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99 results for “csv”
Zebrafish Pathway Metabolite MetFrag Local CSV
<p>This is a local CSV file of Zebrafish metabolites for MetFrag (https://msbi.ipb-halle.de/MetFrag/) extracted from PubChem, based partially on previous data extracted from Wikipathways, KEGG and literature (DOI: <a href="https://doi.org/10.1371/journal.pone.0213661">10.1371/journal.pone.0213661</a>), combined in previous versions of this record (DOI: <a href="https://doi.org/10.5281/zenodo.3541624">10.5281/zenodo.3541624</a>).</p> <p>This file was created as documented on the <a href="https://gitlab.com/uniluxembourg/lcsb/eci/pubchem-docs/-/tree/main/taxonomy/Danio_rerio">ECI GitLab</a>. </p> <p>This file is designed for identification using MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p> </p>
MetFrag Local CSV: CompTox (7 March 2019 release) MetaData File
<p>These are the CSV files that can be used as a local database in MetFrag (https://msbi.ipb-halle.de/MetFrag/), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, updated versions of these files are integrated in the web interface.</p> <p>This upload includes the SelectMetaData version of the CompTox MetFrag file from the 7 March 2019 release (DOI:<a href="https://doi.org/10.23645/epacomptox.7525199.v2">10.23645/epacomptox.7525199.v2</a>).</p>
Natural Products Atlas (NPAtlas) MetFrag Local CSV
<p>This is a local CSV file of the Natural Products Atlas (NPAtlas, <a href="https://www.npatlas.org/joomla/">https://www.npatlas.org/joomla/</a>) for MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>).</p> <p>Data was extracted to CSV from the TSV download from the NPAtlas <a href="https://www.npatlas.org/download">website</a>, with column headers for compulsory fields adjusted to fit the MetFrag format. Several entries with charged formulas (one +3, 7 +2, 125 +, 6 negative) had the charges removed from the formula to produce results consistent with other MetFrag files (where neutral formula is required; no adjustment for +/-H was performed so these remained consistent with the mass entries with minimum manipulation). Several overflowing lines were removed (due to new metadata) and NPA023832 was removed as "Ho" is not recognised by MetFrag. </p> <p>This file is for users wanting to integrate the latest NPAtlas into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p>Please credit the data source in any use of this file as the licence is CC-BY - details at <a href="https://www.npatlas.org/">https://www.npatlas.org/</a></p>
MetFrag Local CSV: CompTox (7 March 2019 release) Smoking MetaData File
<p>This is the CSV file that can be used as a local database in MetFrag (https://msbi.ipb-halle.de/MetFrag/), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, this is already integrated in the web interface.</p> <p>This file is based off the "SelectMetaData" CompTox MetFrag file from the 7 March 2019 release, available from:</p> <p><a>ftp://newftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/Chemistry_Dashboard/MetFrag_metadata_files</a></p> <p>The Smoking MetaData file contains the following fields, in addition to the regular (basic) CompTox data fields:</p> <p>- PubMedNeuro: the <a href="https://comptox.epa.gov/dashboard/chemical_lists/LITMINEDNEURO">LITMINEDNEURO</a> list with total PubMed reference counts in the column</p> <p>- <a href="https://comptox.epa.gov/dashboard/chemical_lists/CIGARETTES">CIGARETTES</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/INDOORCT16">INDOORCT16</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/SRM2585DUST">SRM2585DUST</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/SLTCHEMDB">SLTCHEMDB</a>, <a href="https://comptox.epa.gov/dashboard/chemical_lists/THSMOKE">THSMOKE</a> as suspect lists.</p> <p>First release (July 2019, not archived) contained a smaller subset of the SRM2585DUST list.</p>
WormJam Metabolites Local CSV for MetFrag
<p>This is a local CSV file of WormJam (https://www.tandfonline.com/doi/full/10.1080/21624054.2017.1373939) for MetFrag (https://msbi.ipb-halle.de/MetFrag/).</p> <p>The text file provided by Michael (also part of this dataset) was modified into CSV by adding identifiers and adjusting headers for MetFrag import. </p> <p>This CSV file is for users wanting to integrate WormJam into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p>Update 10 Sept 2019: curated truncated InChIKey, InChI entries, added missing SMILES, added DTXSIDs by InChIKey match.</p>
YMDB2.0 MetFrag Local CSV
