Files and datasets in Parquet format related to molecular dynamics and retrieved from the Zenodo, Figshare and OSF data repositories. The file 'data_model_parquet.md' is a codebook that contains data models for the four Parquet files.

R markdown files for:

• Downloading and cleaning data from the State of Open Data survey 2021
• Basic visualisations of responses to questions in the State of Open Data survey 2021
• HTML file of those visualisations.

Free text fields are included in the markdown but have been turned off for knitting and in the HTML file.

The data is a gridded dataset of global exposure (population, gross domestic product and net fixed asset value) at multiple spatial scales, spanning years 1850 to 2100 at annual resolution, including five future trajectories consistent with the Shared Socio-economic Pathways (SSPs).

Due to large file sizes, only a selection of resolutions and timesteps is provided in this repository: a spatial resolution of 30 arc seconds (approximately 0.93 km at the equator) and at 30 arc min (approximately 56km at the equator). The Python code and input data provided alongside the dataset enable users to generate different resolutions and timesteps as required by their research needs. See the description in documentation/ folder and the readme file in the code/ folder.

Note: the input data is divided into multiple zip files. They all need to be downloaded and unzipped together. In addition to the files here, running the model requires the following external datasets:

• selected Global Human Settlement Layer files (population and build-up surface at 30” resolution, 1975-2030, Total RES+NRES, WGS84 coordination system) from https://human-settlement.emergency.copernicus.eu/download.php
• HYDE 3.2 population count (‘popc’, decennial 1850 AD-1980 AD) in the ‘baseline’ scenario, https://geo.public.data.uu.nl/vault-hyde/HYDE%203.2%5B1710494848%5D/original/
• gridded population projections from Wang et al. (2022), https://doi.org/10.6084/m9.figshare.19608594.v2

The dataset is provided as Deliverable 3.1 of the European Union’s HORIZON project COMPASS

Locations of 5 RDRs: Dryad, EUDAT, Figshare, Zenodo, depositar .

If any corrections are required to this file, the latest version can be accessed via figshare.com: https://figshare.com/articles/NanoPUZZLES_list_of_evaluated_references/2015466 This file is a ZIP archive which contains a spreadsheet of evaluated references prepared within the NanoPUZZLES EU project [http://www.nanopuzzles.eu], along with a README file documenting this spreadsheet. Disclaimers: (1) this work has not undergone peer review (2) no endorsement by third parties should be inferred (3) You are strongly advised to read the "Important Caveats" section of the README file. In light of these key caveats, the annotations in this spreadsheet should only be taken to be indicative of the (meta)data availability corresponding to a given reference. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/ 2007-2013) under grant agreement no. 309837 (NanoPUZZLES project).

Initial conditions

Source Data

Bensasson_etalTableS1.xlsx: a table in excel format that summarizes the results of the growth tests from \citet{kurtzman_chapter_2011-16} applied to the three oak strains (NCYC 4144, NCYC 4145, and NCYC 4146) and to the type strain (NCYC 597). Species results from \citep{lachance_chapter_2011-5} are also summarized. For each test, the following abbreviations are used: +, positive; l, delayed positive; s, slow positive; w, weakly positive; -, negative; v, variable.Bensasson_etalSupp.pdf: A pdf file with Supplemental Results, Table S2 and Figures S1 to S8. The Supplemental Results summarize our validation of methods for determining ploidy.Bensasson_etalTableS3.tsv : a table in text format with tab separated values summarizing heterozygosity analyses for every strain. This includes exact counts of high quality heterozygous base calls (highQualityHetCount); the total length of high quality sequence (highQualityLength; bases with a phred-scaled quality score over 40); the proportion of high quality heterozygous sites; the length of regions that have undergone Loss of Heterozygosity (LOHlength) assessed in 100 kb windows; heterozygosity analysis after excluding LOH regions, centromeres and annotated repeats (annotationLohFilteredHetCount, annotationLohFilteredLength, annotationLohFilteredHeterozygosity); heterozygosity analysis after excluding LOH regions, centromeres and annotated repeats, and regions with more than double the expected read depth (depthFilteredHetCount, depthFilteredLength, depthFilteredHeterozygosity); heterozygosity analysis at 948,860 nucleotide sites that are common to all strains (sitesIn950kbHetCount, sitesIn950kbLength, sitesIn950kbHeterozygosity).Supplemental data in the form of whole chromosome alignments are also available from https://github.com/bensassonlab/data (DOI: 10.5281/zenodo.1488207). Perl scripts are available at https://github.com/bensassonlab/scripts (DOI: 10.5281/zenodo.1488147). The type strain and C. albicans strains isolated from oak are available from the National Collection of Yeast Cultures in the U.K.

