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311 results for “new dataset”
A new ChEMBL dataset for FastTargetPred and target fishing for an exhaustive list of linear tetrapeptides
<p>A ChEMBL-v29 dataset was generated to be used with the ligand-based similarity search target prediction engine FastTargetPred (https://github.com/ludovicchaput/FastTargetPred). Using this new dataset, attempts to predict macromolecular targets for a published dataset of 160,000 tetrapeptides was performed.</p> <p>The dbchembl29 directory contains all the files for FastTargetPred. This command line tool compares using different types of fingerprints, a file containing small query molecules in SDF format (it can be 1 molecule or a collection) to molecules extracted from ChEMBL29. If a match is found, this suggests that your query molecule is similar to a ChEMBL compound and as the ChEMBL compound has bioactivity data against one or more macromolecular target, then this suggests that your query compound could bind to targets that interact with compounds that are similar to the query molecule. The so-called similarity principle in chemistry. Fingerprints are computed with http://www.mayachemtools.org/, a collection of Perl and Python scripts for Chemoinformatics and (Structural) Bioinformatics.</p> <p>To run FastTargetPred with the new ChEMBL 29 data, you just need to unzip the dbchembl29 directory into the FastTargetPred main directory.</p> <p>The default FastTargetPred commands (e.g., python3 FastTargetPred.py rivaroxaban.sdf, the default command uses ECFP4 fingerprints and a Tanimoto coef of 0.6, rivaroxaban here is the query compound, it is provided in the extra_data directory) will use the data present in the default db directory and thus a curated version of ChEMBL-25 release. It was the version of the ChEMBL database available when FastTargetPred was developed. Since then, many new molecules have been added and this is why we generated the ChEMBL-29 dataset (last ChEMBL version at the time of writing).</p> <p>To use the new ChEMBL-29 data, you can run the following command:</p> <p>python3 FastTargetPred.py rivaroxaban.sdf -fp MACCS -tc 0.9 -db dbchembl29/chembl29_active</p> <p>This applies a similarity search for the query compound (here rivaroxaban, you can for instance move this SDF file in the directory containing the file FastTargetPred.py) using MACCS fingerprints, a Tanimoto coefficient threshold of 0.9 and the -db option forces the system to look at the ChEMBL29 curated data and not the default ChEMBL-25 data. This 0.9 value means to focus on molecules very similar to rivaroxaban present in the ChEMBL data. If one is looking for more distantly related compounds, then a value of 0.7 can be used. Users can try different values or try consensus scoring...See FastTargetPred: a program enabling the fast prediction of putative protein targets for input chemical databases. Chaput et al., Bioinformatics. 2020 Aug 15;36(14):4225-4226</p> <p>The chembl29 directory also contains 714,780 compounds (canonical SMILES strings) extracted from ChEMBL29 that have bioactivity data. Fingerprints could not be computed for 19 molecules that have unusual chemistry. We selected the following thresholds (eg, binding assays, activity against targets less than 20 micro-molar, ChEMBL confidence_score = 6 or above, maximum = 9).</p> <p>With this new dataset, we attempted to predict potential targets with FastTargetPred for 160,000 input query peptides (4 amino acids, combination should be 20 x 20 x 20 x 20) previously reported by Dewi Prasasty and Perdana Istyastono, Data in brief 27 (2019) 104607. The peptides for which a putative target was predicted are shown in two DataWarrior files with the amino acid sequence of the query peptide, the compounds found to be similar in the ChEMBL29 dataset (fingerprints = ECFP4, Tanimoto 0.6) and thus the compound IDs, the target ChEMBL IDs, mapping to the UniProt database when available, information about disease involvements, Reactome pathway database identifiers. These two DataWarrior files are searchable, can be sorted and hyperlinks to the ChEMBL, UniProt and Reactome databases have been inserted.</p> <p> </p>
Dataset pertaining to paper "New insights on the radiative impacts of ozone-depleting substances"