<p>This is a local CSV file of YMDB 2.0 (http://www.ymdb.ca/) for MetFrag (https://msbi.ipb-halle.de/MetFrag/).</p> <p>Data was extracted to CSV from the SDF, with column headers for compulsory fields adjusted to fit the MetFrag format. One entry with no SMILES was filled in using the InChI in OpenBabel; entries with no monoisotopic mass or formula were filled in using functions in RChemMass (https://github.com/schymane/RChemMass/), finally one generic formula (row 746) was replaced with the formula from the InChI. </p> <p>This file is for users wanting to integrate the latest YMDB into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p>
MetFrag Local CSV: CompTox (7 March 2019 release) Wastewater MetaData File
<p>This is the CSV file that can be used as a local database in MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>), for those who wish to integrate this into the command line version.</p> <p>Note that this file is TOO LARGE to be uploaded via the web interface, this is already integrated in the web interface.</p> <p>This file is based off the "SelectMetaData" CompTox MetFrag file from the 7 March 2019 release, available from:</p> <p>ftp://newftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/Chemistry_Dashboard/MetFrag_metadata_files</p> <p>The Wastewater MetaData file contains the following fields, in addition to the regular (basic) CompTox data fields:</p> <p>Suspect Lists (1=presence, 0=absence):</p> <p>- ITNANTIBIOTIC, STOFFIDENT, REACH2017, ZINC15PHARMA and PFASMASTER</p> <p>Suspect Lists with scores from KEMI (see details on <a href="https://www.norman-network.com/nds/SLE/">NORMAN-SLE</a> and hyperlinks below):</p> <p>- <a href="https://zenodo.org/record/2628787">KEMIMARKET_EXPO</a>, <a href="https://zenodo.org/record/2628787">KEMIMARKET_HAZ</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_WDUIndex</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_StpSE</a>, <a href="https://zenodo.org/record/2653567">KEMIWW_SEHitsOverDL</a></p>
OpenCitations Meta CSV dataset of all bibliographic metadata
<p>This dataset contains all the bibliographic metadata (in CSV format) included in OpenCitations Meta. In particular, each line of the CSV file defines a bibliographic resource, and includes the following information:</p> <ul> <li><strong>[field "id"]</strong> the IDs for the document described within the line;</li> <li><strong>[field "title"]</strong> the document's title;</li> <li><strong>[field "author"]</strong> the authors of the document;</li> <li><strong>[field "pub_date"]</strong> the date of publication;</li> <li><strong>[field "venue"]</strong> information about the venue, i.e. the bibliographical resource to which the document belongs;</li> <li><strong>[field "volume"]</strong> the volume sequence identifier (e.g. a number) to which the entity belongs;</li> <li><strong>[field "issue"]</strong> the issuesequence identifier (e.g. a number) to which the entity belongs;</li> <li><strong>[field "page"]</strong> the page range of the resource described in the row;</li> <li><strong>[field "type"]</strong> the type of resource described in the row;</li> <li><strong>[field "publisher"]</strong> the entity responsible for making the resource available;</li> <li><strong>[field "editor"] </strong>the editors of the document.</li> </ul> <p>This version of the dataset contains:</p> <ul> <li>114,621,237 bibliographic entities</li> <li>298,847,794 authors and 2,465,711 editors (counted by their roles, without disambiguating individual</li> <li>711,711 publication venues</li> <li>241,783 publishers</li> </ul> <p>The zipped dataset weighs 11 GB, while, when extracted, it weighs 46 GB on an ext4 filesystem.</p> <p>Additional information about OpenCitations Meta at <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p>
ROSEWOOD4.0 Best practices & innovations: CSV file
<p>ROSEWOOD4.0 harnesses digital solutions and knowledge transfer along the forest value chain to reinforce the sustainability of forest resilience and wood mobilisation in Europe. This CSV file includes the complete information of 279 Factsheets of <em>Best practices and Innovations</em> (BP&I) in forest management, wood supply and forest-based industries exploiting relevant digital technologies and industry 4.0 solutions. All these BP&I were jointly identified and validated by the project partners.</p> <p>The BP&I factsheets are published in a <em>Knowledge Platform for Regional Forest Innovation</em>, which is an open, multilingual repository (currently 13 European languages) created by the consortium to enable the widest possible dissemination of results. Spreading this knowledge in Europe will help practitioners and professionals to gain a better understanding of how the digital transformation in forestry can improve sustainable forest management and ecosystem resilience and thus benefit a more competitive forest-based sector in rural regions.</p> <p>The platform is accessible at: https://www.forestinnovationhubs.rosewood-network.eu</p> <p> </p>
