@dataset{2840,
  author = {Burger, Gerhard and Leegwater, Hanneke},
  publisher = {The R Foundation},
  title = {Rspacer: {\textquotesingle RSpace\textquotesingle{}} {API}
    {Wrapper}},
  series = {CRAN: Contributed Packages},
  date = {2025-07-05},
  urldate = {2025-07-10},
  url = {https://CRAN.R-project.org/package=rspacer},
  doi = {10.32614/cran.package.rspacer}
}
@misc{2850,
  author = {{European Organization For Nuclear Research} and {OpenAIRE}},
  publisher = {CERN},
  title = {Zenodo: {Research.} {Shared.}},
  date = {2013},
  urldate = {2025-07-24},
  url = {https://www.zenodo.org/},
  doi = {10.25495/7GXK-RD71},
  langid = {en},
  abstract = {Zenodo is a general purpose repository that enables
    researchers, scientists, projects and institutions to share,
    preserve and showcase multidisciplinary research results (data,
    software and publications) that are not part of the existing
    institutional or subject-based repositories of the research
    communities. It is founded in the trustworthy CERN data centre.}
}
@article{2853,
  author = {Machina, Hari K. and Wild, David J.},
  publisher = {Elsevier BV},
  title = {Electronic {Laboratory} {Notebooks} {Progress} and
    {Challenges} in {Implementation}},
  journal = {SLAS Technology},
  volume = {18},
  number = {4},
  pages = {264-268},
  date = {2013-08},
  doi = {10.1177/2211068213484471},
  issn = {2472-6303},
  langid = {en}
}
@article{2855,
  author = {Foster, Erin D. and Whipple, Elizabeth C. and Rios, Gabriel
    R.},
  publisher = {University Library System, University of Pittsburgh},
  title = {Implementing an Institution-Wide Electronic Lab Notebook
    Initiative},
  journal = {Journal of the Medical Library Association},
  volume = {110},
  number = {2},
  date = {2022-04-26},
  urldate = {2025-07-24},
  url = {https://jmla.pitt.edu/ojs/jmla/article/view/1407},
  doi = {10.5195/jmla.2022.1407},
  issn = {1558-9439, 1536-5050},
  abstract = {Background: To strengthen institutional research data
    management practices, the Indiana University School of Medicine
    (IUSM) licensed an electronic lab notebook (ELN) to improve the
    organization, security, and shareability of information and data
    generated by the school’s researchers. The Ruth Lilly Medical
    Library led implementation on behalf of the IUSM’s Office of
    Research Affairs.Case Presentation: This article describes the pilot
    and full-scale implementation of an ELN at IUSM. The initial pilot
    of the ELN in late 2018 involved fifteen research labs with access
    expanded in 2019 to all academic medical school constituents. The
    Ruth Lilly Medical Library supports researchers using the electronic
    lab notebook by (1) delivering trainings that cover strategies for
    adopting an ELN and a hands-on demo of the licensed ELN, (2)
    providing one-on-one consults with research labs or groups as
    needed, and (3) developing best practice guidance and template
    notebooks to assist in adoption of the ELN. The library also
    communicates availability of the ELN to faculty, students, and staff
    through presentations delivered at department meetings and write-ups
    in the institution\textquotesingle s newsletter as
    appropriate.Conclusion: As of August 2021, there are 829 users at
    IUSM. Ongoing challenges include determining what support to offer
    beyond the existing training, sustaining adoption of the ELN within
    research labs, and defining “successful” adoption at the institution
    level. By leading the development of this service, the library is
    more strongly integrated and visible in the research activities of
    the institution, particularly as related to information and data
    management.}
}
@article{2859,
  author = {Parish, Austin J. and Boyack, Kevin W. and Ioannidis, John
    P. A.},
  editor = {Glanzel, Wolfgang},
  publisher = {Public Library of Science (PLoS)},
  title = {Dynamics of Co-Authorship and Productivity Across Different
    Fields of Scientific Research},
  journal = {PLOS ONE},
  volume = {13},
  number = {1},
  pages = {e0189742},
  date = {2018-01-10},
  doi = {10.1371/journal.pone.0189742},
  issn = {1932-6203},
  langid = {en}
}
@article{2861,
  author = {Perrier, Laure and Blondal, Erik and Ayala, A. Patricia and
    Dearborn, Dylanne and Kenny, Tim and Lightfoot, David and Reka,
    Roger and Thuna, Mindy and Trimble, Leanne and MacDonald, Heather},
  editor = {Jadhao, Sanjay B},
  publisher = {Public Library of Science (PLoS)},
  title = {Research Data Management in Academic Institutions: {A}
    Scoping Review},
  journal = {PLOS ONE},
  volume = {12},
  number = {5},
  pages = {e0178261},
  date = {2017-05-23},
