public health data (comparator)

  • van Panhuis et al. (2014). A systematic review of barriers to data sharing in public health. BMC Public Health 14: 1144. doi:10.1186/1471-2458-14-1144
  • Yehudi et al. (2025). COVID-19: an exploration of consecutive systemic barriers to pathogen-related data sharing during a pandemic. Data & Policy 7: e4. doi:10.1017/dap.2024.79

Export file names

Name each export <database>_<YYYY-MM-DD>[_<query>].<ext>, for example scopus_2026-11-02_q1.ris. The query label is q1, q2, q3, or q4. The date is the search date. Supported formats are RIS and CSV. See the README in data/raw/search_exports/.

References

Chawinga, Winner Dominic, and Sandy Zinn. 2019. “Global Perspectives of Research Data Sharing: A Systematic Literature Review.” Library & Information Science Research 41 (2): 109–22. https://doi.org/10.1016/j.lisr.2019.04.004.
Rethlefsen, Melissa L., Shona Kirtley, Siw Waffenschmidt, et al. 2021. “PRISMA-S: An Extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews.” Systematic Reviews 10: 39. https://doi.org/10.1186/s13643-020-01542-z.