DFT Calculated xyz and log Files as well as csv Files for Machine Learning in Support of "Tailoring Phosphine Ligands for Improved C H Activation: Insights from Δ-Machine Learning"
<p>Transition metal complexes have played crucial roles in various homogeneous catalytic processes due to their exceptional versatility. This adaptability stems not only from the central metal ions but also from the vast array of choices of the ligand spheres, which form an enormously large chemical space. For example, Rh complexes, with a well-designed ligand sphere, are known to be efficient in catalyzing the C-H activation process in alkanes. To investigate the structure-property relation of the Rh complex and identify the optimal ligand that minimizes the calculated reaction energy ΔE of an alkane C-H activation, we have applied a Δ-Machine Learning method trained on various features to study 1,743 pairs of reactants (Rh(PLP)(Cl)(CO)) and intermediates (Rh(PLP)(Cl)(CO)(H)(propyl)). Our findings demonstrate that the models exhibit robust predictive performance when trained on features derived from electron density (R<sup>2 </sup>= 0.816), and SOAPs (R<sup>2 </sup>= 0.819), a set of position-based descriptors. Leveraging the model trained on xTB-SOAPs that only depend on the xTB-equilibrium structures, we propose an efficient and accurate screening procedure to explore the extensive chemical space of bisphosphine ligands. By applying this screening procedure, <a>we identify ten newly selected reactant-intermediate pairs with an average ΔE </a>of 33.2 kJ mol<sup>-1</sup>, remarkably lower than the average ΔE of the original data set of 68.0 kJ mol<sup>-1</sup>. This underscores the efficacy of our screening procedure in pinpointing structures with significantly lower energy levels.</p> <p>_______________________________________________________________________</p> <p>The dataset contains three file types:</p> <p>Version 1.0:</p> <ol> <li>xyz files of the final optimized Rh-phosphine complexes; one set for the starting materials denoted as "molecule-XXXX_4-times" and one set for the intermediates after C-H activation denoted as "molecule-XXXX_6-times"</li> <li>Gaussian16 log files for the optimization process; one set for the starting materials denoted as "molecule-XXXX_4-times" and one set for the intermediates after C-H activation denoted as "molecule-XXXX_6-times"</li> <li>csv files containing the per molecule features used for training the different machine learning models. The name of the csv files indicates which property was predicted and which model was used</li> </ol> <p>New in version 1.1 (other data is unchanged):</p> <ol> <li>Gaussian16 log files for the ten newly identified bisphosphine ligands; one set for the product material denoted as "LXX_6-times-axial" and one set for the transition state for the C-H activation denoted as "LXX_C-H-activation_TS"</li> </ol>
CSV equivalent of LOFAR ACC files
<p>These are conversions of LOFAR ACC files by Griffin Foster from <a href="https://zenodo.org/record/840405/files/20120513_052251_acc_512x192x192.dat">https://zenodo.org/record/840405</a> to demonstrate the use of the ACC to CSV converter developed at DIAS</p>
HMDB4.0 MetFrag Local CSV
<p>This is a local CSV file of HMDB4.0 (http://www.hmdb.ca/) for MetFrag (https://msbi.ipb-halle.de/MetFrag/).</p> <p>Data was extracted from the XML, metals and entries with no monoisotopic mass were removed, one naming error for http://www.hmdb.ca/metabolites/HMDB0037436 was fixed and the XML fields adjusted to headers for MetFrag import. </p> <p>This file is for users wanting to integrate the latest HMDB into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p>The two versions are identical, the two names fit various formatting conventions used behind the scenes in MetFragWeb.</p>
Blood Exposome Database: MetFrag Local CSV
<p>This is a local CSV file of the Blood Exposome Database (<a href="http://bloodexposome.org/">http://bloodexposome.org/</a>) for MetFrag (<a href="https://msbi.ipb-halle.de/MetFrag/">https://msbi.ipb-halle.de/MetFrag/</a>).</p> <p>Data was trimmed to necessary columns from parent-mapped TSV provided by Dinesh Barupal. Approx. 15 entries containing elements not processed by MetFrag were removed. Entries with singly charged formulas had the charge removed from the formula to produce results consistent with other MetFrag files (where neutral formula is required; no adjustment for +/-H was performed so these remained consistent with the mass entries with minimum manipulation - see xlsx file for traceback). Subsequent versions could be adjusted for different behaviour if desired. </p> <p>This file is for users wanting to integrate the latest Blood Exposome Database into MetFrag CL workflows (offline), this file will be integrated into MetFrag online; please use the file in the dropdown menu rather than uploading this one.</p> <p>Please credit the data source in any use of this file as the licence is CC-BY: <a href="http://bloodexposome.org/">http://bloodexposome.org/</a></p>