<p>Focusing on the period 1955-2005, we here present results from offline radiative forcing (RF) calculations from a global chemistry climate model. Using realistic distributions of ODS and consistent stratospheric ozone, we show that ODS dominate the adjusted stratospheric warming of the lower stratosphere, where CO2 has little radiative impact. We also show that the global mean RF of stratospheric ozone only cancels a fraction of the RF of ODS, leaving an important ODS contribution to anthropogenic forcing. Finally we show that the RF of ODS opposes Arctic amplification, its equator-to-pole gradient being larger than the one of CO2.</p> <p>This data-set consists of the output fields including temperature adjustment and radiative forcing from offline experiments using the NCAR CESM-PORT model.</p>
A new remote sensing benchmark dataset for machine learning applications : MultiSenGE
<p>[UPDATE] You can now access MultiSen (GE and NA) collection though this portal : <a href="https://doi.theia.data-terra.org/ai4lcc/?lang=en">https://doi.theia.data-terra.org/ai4lcc/?lang=en</a></p> <p>MultiSenGE is a new large-scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. It contains 8,157 patches of 256 * 256 pixels for Sentinel-2 L2A, Sentinel-1 GRD and a regional LULC topographic regional database. </p> <p>Every file has a specific nomenclature :</p> <ul> <li>Sentinel-1 patches: {tile}_{date}_S1_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>Sentinel-2 patches: {tile}_{date}_S2_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>Ground reference patches: {tile}_GR_{x-pixel-coordinate}_{y-pixel-coordinate}.tif</li> <li>JSON Labels: {tile}_{x-pixel-coordinate}_{y-pixel-coordinate}.json</li> </ul> <p>where <em>tile</em> is the Sentinel-2 tile number, <em>date</em> the date of acquisition of the patch, <em>x-pixel-coordinate</em> and <em>y-pixel-coordinate</em> are the coordinates of the patch in the tile.</p> <p>In addition, you can find a set of useful python tools for extracting information about the dataset on Github : <a href="https://github.com/r-wenger/MultiSenGE-Tools">https://github.com/r-wenger/MultiSenGE-Tools</a></p> <p>First experiments based on this <em>dataset</em> is in press in ISPRS Annals : <strong>Wenger, R., </strong>Puissant, A., Weber, J., Idoumghar, L., and Forestier, G.: MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 635–640, https://doi.org/10.5194/isprs-annals-V-3-2022-635-2022, 2022.</p> <p>Due to the large size of the dataset, you will only find the associated JSON files on this Zenodo repository. To download the Sentinel-1, Sentinel-2 patches and the reference data, please do so via these links: </p> <ul> <li>Sentinel-1 temporal serie patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/s1.tgz</a></li> <li>Sentinel-2 temporal serie patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/s2.tgz</a></li> <li>Ground reference patches: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/ground_reference.tgz</a></li> <li>JSON files for each patch: <a href="https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz">https://s3.unistra.fr/a2s_datasets/MultiSenGE/labels.tgz</a></li> </ul>
Dataset for Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing
<p>This dataset includes one year long measurements of particle number size distributions, chemical composition of PM2.5, gaseous precursors, and meteorological parameters in urban Beijing, China, from March 1, 2018, to March 1, 2019. It is the supplementary data for "Influence of Aerosol Chemical Composition on Condensation Sink Efficiency and New Particle Formation in Beijing", which is published by Environmental Science & Technology Letter. Please cite: Wei Du, Jing Cai, Feixue Zheng, Chao Yan, Ying Zhou, Yishuo Guo, Biwu Chu, Lei Yao, Liine M. Heikkinen, Xiaolong Fan, Yonghong Wang, Runlong Cai, Simo Hakala, Tommy Chan, Jenni Kontkanen, Santeri Tuovinen, Tuukka Petäjä, Juha Kangasluoma, Federico Bianchi, Pauli Paasonen, Yele Sun, Veli-Matti Kerminen, Yongchun Liu, Kaspar R. Daellenbach, Lubna Dada, and Markku Kulmala Environmental Science & Technology Letters Article ASAP DOI: 10.1021/acs.estlett.2c00159</p>
TocoDecoy: a new approach to design unbiased datasets for training and benchmarking machine-learning scoring functions