  doi = {10.1371/journal.pone.0178261},
  issn = {1932-6203},
  langid = {en}
}
@article{351,
  author = {Carpenter, Anne E and Jones, Thouis R and Lamprecht, Michael
    R and Clarke, Colin and Kang, In Han and Friman, Ola and Guertin,
    David A and Chang, Joo Han and Lindquist, Robert A and Moffat, Jason
    and Golland, Polina and Sabatini, David M},
  title = {CellProfiler: Image Analysis Software for Identifying and
    Quantifying Cell Phenotypes},
  journal = {Genome Biol.},
  volume = {7},
  number = {10},
  pages = {R100},
  date = {2006-10-31},
  doi = {10.1186/gb-2006-7-10-r100},
  issn = {1465-6906},
  abstract = {Biologists can now prepare and image thousands of samples
    per day using automation, enabling chemical screens and functional
    genomics (for example, using RNA interference). Here we describe the
    first free, open-source system designed for flexible,
    high-throughput cell image analysis, CellProfiler. CellProfiler can
    address a variety of biological questions quantitatively, including
    standard assays (for example, cell count, size, per-cell protein
    levels) and complex morphological assays (for example,
    cell/organelle shape or subcellular patterns of DNA or protein
    staining).}
}
@article{Kbjiwfet/2sNhaJ8r,
  author = {Perez-Riverol, Yasset and Bandla, Chakradhar and Kundu,
    Deepti~J and Kamatchinathan, Selvakumar and Bai, Jingwen and
    Hewapathirana, Suresh and John, Nithu~Sara and Prakash, Ananth and
    Walzer, Mathias and Wang, Shengbo and Vizcaíno, Juan~Antonio},
  title = {The {PRIDE} Database at 20 Years: 2025 Update},
  journal = {Nucleic Acids Research},
  volume = {53},
  number = {D1},
  pages = {D543-D553},
  date = {2025-01-06},
  doi = {10.1093/nar/gkae1011},
  issn = {0305-1048, 1362-4962},
  langid = {en},
  abstract = {Abstract The PRoteomics IDEntifications (PRIDE) database
    (https://www.ebi.ac.uk/pride/) is the world’s leading mass
    spectrometry (MS)-based proteomics data repository and one of the
    founding members of the ProteomeXchange consortium. This manuscript
    summarizes the developments in PRIDE resources and related tools for
    the last three years. The number of submitted datasets to PRIDE
    Archive (the archival component of PRIDE) has reached on average
    around 534 datasets per month. This has been possible thanks to
    continuous improvements in infrastructure such as a new file
    transfer protocol for very large datasets (Globus), a new data
    resubmission pipeline and an automatic dataset validation process.
    Additionally, we will highlight novel activities such as the
    availability of the PRIDE chatbot (based on the use of open-source
    Large Language Models), and our work to improve support for MS
    crosslinking datasets. Furthermore, we will describe how we have
    increased our efforts to reuse, reanalyze and disseminate
    high-quality proteomics data into added-value resources such as
    UniProt, Ensembl and Expression Atlas.}
}
@article{Kbjiwfet/582m1iNq,
  author = {Wilkinson, Mark D. and Dumontier, Michel and Aalbersberg,
    IJsbrand Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie
    and Blomberg, Niklas and Boiten, Jan-Willem and Da Silva Santos,
    Luiz Bonino and Bourne, Philip E. and Bouwman, Jildau and Brookes,
    Anthony J. and Clark, Tim and Crosas, Mercè and Dillo, Ingrid and
    Dumon, Olivier and Edmunds, Scott and Evelo, Chris T. and Finkers,
    Richard and Gonzalez-Beltran, Alejandra and Gray, Alasdair J.G. and
    Groth, Paul and Goble, Carole and Grethe, Jeffrey S. and Heringa,
    Jaap and ’ T Hoen, Peter A.C and Hooft, Rob and Kuhn, Tobias and
    Kok, Ruben and Kok, Joost and Lusher, Scott J. and Martone, Maryann
    E. and Mons, Albert and Packer, Abel L. and Persson, Bengt and
    Rocca-Serra, Philippe and Roos, Marco and Van Schaik, Rene and
    Sansone, Susanna-Assunta and Schultes, Erik and Sengstag, Thierry
    and Slater, Ted and Strawn, George and Swertz, Morris A. and
    Thompson, Mark and Van Der Lei, Johan and Van Mulligen, Erik and
    Velterop, Jan and Waagmeester, Andra and Wittenburg, Peter and
    Wolstencroft, Katherine and Zhao, Jun and Mons, Barend},
  title = {The {FAIR} {Guiding} {Principles} for Scientific Data
    Management and Stewardship},
  journal = {Scientific Data},
  volume = {3},
  number = {1},
  pages = {160018},
  date = {2016-03-15},
  doi = {10.1038/sdata.2016.18},
  issn = {2052-4463},
  langid = {en},
  abstract = {Abstract There is an urgent need to improve the
    infrastructure supporting the reuse of scholarly data. A diverse set
    of stakeholders—representing academia, industry, funding agencies,
    and scholarly publishers—have come together to design and jointly
