western blot,

Availability of data, code, and plot creation for various figures throughout my PhD thesis. Rough organisation currently. Pertains to Figures 5.4, 5.8, 6.11, 6.18, 7.3, 7.12, and Table 6.1.

This dataset contains the data availability statements (DAS) from four Nature Portfolio journals from January 2017 to December 2021. This covers two periods; one prior to integrating the figshare repository with the submission system of each journal (January 2017 to December 2021) and one following the integration (April 2022 to July 2023).Each DAS is assigned one or more of seven categories based on its content and any links to available data. This enables analysis of changes in data sharing behaviour, for example either side of the figshare integration.Summary statistics by year and the DAS categories are provided in separate tabs of the worksheet. DAS were initially assigned by basic text-matching (for example the presence of key terms like 'request' in the DAS indicating data are available on request). A human curator then verified each article's categorisation and amended if necessary.

These are the slides of a presentation given at figshare Fest New Zealand on 27th October 2017 at Auckland University. One slide has been removed for copyright reasons from the original slide deck.

Graphs and data for ten journals sharing data in the Dryad digital repository.

This dataset contains 189 survey responses from a respository/ data managers' survey where we explored the current status, needs and challenges of research data repositories.

Collection of scripts and numerically derived data.

Repository to make datasets resulting from NIH funded research more accessible, citable, shareable, and discoverable. Data submitted will be reviewed to ensure there is no personally identifiable information in data and metadata prior to being published and in line with FAIR -Findable, Accessible, Interoperable, and Reusable principles. Data published on Figshare is assigned persistent, citable DOI (Digital Object Identifier) and is discoverable in Google, Google Scholar, Google Dataset Search, and more.Complited on July,2020. Researches can continue to share NIH funded data and other research product on figshare.com.

Collection of experimentally measured data, FEM simulation scripts, and numerically derived data.

General descriptionThis dataset contains some markers of Open Science in the publications of the Chemical Biology Consortium Sweden (CBCS) between 2010 and July 2023. The sample of CBCS publications during this period consists of 188 articles. Every publication was visited manually at its DOI URL to answer the following questions.1. Is the research article an Open Access publication?2. Does the research article have a Creative Common license or a similar license?3. Does the research article contain a data availability statement?4. Did the authors submit data of their study to a repository such as EMBL, Genbank, Protein Data Bank PDB, Cambridge Crystallographic Data Centre CCDC, Dryad or a similar repository?5. Does the research article contain supplementary data?6. Do the supplementary data have a persistent identifier that makes them citable as a defined research output?VariablesThe data were compiled in a Microsoft Excel 365 document that includes the following variables.1. DOI URL of research article2. Year of publication3. Research article published with Open Access4. License for research article5. Data availability statement in article6. Supplementary data added to article7. Persistent identifier for supplementary data8. Authors submitted data to NCBI or EMBL or PDB or Dryad or CCDCVisualizationParts of the data were visualized in two figures as bar diagrams using Microsoft Excel 365. The first figure displays the number of publications during a year, the number of publications that is published with open access and the number of publications that contain a data availability statement (Figure 1). The second figure shows the number of publication sper year and how many publications contain supplementary data. This figure also shows how many of the supplementary datasets have a persistent identifier (Figure 2).File formats and softwareThe file formats used in this dataset are:.csv (Text file).docx (Microsoft Word 365 file).jpg (JPEG image file).pdf/A (Portable Document Format for archiving).png (Portable Network Graphics image file).pptx (Microsoft Power Point 365 file).txt (Text file).xlsx (Microsoft Excel 365 file)All files can be opened with Microsoft Office 365 and work likely also with the older versions Office 2019 and 2016. MD5 checksumsHere is a list of all files of this dataset and of their MD5 checksums.1. Readme.txt (MD5: 795f171be340c13d78ba8608dafb3e76)2. Manifest.txt (MD5: 46787888019a87bb9d897effdf719b71)3. Materials_and_methods.docx (MD5: 0eedaebf5c88982896bd1e0fe57849c2),4. Materials_and_methods.pdf (MD5: d314bf2bdff866f827741d7a746f063b),5. Materials_and_methods.txt (MD5: 26e7319de89285fc5c1a503d0b01d08a),6. CBCS_publications_until_date_2023_07_05.xlsx (MD5: 532fec0bd177844ac0410b98de13ca7c),7. CBCS_publications_until_date_2023_07_05.csv (MD5: 2580410623f79959c488fdfefe8b4c7b),8. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.xlsx (MD5: 9c67dd84a6b56a45e1f50a28419930e5),9. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.csv (MD5: fb3ac69476bfc57a8adc734b4d48ea2b),10. Aggregated_data_from_CBCS_publications_until_2023_07_05.xlsx (MD5: 6b6cbf3b9617fa8960ff15834869f793),11. Aggregated_data_from_CBCS_publications_until_2023_07_05.csv (MD5: b2b8dd36ba86629ed455ae5ad2489d6e),12. Figure_1_CBCS_publications_until_2023_07_05_Open_Access_and_data_availablitiy_statement.xlsx (MD5: 9c0422cf1bbd63ac0709324cb128410e),13. Figure_1.pptx (MD5: 55a1d12b2a9a81dca4bb7f333002f7fe),14. Image_of_figure_1.jpg (MD5: 5179f69297fbbf2eaaf7b641784617d7),15. Image_of_figure_1.png (MD5: 8ec94efc07417d69115200529b359698),16. Figure_2_CBCS_publications_until_2023_07_05_supplementary_data_and_PID_for_supplementary_data.xlsx (MD5: f5f0d6e4218e390169c7409870227a0a),17. Figure_2.pptx (MD5: 0fd4c622dc0474549df88cf37d0e9d72),18. Image_of_figure_2.jpg (MD5: c6c68b63b7320597b239316a1c15e00d),19. Image_of_figure_2.png (MD5: 24413cc7d292f468bec0ac60cbaa7809)