Trait records sample: Carnivora traits from traitdb (ZIP of CSV files)
This file is part of an API design process and is placed here for internal review. It contains trait records for Carnivora, and neighboring files.<p></p>This is going to similar to the previous set of CSV files, but with fewer tables and fewer columns, since it is derived from the neo4j traitdb instead of the whole back-end SQL database
Model quality assessment: Results of human ratings, prompts, models as .csv and answers of the LLM
<p>This is the dataset for the paper "<a href="https://doi.org/10.1007/978-3-031-77908-4_7" target="_blank" rel="noopener">Assessing Model Quality Using Large Language Models</a>" and contains the following files of the assessment of the model quality:</p> <ul> <li>Results of the human ratings</li> <li>Prompts</li> <li>Models as .csv</li> <li>Answers of the LLM</li> </ul> <p>The graphical representation of the models is linked under Related works.</p>
Energy Climate dataset consitent with ENTSO-E TYNDP2020 studies (CSV & NetCDF) for ACDC-ESM
<p><strong>Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format</strong></p> <p><strong>TL;DR</strong>: this is a tidy and friendly version of a recreation of ENTSO-E's PECD 2021.3 data by using ERA5: hourly capacity factors for wind onshore, offshore, solar PV and hourly electricity demand are provided. All the data is provided for 28-71 climatic years (1950-2020 for wind and solar, 1982-2010 for demand).</p> <p><strong>Description</strong><br> Country averages of energy-climate variables generated using the Python scripts, based on the <a href="https://2020.entsos-tyndp-scenarios.eu/">ENTSO-E's TYNDP 2020 study</a>. For the following scenario's data is available</p> <ul> <li>National trends 2025 (NT 2025)</li> <li>National trends 2030 (NT 2030)</li> <li>National trends 2040 (NT 2040)</li> <li>Distributed Energy 2030 (DE 2030)</li> <li>Distributed Energy 2040 (DE 2040)</li> <li>Global Ambitions (GA 2030)</li> <li>Global Ambitions (GA 2040)</li> </ul> <p>The time-series are at hourly resolution and the included variables are:</p> <ul> <li>Generation wind offshore (aggregated for all years per scenario in a .zip)</li> <li>Generation wind onshore (aggregated for all years per scenario in a .zip)</li> <li>Generation solar photovoltaic (aggregated for all years per scenario in a .zip)</li> <li>Total energy demand (all zones combined in single file per scenario)</li> </ul> <p>The Files are provided in CSV (.csv) & NetCDF (.nc). The data is given per ENTSO-E's bidding zone as used within the TYNDP2020.<br> </p> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. </em></p>
COCONUT Database January2022 version CSV
<p>This is a COCONUT dump in the form of CSV format, converted from the SDF file available on the COCONUT website. </p>
CSV Trip Advisor restaurant NYC test
<p>The CSV distribution of the Trip Advisor Newyork City restaurants Dataset used in the NeIC spring 2023 FAIR training.</p>
Waverinet: training CSV
<p>Original dataframe used to train Waverinet, exported as a CSV file. The data must be checked for corruption.</p>
CSV files and R script: writing process data of typed picture description by 15 cognitively impaired patients and 15 healthy controls
<p>Writing process data of 15 cognitively impaired patients and 15 age- and gender-matched healthy controls were obtained. Each of them completed two typed picture description tasks that were logged with Inputlog, a keystroke logging tool. Variables included time on task; number of characters, pauses and Pause-bursts per minute; proportion of pause time; duration of Pause-bursts; and pause time between words. For pause time between words, also the effect of pauses preceeding specific word categories was analyzed.</p> <p>The data were used to explore if the observation of writing behavior can assist in the screening and follow-up of mild cognitive impairment (MCI) and mild dementia due to Alzheimer’s disease (AD). This data set contains the CSV files that were used for the analyses and the corresponding R script.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.