    endorse a concise and measureable set of principles that we refer to
    as the FAIR Data Principles. The intent is that these may act as a
    guideline for those wishing to enhance the reusability of their data
    holdings. Distinct from peer initiatives that focus on the human
    scholar, the FAIR Principles put specific emphasis on enhancing the
    ability of machines to automatically find and use the data, in
    addition to supporting its reuse by individuals. This Comment is the
    first formal publication of the FAIR Principles, and includes the
    rationale behind them, and some exemplar implementations in the
    community.}
}
@article{Kbjiwfet/6NZuEeQJ,
  author = {Burger, Gerhard A. and Van De Water, Bob and Le Dévédec,
    Sylvia E. and Beltman, Joost B.},
  title = {Density-Dependent {Migration} {Characteristics} of {Cancer}
    {Cells} {Driven} by {Pseudopod} {Interaction}},
  journal = {Frontiers in Cell and Developmental Biology},
  volume = {10},
  pages = {854721},
  date = {2022-04-25},
  doi = {10.3389/fcell.2022.854721},
  issn = {2296-634X},
  abstract = {The ability of cancer cells to invade neighboring tissue
    from primary tumors is an important determinant of metastatic
    behavior. Quantification of cell migration characteristics such as
    migration speed and persistence helps to understand the requirements
    for such invasiveness. One factor that may influence invasion is how
    local tumor cell density shapes cell migration characteristics,
    which we here investigate with a combined experimental and
    computational modeling approach. First, we generated and analyzed
    time-lapse imaging data on two aggressive Triple-Negative Breast
    Cancer (TNBC) cell lines, HCC38 and Hs578T, during 2D migration
    assays at various cell densities. HCC38 cells exhibited a
    counter-intuitive increase in speed and persistence with increasing
    density, whereas Hs578T did not exhibit such an increase. Moreover,
    HCC38 cells exhibited strong cluster formation with active
    pseudopod-driven migration, especially at low densities, whereas
    Hs578T cells maintained a dispersed positioning. In order to obtain
    a mechanistic understanding of the density-dependent cell migration
    characteristics and cluster formation, we developed realistic
    spatial simulations using a Cellular Potts Model (CPM) with an
    explicit description of pseudopod dynamics. Model analysis
    demonstrated that pseudopods exerting a pulling force on the cell
    and interacting via increased adhesion at pseudopod tips could
    explain the experimentally observed increase in speed and
    persistence with increasing density in HCC38 cells. Thus, the
    density-dependent migratory behavior could be an emergent property
    of single-cell characteristics without the need for additional
    mechanisms. This implies that pseudopod dynamics and interaction may
    play a role in the aggressive nature of cancers through mediating
    dispersal.}
}
@article{Kbjiwfet/8bZBfR2d,
  author = {Schmied, Christopher and Nelson, Michael S. and Avilov,
    Sergiy and Bakker, Gert-Jan and Bertocchi, Cristina and Bischof,
    Johanna and Boehm, Ulrike and Brocher, Jan and Carvalho, Mariana T.
    and Chiritescu, Catalin and Christopher, Jana and Cimini, Beth A.
    and Conde-Sousa, Eduardo and Ebner, Michael and Ecker, Rupert and
    Eliceiri, Kevin and Fernandez-Rodriguez, Julia and Gaudreault,
    Nathalie and Gelman, Laurent and Grunwald, David and Gu, Tingting
    and Halidi, Nadia and Hammer, Mathias and Hartley, Matthew and Held,
    Marie and Jug, Florian and Kapoor, Varun and Koksoy, Ayse Aslihan
    and Lacoste, Judith and Le Dévédec, Sylvia and Le Guyader, Sylvie
    and Liu, Penghuan and Martins, Gabriel G. and Mathur, Aastha and
    Miura, Kota and Montero Llopis, Paula and Nitschke, Roland and
    North, Alison and Parslow, Adam C. and Payne-Dwyer, Alex and
    Plantard, Laure and Ali, Rizwan and Schroth-Diez, Britta and Schütz,
    Lucas and Scott, Ryan T. and Seitz, Arne and Selchow, Olaf and
    Sharma, Ved P. and Spitaler, Martin and Srinivasan, Sathya and
    Strambio-De-Castillia, Caterina and Taatjes, Douglas and Tischer,
    Christian and Jambor, Helena Klara},
  title = {Community-Developed Checklists for Publishing Images and
    Image Analyses},
  journal = {Nature Methods},
  volume = {21},
  number = {2},
  pages = {170-181},
  date = {2024-02},
  doi = {10.1038/s41592-023-01987-9},
  issn = {1548-7091, 1548-7105},
  langid = {en}
}
@article{Kbjiwfet/94CwkKGK,
  author = {Kanza, Samantha and Willoughby, Cerys and Gibbins, Nicholas
    and Whitby, Richard and Frey, Jeremy Graham and Erjavec, Jana and
