Laura Boeschoten

571 total citations · 1 hit paper
21 papers, 216 citations indexed

About

Laura Boeschoten is a scholar working on Artificial Intelligence, Statistics and Probability and Sociology and Political Science. According to data from OpenAlex, Laura Boeschoten has authored 21 papers receiving a total of 216 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 7 papers in Statistics and Probability and 6 papers in Sociology and Political Science. Recurrent topics in Laura Boeschoten's work include Statistical Methods and Bayesian Inference (6 papers), Statistical Methods and Inference (4 papers) and Privacy-Preserving Technologies in Data (4 papers). Laura Boeschoten is often cited by papers focused on Statistical Methods and Bayesian Inference (6 papers), Statistical Methods and Inference (4 papers) and Privacy-Preserving Technologies in Data (4 papers). Laura Boeschoten collaborates with scholars based in Netherlands, United Kingdom and Australia. Laura Boeschoten's co-authors include Daniel L. Oberski, Theo Araujo, Byron Reeves, Deen Freelon, Jakob Ohme, Thomas N. Robinson, Nilàm Ram, Jef Ausloos, Judith Möller and Ton de Waal and has published in prestigious journals such as Ecology, Annals of the New York Academy of Sciences and Journal of the Royal Statistical Society Series A (Statistics in Society).

In The Last Decade

Laura Boeschoten

18 papers receiving 210 citations

Hit Papers

Digital Trace Data Collection for Social Media Effects Re... 2023 2026 2024 2025 2023 20 40 60
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking
    2023 61 citations Jakob Ohme, Theo Araujo et al. Communication Methods and Measures profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Laura Boeschoten Netherlands 7 112 43 30 29 26 21 216
Wolfgang Bandilla Germany 10 239 2.1× 31 0.7× 11 0.4× 23 0.8× 17 0.7× 21 327
Silvia Biffignandi Italy 8 133 1.2× 21 0.5× 14 0.5× 22 0.8× 9 0.3× 35 244
Casey Langer Tesfaye United States 7 139 1.2× 45 1.0× 19 0.6× 4 0.1× 5 0.2× 17 280
Seyum Getenet Australia 12 62 0.6× 7 0.2× 24 0.8× 22 0.8× 17 0.7× 36 365
Aigul Mavletova Russia 8 271 2.4× 63 1.5× 9 0.3× 17 0.6× 14 0.5× 13 355
Daniele Toninelli Italy 9 179 1.6× 47 1.1× 11 0.4× 9 0.3× 4 0.2× 18 260
Bob van de Velde Netherlands 8 163 1.5× 105 2.4× 92 3.1× 2 0.1× 21 0.8× 13 336
Dale C. Brandenburg United States 9 54 0.5× 33 0.8× 8 0.3× 10 0.3× 6 0.2× 34 381
Michela Montesi Spain 10 96 0.9× 57 1.3× 52 1.7× 17 0.7× 44 289
Bas Hofstra Netherlands 9 166 1.5× 62 1.4× 26 0.9× 2 0.1× 18 0.7× 18 294

Countries citing papers authored by Laura Boeschoten

Since Specialization
Citations

This map shows the geographic impact of Laura Boeschoten's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Laura Boeschoten with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laura Boeschoten more than expected).

Fields of papers citing papers by Laura Boeschoten

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Laura Boeschoten. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Laura Boeschoten. The network helps show where Laura Boeschoten may publish in the future.