Increased susceptibility to fatigue is a negative predictor of survival commonly experienced by women with breast cancer (BC). In this study, the authors sought to identify molecular changes induced in human skeletal muscle by BC regardless of treatment history or tumor molecular subtype using RNA-sequencing (RNA-seq) and proteomic analyses.Data access: The processed RNA-Seq and proteomics datasets generated during this study are publicly available in the figshare repository as part of this figshare data record: https://doi.org/10.6084/m9.figshare.12248951. The dataset ClinicalCharacteristics.xlsx is not publicly available in order to protect patient privacy, but will be made available on reasonable request from the corresponding author. The patients who took part in this study, did not give consent to have their genetic data made publicly available, and therefore the raw transcriptomic and proteomics data are not publicly available. Raw RNA-Seq and proteomics data will be made available on reasonable request from the corresponding author, to researchers who have completed a Data Usage Agreement. Corresponding author details: Dr. Emidio E. Pistilli, West Virginia University School of Medicine, email address: epistilli2@hsc.wvu.edu.Study approval and patient consent: The procedures in this study were reviewed and approved by the West Virginia University Institutional Review Board (IRB). Informed written consent was obtained from each subject or each subject’s guardian.Study aims and methodology: Muscle dysfunction in individuals with cancer is commonly thought to be a consequence of muscle atrophy, which is a major component of the paraneoplastic syndrome known as cancer cachexia. In this study, the authors tested the hypothesis that breast cancer induces a common molecular response in skeletal muscle that is independent of the molecular subtype of the tumor and the patient’s treatment history.A total of 71 female surgical patients provided informed consent for inclusion in this study (control n=20; BC n=51).Women with BC provided muscle biopsies from the pectoralis major muscle intraoperatively at the time of mastectomy, and control patients provided pectoralis major muscle samples intraoperatively during other breast surgeries. Women with BC were classified into four molecular subtypes based on immunohistochemical staining of their primary tumors:positive for estrogen receptor (ER) and progesterone receptor (PR)- ERPR (n=20), overexpression of HER2/neu in the absence of ER and PR expression- HER2 (n=9), triple negative —absence of ER, PR, and HER2/neu expression- TN (n=11), or triple positive—presence of ER and PR expression, and overexpression of HER2/neuTP-TP (n=11).Information on BMI at multiple time points was collected in 12 control and 50 BC patients. The following techniques are described in more detail in the published article: RNA sequencing, proteomics (including sample preparation, mass spectrometry, and mass spectrometry analysis), Western blotting, and patient muscle ATP quantification.Animal experiments were approved by the WVU Institutional Animal Care and Use Committee, and conducted in accordance with the Guidelines for Ethical Conduct in the Care and Use of Nonhuman Animals in Research. BC-PDOX mice were created by implanting human BC tumor fragments into themammary fat pad of female NOD.CG-Prkdscid Il2rgtm1 Wjl/SzJ/ 0557 (NSG) mice (n=6).For the in vitro experiments, the following cell lines were used: EpH4-EV (immortalized normal murine mammary epithelium), EO771 (murine luminal BC), NF639 (murine HER2/neu-overexpressing BC), HEK293 (human embryonic kidney), and C2C12 (murine myoblasts).Data supporting the figures and supplementary tables in the published article: The following datasets are included in this data record:3000pts.csv in .csv file formatAlbuminAndWeightLoss.csv in .csv file formatATPContentHuman.xlsx in .xlsx file formatATPContentPDOX.xlsx in .xlsx file formatATPProduction.xlsx in .xlsx file formatGFP.xlsx in .xlsx file formatRNASeqProteomicsCorrelation.xlsx in .xlsx file format, contains log-transformed gene and protein expression data for 8 patients with matched RNA-seq and proteomics dataSupplementary Data 3.xlsx in .xlsx file formatSupplementary Data1.xlsx in .xlsx file formatSupplementary Data2.xlsx in .xlsx file formatWBdata.xlsxDataset ClinicalCharacteristics.xlsx contains clinical information on study patients (i.e. body composition, race, treatment history, etc.) and will be made available on request.Figure/Supplementary table supported by the datasets listed above:Figure 1> SupplementaryData1.xlsxFigure 2> AlbuminAndWeightLoss.csv, 3000pts.csvFigure 3> SupplementaryData1.xlsxFigure 4> SupplementaryData1.xlsxFigure 5> SupplementaryData2.xlsx, WBdata.xlsx, SupplementaryData3.xlsxFigure 6> SupplementaryData1.xlsx, ATPContentHuman.xlsx, ATPContentPDOX, ATPProduction.xlsx,GFP.xlsxSupplementary table 1> SupplementaryData1.xlsxSupplementary table 2> SupplementaryData2.xlsxSupplementary table 3> SupplementaryData3.xlsx