    Zupančič, Klemen and Hren, Matjaž and Kovač, Katarina},
  title = {Electronic Lab Notebooks: Can They Replace Paper?},
  journal = {Journal of Cheminformatics},
  volume = {9},
  number = {1},
  pages = {31},
  date = {2017-12},
  doi = {10.1186/s13321-017-0221-3},
  issn = {1758-2946},
  langid = {en}
}
@article{Kbjiwfet/Blt53i0n,
  author = {Barrett, Tanya and Wilhite, Stephen E. and Ledoux, Pierre
    and Evangelista, Carlos and Kim, Irene F. and Tomashevsky, Maxim and
    Marshall, Kimberly A. and Phillippy, Katherine H. and Sherman, Patti
    M. and Holko, Michelle and Yefanov, Andrey and Lee, Hyeseung and
    Zhang, Naigong and Robertson, Cynthia L. and Serova, Nadezhda and
    Davis, Sean and Soboleva, Alexandra},
  title = {NCBI {GEO:} Archive for Functional Genomics Data Sets—Update},
  journal = {Nucleic Acids Research},
  volume = {41},
  number = {D1},
  pages = {D991-D995},
  date = {2012-11-26},
  doi = {10.1093/nar/gks1193},
  issn = {0305-1048, 1362-4962},
  langid = {en}
}
@article{Kbjiwfet/FIizNHeM,
  author = {{wwPDB consortium} and Burley, Stephen K and Berman, Helen M
    and Bhikadiya, Charmi and Bi, Chunxiao and Chen, Li and Costanzo,
    Luigi Di and Christie, Cole and Duarte, Jose M and Dutta,
    Shuchismita and Feng, Zukang and Ghosh, Sutapa and Goodsell, David S
    and Green, Rachel Kramer and Guranovic, Vladimir and Guzenko, Dmytro
    and Hudson, Brian P and Liang, Yuhe and Lowe, Robert and Peisach,
    Ezra and Periskova, Irina and Randle, Chris and Rose, Alexander and
    Sekharan, Monica and Shao, Chenghua and Tao, Yi-Ping and Valasatava,
    Yana and Voigt, Maria and Westbrook, John and Young, Jasmine and
    Zardecki, Christine and Zhuravleva, Marina and Kurisu, Genji and
    Nakamura, Haruki and Kengaku, Yumiko and Cho, Hasumi and Sato, Junko
    and Kim, Ju Yaen and Ikegawa, Yasuyo and Nakagawa, Atsushi and
    Yamashita, Reiko and Kudou, Takahiro and Bekker, Gert-Jan and
    Suzuki, Hirofumi and Iwata, Takeshi and Yokochi, Masashi and
    Kobayashi, Naohiro and Fujiwara, Toshimichi and Velankar, Sameer and
    Kleywegt, Gerard J and Anyango, Stephen and Armstrong, David R and
    Berrisford, John M and Conroy, Matthew J and Dana, Jose M and
    Deshpande, Mandar and Gane, Paul and Gáborová, Romana and Gupta,
    Deepti and Gutmanas, Aleksandras and Koča, Jaroslav and Mak, Lora
    and Mir, Saqib and Mukhopadhyay, Abhik and Nadzirin, Nurul and Nair,
    Sreenath and Patwardhan, Ardan and Paysan-Lafosse, Typhaine and
    Pravda, Lukas and Salih, Osman and Sehnal, David and Varadi, Mihaly
    and Vařeková, Radka and Markley, John L and Hoch, Jeffrey C and
    Romero, Pedro R and Baskaran, Kumaran and Maziuk, Dimitri and
    Ulrich, Eldon L and Wedell, Jonathan R and Yao, Hongyang and Livny,
    Miron and Ioannidis, Yannis E},
  title = {Protein {Data} {Bank:} The Single Global Archive for {3D}
    Macromolecular Structure Data},
  journal = {Nucleic Acids Research},
  volume = {47},
  number = {D1},
  pages = {D520-D528},
  date = {2019-01-08},
  doi = {10.1093/nar/gky949},
  issn = {0305-1048, 1362-4962},
  langid = {en}
}
@article{Kbjiwfet/FtFYpjWd,
  author = {Koedoot, Esmee and Fokkelman, Michiel and Rogkoti, Vasiliki
    Maria and Smid, Marcel and van de Sandt, Iris and de Bont, Hans and
    Pont, Chantal and Klip, Janna E. and Wink, Steven and Timmermans,
    Mieke A. and Wiemer, Erik A.C. and Stoilov, Peter and Foekens, John
    A. and Le Dévédec, Sylvia E. and Martens, John W.M. and van de
    Water, Bob},
  publisher = {Springer US},
  title = {Uncovering the Signaling Landscape Controlling Breast Cancer
    Cell Migration Identifies Novel Metastasis Driver Genes},
  journal = {Nature Communications},
  volume = {10},
  number = {1},
  pages = {1-16},
  date = {2019},
  doi = {10.1038/s41467-019-11020-3},
  abstract = {Ttriple-negative breast cancer (TNBC) is an aggressive and
    highly metastatic breast cancer subtype. Enhanced TNBC cell motility
    is a prerequisite of TNBC cell dissemination. Here, we apply an
    imaging-based RNAi phenotypic cell migration screen using two highly
    motile TNBC cell lines (Hs578T and MDA-MB-231) to provide a
    repository of signaling determinants that functionally drive TNBC
    cell motility. We have screened \textasciitilde4,200 target genes
    individually and discovered 133 and 113 migratory modulators of
    Hs578T and MDA-MB-231, respectively, which are linked to signaling
    networks predictive for breast cancer progression. The splicing
    factors PRPF4B and BUD31 and the transcription factor BPTF are
    essential for cancer cell migration, amplified in human primary
    breast tumors and associated with metastasis-free survival.