Test_upload

Please check the original dataset on FigShare for accessing all files and for more information:

Anand, Shreyas; Dighe, Nachiket; Gupta, Pranshul; Alderliesten, Rene; Giovani Pereira Castro, Saullo (2025). Dataset for Glass Reinforced aluminum (GLARE) Laminates subjected to three-Point Bending. figshare. https://doi.org/10.6084/m9.figshare.29590073.v1

This dataset contains data for GLARE laminates subjected to 3-point bending loads. The dataset includes experimental data and data obtained from a proposed semi-analytical modeling approach for central cracking failure of GLARE laminates. The dataset aims to support the development and validation of analytical or numerical models for damage and energy absorption in GLARE fiber-metal laminates. Researchers working on crashworthiness and impact may find this data particularly useful.

The dataset was compiled from experimental testing conducted at TU Delft to characterize the bending response of various GLARE configurations. Each test corresponds to a unique combination of GLARE type, layer sequence, fiber orientation, and loading rate. The primary focus of the experimental campaign was to identify the effect of various parameters on failure mechanism and the energy absorption for various GLARE specimens.

This dataset is now maintained on Zenodo. See: https://zenodo.org/record/4020486

This table compares three recent studies focused on extracting and structuring metadata from scientific software into knowledge graphs. They use methods like metadata extraction frameworks, static code analysis, and data mining from repositories like GitHub, Zenodo, and figshare. Results include the creation of extensive knowledge graphs and structured scholarly knowledge, enhancing accessibility and reproducibility in scientific research.

deprecated

Dataset extracted from the source code of OpenJDK 8: http://openjdk.java.net/

This dataset is a breakdown in 4 different files of the dataset at: https://doi.org/10.5281/zenodo.579977

The dataset includes different kinds of triples: structural information extracted from source code, DBpedia links generated from javadoc comments, actual source code as literals and literal comments.

This fileset contains a preprint version of the conference paper (.pdf), presentation slides (as .pptx) and the dataset(s) and validation schema(s) for the IDCC 2019 (Melbourne) conference paper: The Red Queen in the Repository: metadata quality in an ever-changing environment. Datasets and schemas are in .xml, .xsd , Excel (.xlsx) and .csv (two files representing two different sheets in the .xslx -file). The validationSchemas.zip holds the additional validation schemas (.xsd), that were not found in the schemaLocations of the metadata xml-files to be validated. The schemas must all be placed in the same folder, and are to be used for validating the Dataverse dcterms records (with metadataDCT.xsd) and the Zenodo oai_datacite feeds respectively (schema.datacite.org_oai_oai-1.0_oai.xsd). In the latter case, a simpler way of doing it might be to replace the incorrect URL "http://schema.datacite.org/oai/oai-1.0/ oai_datacite.xsd" in the schemaLocation of these xml-files by the CORRECT: schemaLocation="http://schema.datacite.org/oai/oai-1.0/ http://schema.datacite.org/oai/oai-1.0/oai.xsd" as has been done already in the sample files here. The sample file folders testDVNcoll.zip (Dataverse), testFigColl.zip (Figshare) and testZenColl.zip (Zenodo) contain all the metadata files tested and validated that are registered in the spreadsheet with objectIDs. In the case of Zenodo, one original file feed, zen2018oai_datacite3orig-https%20_zenodo.org_oai2d%20verb=ListRecords%26metadata Prefix=oai_datacite%26from=2018-11-29%26until=2018-11-30.xml , is also supplied to show what was necessary to change in order to perform validation as indicated in the paper.