Co-authorship network of co-authors of Laura Boeschoten

This figure shows the co-authorship network connecting the top 25 collaborators of Laura Boeschoten. A scholar is included among the top collaborators of Laura Boeschoten based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Laura Boeschoten. Laura Boeschoten is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ferguson, Amanda M, et al.. (2025). Digital Data Donation With Adolescents. Annals of the New York Academy of Sciences. 1554(1). 251–266.
2.
Kormelink, Tim Groot, et al.. (2025). Meaningful informed consent? How participants experience and understand data donation. Information Communication & Society. 1–18.
3.
Boeschoten, Laura, et al.. (2024). Best practices for studies using digital data donation. Quality & Quantity. 59(S1). 389–412. 2 indexed citations
4.
Hase, Valerie, Jef Ausloos, Laura Boeschoten, et al.. (2024). Fulfilling data access obligations: How could (and should) platforms facilitate data donation studies?. Internet Policy Review. 13(3). 2 indexed citations
5.
6.
Boeschoten, Laura, et al.. (2023). Port: A software tool for digital data donation. The Journal of Open Source Software. 8(90). 5596–5596. 8 indexed citations
7.
Ohme, Jakob, Theo Araujo, Laura Boeschoten, et al.. (2023). Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking. Communication Methods and Measures. 18(2). 124–141. 61 indexed citations breakdown →
8.
Giachanou, Anastasia, et al.. (2023). On Text-based Personality Computing: Challenges and Future Directions. 10861–10879. 4 indexed citations
9.
Ruijer, Erna, et al.. (2023). Open data work for empowered deliberative democracy: Findings from a living lab study. Government Information Quarterly. 41(1). 101902–101902. 9 indexed citations
10.
Driel, Irene I. van, Anastasia Giachanou, J. Loes Pouwels, et al.. (2022). Promises and Pitfalls of Social Media Data Donations. Communication Methods and Measures. 16(4). 266–282. 37 indexed citations
11.
Boeschoten, Laura, et al.. (2022). Privacy-preserving local analysis of digital trace data: A proof-of-concept. Patterns. 3(3). 100444–100444. 12 indexed citations
12.
Boeschoten, Laura, Jef Ausloos, Judith Möller, Theo Araujo, & Daniel L. Oberski. (2022). A framework for privacy preserving digital trace data collection through data donation. UvA-DARE (University of Amsterdam). 4(2). 388–423. 38 indexed citations
13.
Boeschoten, Laura, et al.. (2020). Instagram Use and the Well-Being of Adolescents: Using Deep Learning to Link Social Scientific Self-reports with Instagram Data Download Packages. Data Archiving and Networked Services (DANS). 523–523. 3 indexed citations
14.
Boeschoten, Laura, Ton de Waal, & Jeroen K. Vermunt. (2019). Estimating the Number of Serious Road Injuries Per Vehicle Type in the Netherlands by using Multiple Imputation of Latent Classes. Journal of the Royal Statistical Society Series A (Statistics in Society). 182(4). 1463–1486. 3 indexed citations
15.
Boeschoten, Laura, et al.. (2019). Combining Multiple Imputation and Hidden Markov Modeling to Obtain Consistent Estimates of Employment Status. Journal of Survey Statistics and Methodology. 9(3). 549–573. 4 indexed citations
16.
Boeschoten, Laura, Daniel L. Oberski, Ton de Waal, & Jeroen K. Vermunt. (2018). Updating Latent Class Imputations with External Auxiliary Variables. Structural Equation Modeling A Multidisciplinary Journal. 25(5). 750–761. 5 indexed citations
17.
Boeschoten, Laura, Marcel A. Croon, & Daniel L. Oberski. (2018). A Note on Applying the BCH Method Under Linear Equality and Inequality Constraints. Journal of Classification. 36(3). 566–575. 2 indexed citations
18.
Boeschoten, Laura, Daniel L. Oberski, & Ton de Waal. (2017). Estimating Classification Errors Under Edit Restrictions in Composite Survey-Register Data Using Multiple Imputation Latent Class Modelling (MILC). Journal of Official Statistics. 33(4). 921–962. 17 indexed citations
19.
Boeschoten, Laura, Gerko Vink, & Joop J. Hox. (2017). How to Obtain Valid Inference under Unit Nonresponse?. Journal of Official Statistics. 33(4). 963–978. 2 indexed citations
20.
Boeschoten, Laura, et al.. (2016). Estimating classification error under edit restrictions in combined survey-register data. Research portal (Tilburg University). 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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