    Depletion of PRPF4B, BUD31 and BPTF causes primarily down regulation
    of genes involved in focal adhesion and ECM-interaction pathways.
    PRPF4B is essential for TNBC metastasis formation in vivo, making
    PRPF4B a candidate for further drug development.}
}
@article{Kbjiwfet/LNcisbHH,
  author = {Higgins, Stuart G. and Nogiwa-Valdez, Akemi A. and Stevens,
    Molly M.},
  title = {Considerations for Implementing Electronic Laboratory
    Notebooks in an Academic Research Environment},
  journal = {Nature Protocols},
  volume = {17},
  number = {2},
  pages = {179-189},
  date = {2022-02},
  doi = {10.1038/s41596-021-00645-8},
  issn = {1754-2189, 1750-2799},
  langid = {en}
}
@report{Kbjiwfet/LqlhCVIm,
  author = {{R Core Team}},
  publisher = {R Foundation for Statistical Computing},
  title = {R: A Language and Environment for Statistical Computing},
  date = {2024},
  address = {Vienna, Austria},
  url = {https://www.R-project.org/}
}
@article{Kbjiwfet/QxJiECvY,
  author = {Hosseini, Rohola and Vlasveld, Matthijs and Willemse, Joost
    and Van De Water, Bob and Le Dévédec, Sylvia E. and Wolstencroft,
    Katherine J.},
  title = {FAIR {High} {Content} {Screening} in {Bioimaging}},
  journal = {Scientific Data},
  volume = {10},
  number = {1},
  pages = {462},
  date = {2023-07-17},
  doi = {10.1038/s41597-023-02367-w},
  issn = {2052-4463},
  langid = {en}
}
@article{Kbjiwfet/WyQgwwAi,
  author = {Courtot, Mélanie and Gupta, Dipayan and Liyanage, Isuru and
    Xu, Fuqi and Burdett, Tony},
  title = {BioSamples Database: {FAIRer} Samples Metadata to Accelerate
    Research Data Management},
  journal = {Nucleic Acids Research},
  volume = {50},
  number = {D1},
  pages = {D1500-D1507},
  date = {2022-01-07},
  doi = {10.1093/nar/gkab1046},
  issn = {0305-1048, 1362-4962},
  langid = {en},
  abstract = {Abstract The BioSamples database at EMBL-EBI is the
    central institutional repository for sample metadata storage and
    connection to EMBL-EBI archives and other resources. The technical
    improvements to our infrastructure described in our last update have
    enabled us to scale and accommodate an increasing number of
    communities, resulting in a higher number of submissions and more
    heterogeneous data. The BioSamples database now has a valuable set
    of features and processes to improve data quality in BioSamples, and
    in particular enriching metadata content and following FAIR
    principles. In this manuscript, we describe how BioSamples in 2021
    handles requirements from our community of users through exemplar
    use cases: increased findability of samples and improved data
    management practices support the goals of the ReSOLUTE project, how
    the plant community benefits from being able to link genotypic to
    phenotypic information, and we highlight how cumulatively those
    improvements contribute to more complex multi-omics data integration
    supporting COVID-19 research. Finally, we present underlying
    technical features used as pillars throughout those use cases and
    how they are reused for expanded engagement with communities such as
    FAIRplus and the Global Alliance for Genomics and Health.
    Availability: The BioSamples database is freely available at
    http://www.ebi.ac.uk/biosamples. Content is distributed under the
    EMBL-EBI Terms of Use available at
    https://www.ebi.ac.uk/about/terms-of-use. The BioSamples code is
    available at https://github.com/EBIBioSamples/biosamples-v4 and
    distributed under the Apache~2.0 license.}
}
@software{Kbjiwfet/cXgcxWMZ,
  author = {Burger, Gerhard and Heldring, Muriel and Wink, Steven},
  title = {CPTrackR: {Create} Unique Track Identifiers for
    {CellProfiler} Tracking Output},
  version = {R package version 0.2.2.9000, commit
    580a1b79786564f03ade68cd29fb32ef5c867c21},
  date = {2024},
  url = {https://github.com/burgerga/CPTrackR}
}
@article{Kbjiwfet/eqgDZAyy,
  author = {Kopczynski, Dominik and Ejsing, Christer S. and McDonald,
    Jeffrey G. and Bamba, Takeshi and Baker, Erin S. and
    Bertrand-Michel, Justine and Brügger, Britta and Coman, Cristina and
    Ellis, Shane R. and Garrett, Timothy J. and Griffiths, William J.