Spreadsheet listing data repositories that are recommended by Scientific Data (Springer Nature) as being suitable for hosting data associated with peer-reviewed articles. Please see the repository list on Scientific Data's website for the most up to date list.

Data repository for Seasonality modulates coral trophic plasticity in an extreme, multi-stressor environment in Limnology and Oceanography.

Repository for all data, figures, theses, publications, posters, presentations, filesets, videos, datasets, negative data in a citable, shareable and discoverable manner with Digital Object Identifiers. Allows to upload any file format to be made visualisable in the browser so that figures, datasets, media, papers, posters, presentations and filesets can be disseminated in a way that the current scholarly publishing model does not allow. Features integration with ORCID, Symplectic Elements, can import items from Github and is a source tracked by Altmetric.com. Figshare gives users unlimited public space and 1GB of private storage space for free. Data are digitally preserved by CLOCKSS. Supported by Digital Science, a division of Macmillan Publishers Limited, as a community-based, open science project that retains its autonomy.

This data repository contains Mars Odyssey Thermal Emission Imaging System base mosaics (592.75 pixels/degree), Mars Global Surveyor Mars Orbiter Laser Altimeter gridded topography (128 pixels/degree), and ArcGIS polygon shapefiles for Cawley, J. C., & Irwin, R. P., III (2018). Evolution of escarpments, pediments, and plains in the Noachian highlands of Mars. Journal of Geophysical Research: Planets, 123, 3167–3187. https://doi.org/10.1029/2018JE005681.

Bridges is Monash University's repository for research data, collections, non-traditional research outputs and research activities.

This dataset contains article metadata and information about Open Science Indicators for approximately 139,000 research articles published in PLOS journals from 1 January 2018 to 30 March 2025 and a set of approximately 28,000 comparator articles published in non-PLOS journals. This is the tenth release of this dataset, which will be updated with new versions on an annual basis.This version of the Open Science Indicators dataset shares the indicators seen in the previous versions as well as fully operationalised protocols and study registration indicators, which were previously only shared in preliminary forms. The v10 dataset focuses on detection of five Open Science practices by analysing the XML of published research articles:Sharing of research data, in particular data shared in data repositoriesSharing of codePosting of preprintsSharing of protocolsSharing of study registrationsThe dataset provides data and code generation and sharing rates, the location of shared data and code (whether in Supporting Information or in an online repository). It also provides preprint, protocol and study registration sharing rates as well as details of the shared output, such as publication date, URL/DOI/Registration Identifier and platform used. Additional data fields are also provided for each article analysed. This release has been run using an updated preprint detection method (see OSI-Methods-Statement_v10_Jul25.pdf for details). Further information on the methods used to collect and analyse the data can be found in Documentation.Further information on the principles and requirements for developing Open Science Indicators is available in https://doi.org/10.6084/m9.figshare.21640889.Data folders/filesData Files folderThis folder contains the main OSI dataset files PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv, which containdescriptive metadata, e.g. article title, publication data, author countries, is taken from the article .xml filesadditional information around the Open Science Indicators derived algorithmicallyand the OSI-Summary-statistics_v10_Jul25.xlsx file contains the summary data for both PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv.Documentation folderThis file contains documentation related to the main data files. The file OSI-Methods-Statement_v10_Jul25.pdf describes the methods underlying the data collection and analysis. OSI-Column-Descriptions_v10_Jul25.pdf describes the fields used in PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv. OSI-Repository-List_v1_Dec22.xlsx lists the repositories and their characteristics used to identify specific repositories in the PLOS-Dataset_v10_Jul25.csv and Comparator-Dataset_v10_Jul25.csv repository fields.The folder also contains documentation originally shared alongside the preliminary versions of the protocols and study registration indicators in order to give fuller details of their detection methods.Note on the accuracy of the protocols indicator (January 2026)It has come to our attention that the accuracy rates for the protocols indicator as reported in the Protocols_Methods_Statement_Sep23.pdf file are incorrect. The groundtruth exercise for protocols incorrectly identified 19 articles as having protocols in the PLOS corpus and 21 in the Comparator corpus during the manual exercise. These have been corrected in the groundtruth dataset. The accuracy rates for the PLOS and Comparator corpus are therefore 94% and 97% respectively. Documentation will be updated in the next scheduled release (anticipated in July 2026).Contact details for further information:Iain Hrynaszkiewicz, Director, Open Research Solutions, PLOS, ihrynaszkiewicz@plos.org / plos@plos.orgLauren Cadwallader, Open Research Manager, PLOS, lcadwallader@plos.org / plos@plos.orgAcknowledgements:Thanks to Allegra Pearce, Tim Vines, Asura Enkhbayar, Scott Kerr and parth sarin of DataSeer for contributing to data acquisition and supporting information.