For Dataverse, a corrected version of a file, dvn2014ddi-27595Corr_https%20_dataverse.harvard.edu_api_datasets_export%20 exporter=ddi%26persistentId=doi%253A10.7910_DVN_27595Corr.xml , is also supplied in order to show the changes it would take to make the file validate without error.

The data of CESM2.

This archive provides consistent global datasets on Harmonized Land-use (HHLU), Biodiversity Intactness Index (BII), and Biodiversity Intactness Footprint (BIIFP) of Agricultural production from 2000 to 2020._Thank you for your interest in the dataset. Please note that this data archive has been permanently relocated from Figshare to Zenodo.The dataset is now hosted and maintained at https://doi.org/10.5281/zenodo.17608318_Relocation & License Notice:The relocation is to ensure that the dataset is correctly distributed under the license and terms of use of the PREDICT project, which is under the license of Creative Commons Attribution–NonCommercial–ShareAlike 4.0 International (CC BY-NC-SA 4.0).The Zenodo license now applies to all versions of this dataset, including any copies that users may have previously downloaded from Figshare.If you have already downloaded the data from Figshare, we ask you to ensure that your use of this dataset complies with the license terms specified on Zenodo (CC BY-NC-SA 4.0).For full license details, please visit: https://creativecommons.org/licenses/by-nc-sa/4.0/

The folder contains “Lithology” and “Distance to fault” data (https://doi.org/10.5281/zenodo.12629901).

Tabula Sapiens paper. The Tabula Sapiens Consortium, Science 376, eabl4896 (2022). Tabula Sapiens datasets. Before you use this data please see Tabula Sapiens' Data Release Policy available here. Tabula Sapiens figshare. Pisco, Angela; Consortium, Tabula Sapiens (2021): Tabula Sapiens Single-Cell Dataset. figshare. Dataset. https://doi.org/10.6084/m9.figshare.14267219.v4 {"references": ["The Tabula Sapiens Consortium, Science 376, eabl4896 (2022)"]}

GX Dataset downsampled - Experiment 1

The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.
Here the GX Dataset for Experiment 1 is downsampled to 1 kHz and saved in .MAT format which can be used in both MATLAB and Python.

Publication

A full data descriptor is published in Nature Scientific Data. Please cite this work as:

Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y

Descriptions

A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG, EOG), and continuous behavioral vigilance/alertness metrics.

Acknowledgments

Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499.

Extras

Back to Full GX Dataset : https://doi.org/10.5281/zenodo.4456079

For downsampled data (1 kHz ) please see (in .mat format):

• Experiment 1 : https://doi.org/10.5281/zenodo.3840615
• Experiment 2 : https://doi.org/10.5281/zenodo.3840617

Code used to import, process, and plot this dataset can be found here:

• GitHub : https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior

Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:

• PSD: https://doi.org/10.6084/m9.figshare.14810517.v1
• Topoplots During Stimulation: https://doi.org/10.6084/m9.figshare.14810478
• Voltage timeseries, spectrogram and behavior: https://doi.org/10.6084/m9.figshare.14810442.v1

The full dataset is also provided in BIDS format here:

• Data in BIDS format: https://doi.org/10.18112/openneuro.ds003670.v1.1.0

Data License
Creative Common 4.0 with attribution (CC BY 4.0)

NOTE

Please email ngebodh01@citymail.cuny.edu with any questions.

Follow @NigelGebodh for latest updates.