    and Guan, Xue Li and Han, Xianlin and Höring, Marcus and Holčapek,
    Michal and Hoffmann, Nils and Huynh, Kevin and Lehmann, Rainer and
    Jones, Jace W. and Kaddurah-Daouk, Rima and Köfeler, Harald C. and
    Meikle, Peter J. and Metz, Thomas O. and O’Donnell, Valerie B. and
    Saigusa, Daisuke and Schwudke, Dominik and Shevchenko, Andrej and
    Torta, Federico and Vizcaíno, Juan Antonio and Welti, Ruth and Wenk,
    Markus R. and Wolrab, Denise and Xia, Yu and Ekroos, Kim and
    Ahrends, Robert and Liebisch, Gerhard},
  title = {The Lipidomics Reporting Checklist a Framework for
    Transparency of Lipidomic Experiments and Repurposing Resource Data},
  journal = {Journal of Lipid Research},
  volume = {65},
  number = {9},
  pages = {100621},
  date = {2024-09},
  doi = {10.1016/j.jlr.2024.100621},
  issn = {00222275},
  langid = {en}
}
@article{Kbjiwfet/evy2Mqu1,
  author = {Allan, Chris and Burel, Jean-Marie and Moore, Josh and
    Blackburn, Colin and Linkert, Melissa and Loynton, Scott and
    MacDonald, Donald and Moore, William J and Neves, Carlos and
    Patterson, Andrew and Porter, Michael and Tarkowska, Aleksandra and
    Loranger, Brian and Avondo, Jerome and Lagerstedt, Ingvar and
    Lianas, Luca and Leo, Simone and Hands, Katherine and Hay, Ron T and
    Patwardhan, Ardan and Best, Christoph and Kleywegt, Gerard J and
    Zanetti, Gianluigi and Swedlow, Jason R},
  title = {OMERO: Flexible, Model-Driven Data Management for
    Experimental Biology},
  journal = {Nature Methods},
  volume = {9},
  number = {3},
  pages = {245-253},
  date = {2012-03},
  doi = {10.1038/nmeth.1896},
  issn = {1548-7091, 1548-7105},
  langid = {en}
}
@article{Kbjiwfet/f2ngKxrs,
  author = {Nijsse, Bart and Schaap, Peter J and Koehorst, Jasper J},
  title = {FAIR Data Station for Lightweight Metadata Management and
    Validation of Omics Studies},
  journal = {GigaScience},
  volume = {12},
  pages = {giad014},
  date = {2022-12-28},
  doi = {10.1093/gigascience/giad014},
  issn = {2047-217X},
  langid = {en},
  abstract = {Abstract Background The life sciences are one of the
    biggest suppliers of scientific data. Reusing and connecting these
    data can uncover hidden insights and lead to new concepts. Efficient
    reuse of these datasets is strongly promoted when they are
    interlinked with a sufficient amount of machine-actionable metadata.
    While the FAIR (Findable, Accessible, Interoperable, Reusable)
    guiding principles have been accepted by all stakeholders, in
    practice, there are only a limited number of easy-to-adopt
    implementations available that fulfill the needs of data producers.
    Findings We developed the FAIR Data Station, a lightweight
    application written in Java, that aims to support researchers in
    managing research metadata according to the FAIR principles. It
    implements the ISA metadata framework and uses minimal information
    metadata standards to capture experiment metadata. The FAIR Data
    Station consists of 3 modules. Based on the minimal information
    model(s) selected by the user, the “form generation module” creates
    a metadata template Excel workbook with a header row of
    machine-actionable attribute names. The Excel workbook is
    subsequently used by the data producer(s) as a familiar environment
    for sample metadata registration. At any point during this process,
    the format of the recorded values can be checked using the
    “validation module.” Finally, the “resource module” can be used to
    convert the set of metadata recorded in the Excel workbook in RDF
    format, enabling (cross-project) (meta)data searches and, for
    publishing of sequence data, in an European Nucleotide
    Archive–compatible XML metadata file. Conclusions Turning FAIR into
    reality requires the availability of easy-to-adopt data
    FAIRification workflows that are also of direct use for data
    producers. As such, the FAIR Data Station provides, in addition to
    the means to correctly FAIRify (omics) data, the means to build
    searchable metadata databases of similar projects and can assist in
    ENA metadata submission of sequence data. The FAIR Data Station is
    available at https://fairbydesign.nl.}
}
@article{Kbjiwfet/gT3XgAG0,
  author = {Sarkans, Ugis and Gostev, Mikhail and Athar, Awais and
    Behrangi, Ehsan and Melnichuk, Olga and Ali, Ahmed and Minguet,
    Jasmine and Rada, Juan Camillo and Snow, Catherine and Tikhonov,
    Andrew and Brazma, Alvis and McEntyre, Johanna},
  title = {The {BioStudies} Database—One Stop Shop for All Data
    Supporting a Life Sciences Study},
  journal = {Nucleic Acids Research},
  volume = {46},
  number = {D1},
  pages = {D1266-D1270},
  date = {2018-01-04},
  doi = {10.1093/nar/gkx965},
  issn = {0305-1048, 1362-4962},