This record includes the replication data and code supporting results of a published book chapter Discoverability beyond the library: Search engine optimization (case study) [link to be added]. The case study compares the discoverability of two hosted institutional repository solutions, Digital Commons and Figshare using a randomized controlled experiment. Two randomly selected groups of journal articles were deposited and made open access in institutional repositories hosted on Digital Commons and Figshare respectively. Download count data were collected over 7 months to measure and compare the open access discoverability and search engine visibility of the two platforms. GENERAL INFORMATION This readme file was generated on 2022-07-04 by Dong Danping Author Contact Name: Dong Danping ORCID: 0000-0002-2229-6709 Institution: Singapore Management University Email: danpingzzz@gmail.com Name: Aaron Tay ORCID: 0000-0003-0159-013X Institution: Singapore Management University Email: aarontay@smu.edu.sg Date of data collection: 2021-04-01 to 2021-10-01 SHARING/ACCESS INFORMATION Licenses/restrictions placed on the data: CC-BY-4.0 License Links to publications that cite or use the data: [to be updated] Links to other publicly accessible locations of the data: https://github.com/dpdong19/IR-compare https://doi.org/10.25440/smu.19121768 Recommended citation for this dataset: Dong, D., & Tay, A. (2022). Data and code to compare the discoverability of Digital Commons and Figshare. https://doi.org/10.25440/smu.19121768 DATA & FILE OVERVIEW File List /analysis/2021-11_DataAnalysis_DCvsFig.ipynb This is the Jupyter Notebook containing notes and scripts of statistical analysis for the case study. /analysis/DCvsFigshare-downloads-combined-v1.csv This file contains clean data for analysis containing download stats from Apr to Oct 2021 for both InK(Digital Commons) and RDR(Figshare).

Relationship between files: The Jupyter Notebook and data file should be placed in the same folder for the code to run. Data Dictionary DCvsFigshare-downloads-combined-v1.csv Number of variables: 15 Number of cases/rows: 92 Variable List Identifier: unique ID for each record. Also the URL to access the record. (Note: Figshare records have been unpublished after the study thus no longer accessible) IR: Name of the IR. InK is on Digital Commons and RDR is on Figshare. Title: title of the deposited journal article Column D-J: monthly download count excluding bots downloads from April to October 2021. Total: sum of column D-J, total download count during the study period AugToOct: sum of column H-J from Aug to Oct 2021 GS_avail: whether the record can be found in Google Scholar. uniq_PDF: whether the record provides the only PDF in Google Scholar primary: whether the record is displayed as the primary record in Google Scholar.

Missing data codes: blank METHODOLOGICAL INFORMATION Methods are described in 2021-11_DataAnalysis_DCvsFig.ipynb and published book chapter [link to be added]

This is a comprehensive repository of spatially and temporally correlated wind velocity samples. The samples are classified based on the maximum sustained wind speed (maximum 1-min average) and the 3-sec gust wind speed (maximum 3-sec average), which are the intensity measures commonly used for structural analysis. The repository consists of 58,000 wind samples with 1-hour duration, as well as 110,847 samples with 2-min and 87,257 wind samples with10-min durations. The developed wind database will allow researchers and engineers to readily select and use wind time histories for many applications, such as the analysis of tall structures located in open terrain areas.

Data on Calcium Dynamics based on calcium indicators Salsa6F and Fluo4 in astrocytes in response to photolysis