  langid = {en}
}
@article{Kbjiwfet/gt8fS2Fr,
  author = {Rocca-Serra, Philippe and Brandizi, Marco and Maguire,
    Eamonn and Sklyar, Nataliya and Taylor, Chris and Begley, Kimberly
    and Field, Dawn and Harris, Stephen and Hide, Winston and Hofmann,
    Oliver and Neumann, Steffen and Sterk, Peter and Tong, Weida and
    Sansone, Susanna-Assunta},
  title = {ISA Software Suite: Supporting Standards-Compliant
    Experimental Annotation and Enabling Curation at the Community
    Level},
  journal = {Bioinformatics},
  volume = {26},
  number = {18},
  pages = {2354-2356},
  date = {2010-09-15},
  doi = {10.1093/bioinformatics/btq415},
  issn = {1367-4811, 1367-4803},
  langid = {en},
  abstract = {Abstract Summary: The first open source software suite for
    experimentalists and curators that (i) assists in the annotation and
    local management of experimental metadata from high-throughput
    studies employing one or a combination of omics and other
    technologies; (ii) empowers users to uptake community-defined
    checklists and ontologies; and (iii) facilitates submission to
    international public repositories. Availability and Implementation:
    Software, documentation, case studies and implementations at
    http://www.isa-tools.org Contact: ~isatools@googlegroups.com}
}
@software{Kbjiwfet/lX7U9jLx,
  author = {Wickham, Hadley},
  title = {Httr2: {Perform} {HTTP} Requests and Process the Responses},
  version = {R package version 1.1.0},
  date = {2025},
  url = {https://CRAN.R-project.org/package=httr2}
}
@article{Kbjiwfet/rfyFooBR,
  author = {Deutsch, Eric W and Bandeira, Nuno and Perez-Riverol, Yasset
    and Sharma, Vagisha and Carver, Jeremy~J and Mendoza, Luis and
    Kundu, Deepti J and Wang, Shengbo and Bandla, Chakradhar and
    Kamatchinathan, Selvakumar and Hewapathirana, Suresh and Pullman,
    Benjamin~S and Wertz, Julie and Sun, Zhi and Kawano, Shin and Okuda,
    Shujiro and Watanabe, Yu and MacLean, Brendan and MacCoss, Michael~J
    and Zhu, Yunping and Ishihama, Yasushi and Vizcaíno, Juan~Antonio},
  title = {The {ProteomeXchange} Consortium at 10 Years: 2023 Update},
  journal = {Nucleic Acids Research},
  volume = {51},
  number = {D1},
  pages = {D1539-D1548},
  date = {2023-01-06},
  doi = {10.1093/nar/gkac1040},
  issn = {0305-1048, 1362-4962},
  langid = {en},
  abstract = {Abstract Mass spectrometry (MS) is by far the most used
    experimental approach in high-throughput proteomics. The
    ProteomeXchange (PX) consortium of proteomics resources
    (http://www.proteomexchange.org) was originally set up to
    standardize data submission and dissemination of public MS
    proteomics data. It is now 10 years since the initial data workflow
    was implemented. In this manuscript, we describe the main
    developments in PX since the previous update manuscript
    in~Nucleic~Acids Research~was published in 2020. The six members of
    the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE,
    jPOST, iProX and Panorama Public. We report the current data
    submission statistics, showcasing that the number of datasets
    submitted to PX resources has continued to increase every year. As
    of June 2022, more than 34 233 datasets had been submitted to PX
    resources, and from those, 20 062 (58.6\%) just in the last three
    years. We also report the development of the Universal Spectrum
    Identifiers and the improvements in capturing the experimental
    metadata annotations. In parallel, we highlight that data re-use
    activities of public datasets continue to increase, enabling
    connections between PX resources and other popular bioinformatics
    resources, novel research and also new data resources. Finally, we
    summarise the current state-of-the-art in data management practices
    for sensitive human (clinical) proteomics data.}
}
@article{Kbjiwfet/siOdZI48,
  author = {Lamprecht, Anna-Lena and Palmblad, Magnus and Ison, Jon and
    Schwämmle, Veit and Al Manir, Mohammad Sadnan and Altintas, Ilkay
    and Baker, Christopher J. O. and Ben Hadj Amor, Ammar and
    Capella-Gutierrez, Salvador and Charonyktakis, Paulos and Crusoe,
    Michael R. and Gil, Yolanda and Goble, Carole and Griffin, Timothy
    J. and Groth, Paul and Ienasescu, Hans and Jagtap, Pratik and Kalaš,
    Matúš and Kasalica, Vedran and Khanteymoori, Alireza and Kuhn,
    Tobias and Mei, Hailiang and Ménager, Hervé and Möller, Steffen and
    Richardson, Robin A. and Robert, Vincent and Soiland-Reyes, Stian
    and Stevens, Robert and Szaniszlo, Szoke and Verberne, Suzan and
    Verhoeven, Aswin and Wolstencroft, Katherine},
  title = {Perspectives on Automated Composition of Workflows in the
    Life Sciences},
  journal = {F1000Research},
  volume = {10},
  pages = {897},
  date = {2021-09-07},
  doi = {10.12688/f1000research.54159.1},
  issn = {2046-1402},
  langid = {en},
  abstract = {Scientific data analyses often combine several
    computational tools in automated pipelines, or workflows. Thousands
    of such workflows have been used in the life sciences, though their
    composition has remained a cumbersome manual process due to a lack
    of standards for annotation, assembly, and implementation. Recent
    technological advances have returned the long-standing vision of
    automated workflow composition into focus. This article summarizes a
    recent Lorentz Center workshop dedicated to automated composition of
    workflows in the life sciences. We survey previous initiatives to
    automate the composition process, and discuss the current state of
    the art and future perspectives. We start by drawing the “big
    picture” of the scientific workflow development life cycle, before
    surveying and discussing current methods, technologies and practices
    for semantic domain modelling, automation in workflow development,
    and workflow assessment. Finally, we derive a roadmap of individual
    and community-based actions to work toward the vision of automated
    workflow development in the forthcoming years. A central outcome of
    the workshop is a general description of the workflow life cycle in
    six stages: 1) scientific question or hypothesis, 2) conceptual
    workflow, 3) abstract workflow, 4) concrete workflow, 5) production
    workflow, and 6) scientific results. The transitions between stages
    are facilitated by diverse tools and methods, usually incorporating
    domain knowledge in some form. Formal semantic domain modelling is
    hard and often a bottleneck for the application of semantic
    technologies. However, life science communities have made
    considerable progress here in recent years and are continuously
    improving, renewing interest in the application of semantic
    technologies for workflow exploration, composition and
    instantiation. Combined with systematic benchmarking with reference
    data and large-scale deployment of production-stage workflows, such
    technologies enable a more systematic process of workflow
    development than we know today. We believe that this can lead to
    more robust, reusable, and sustainable workflows in the future.}
}
@article{Kbjiwfet/uN9W7T8b,
  author = {Athar, Awais and Füllgrabe, Anja and George, Nancy and
    Iqbal, Haider and Huerta, Laura and Ali, Ahmed and Snow, Catherine
    and Fonseca, Nuno A and Petryszak, Robert and Papatheodorou, Irene
    and Sarkans, Ugis and Brazma, Alvis},
  title = {ArrayExpress Update – from Bulk to Single-Cell Expression
    Data},
  journal = {Nucleic Acids Research},
  volume = {47},
  number = {D1},
  pages = {D711-D715},
  date = {2019-01-08},
  doi = {10.1093/nar/gky964},
  issn = {0305-1048, 1362-4962},
  langid = {en}
}
@article{Kbjiwfet/wOq7cOsP,
  author = {Kwok, Roberta},
  title = {How to Pick an Electronic Laboratory Notebook},
  journal = {Nature},
  volume = {560},
  number = {7717},
  pages = {269-270},
  date = {2018-08-09},
  doi = {10.1038/d41586-018-05895-3},
  issn = {0028-0836, 1476-4687},
  langid = {en}
}
@article{Kbjiwfet/zEKU8YEx,
  author = {Yurekten, Ozgur and Payne, Thomas and Tejera, Noemi and
    Amaladoss, Felix Xavier and Martin, Callum and Williams, Mark and
    O’Donovan, Claire},
  title = {MetaboLights: Open Data Repository for Metabolomics},
  journal = {Nucleic Acids Research},
  volume = {52},
  number = {D1},
  pages = {D640-D646},
  date = {2024-01-05},
  doi = {10.1093/nar/gkad1045},
  issn = {0305-1048, 1362-4962},
  langid = {en},
  abstract = {Abstract MetaboLights is a global database for
    metabolomics studies including the raw experimental data and the
    associated metadata. The database is cross-species and
    cross-technique and covers metabolite structures and their reference
    spectra as well as their biological roles and locations where
    available. MetaboLights is the recommended metabolomics repository
    for a number of leading journals and ELIXIR, the European
    infrastructure for life science information. In this article, we
    describe the continued growth and diversity of submissions and the
    significant developments in recent years. In particular, we
    highlight MetaboLights Labs, our new Galaxy Project instance with
    repository-scale standardized workflows, and how data public on
    MetaboLights are being reused by the community. Metabolomics
    resources and data are available under the EMBL-EBI’s Terms of Use
    at https://www.ebi.ac.uk/metabolights and under Apache 2.0 at
    https://github.com/EBI-Metabolights.}
}
@book{wickham2023r,
  title={R packages},
  author={Wickham, Hadley and Bryan, Jennifer},
  url = {https://r-pkgs.org/}
}