<p>This dataset file contains TocoDecoy datasets generated based on the targets and active ligands of LIT-PCBA.</p> <p>1_property_filtered.zip :</p> <ul> <li>TD set: the ligand file name, 2D T-sne vectors, Smiles, molecular weight (MW), Wildman-Crippen partition coefficient (log P), number of rotatable bonds (RB), number of hydrogen-bond acceptors (HBA), number of hydrogen-bond donors (HBD), number of halogens (HAL), topology similarities of decoys to the seed active ligands, active label (active or inactive) and training set label (whether belongs to training set or test set) <strong>OF active ligands and their topologically dissimilar decoys</strong></li> <li>CD set: the decoy conformations with low docking scores generated by docking active ligands into protein pockets using Glide, Schrödinger.</li> </ul> <p> </p>
ReSpa - Towards an automatic requirements classification in a new Spanish dataset
<p>ReSpa (Spanish Dataset for requirements classification) dataset is conformed by requirements collected from final degree projects from one University. It was presented in the paper 'Towards an automatic requirements classification in a new Spanish dataset' and used also in the paper 'Requirements Classification Using FastText and BETO in Spanish Documents'.</p> <p>Cited as:</p> <p>Limaylla-Lunarejo, M. I., Condori-Fernandez, N., & Luaces, M. R. (2022, August). Towards an automatic requirements classification in a new Spanish dataset. In <em>2022 IEEE 30th International Requirements Engineering Conference (RE)</em> (pp. 270-271). IEEE. https://doi.org/10.1109/RE54965.2022.00039</p> <p> </p> <p> </p> <p> </p> <p> </p>
A dataset of anonymised hospitalised COVID-19 patient data: outcomes, demographics and biomarker measurements for two New York hospitals
<p>These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State<br> University of New York (SUNY) Downstate Health Sciences University and Maimonides<br> Medical Center.</p> <p>The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.</p> <p>The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.</p> <p><strong>Study approval and data collection</strong></p> <p>Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB\#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB\#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.</p>
Survey Data on New Genomic Techniques (Dataset)
<table> <tbody> <tr> <td>This survey by EFSA provides insights in terms of:<br> • Europeans’ concerns regarding food, and interest in several food safety topics.<br> • Europeans’ knowledge and perception of new genomic techniques (NGTs), including awareness of NGTs, which NGT-related information evokes most interest, perceived effects on the environment, health, etc. of the application of NGTs to food, among others.<br> <br> The survey was implemented by the Teleperformance in 24 member states (i.e. all EU27 countries except Cyprus, Luxembourg, and Malta) plus Norway between 17th and 19th of November 2021. A total of 8,900 respondents from different social and demographic groups completed the survey online in their mother tongue, with 300 to 500 respondents per country. These sample sizes provide robust results and ensure that responses are representative in each of the countries to be surveyed.<br> <br> The sample was nationally representative with respect to age and gender. Other demographic information collected included education, among others.</td> </tr> </tbody> </table>
NewsQuote Dataset: Quote-Speaker Pairs from New Articles
<p>To seek reliable information sources for news events, we introduce a novel task of expert recommendation, which aims to identify trustworthy sources based on their previously quoted statements. To achieve this, we built a novel dataset, called NewsQuote, consisting of 23,571 quote-speaker pairs sourced from a collection of news articles.</p> <p>We built our NewsQuote dataset from the <a href="https://aylien.com/resources/datasets/coronavirus-dataset">AYLIEN coronavirus dataset</a>, which contains news articales published between November 2019 and August 2020. Apart from text, each article is also accompanied with the meta data such as authors, keywords, summary, source, publishing time, topical categories coded by both the Interactive Advertising Bureau (IAB) taxonomy and the IPTC NewsCodes, as well as the recognized entities and entity links from the DBpedia.</p> <p>Our data covers three categories of quotes: direct quote, indirect quote and mixed quote. We roughly estimated the weight of each quotation type on the dataset by the number and position of quotation marks: 81% for indirect quotes, 11% for direct quotes, and 7% for mixed quotes. In the test set, there are 1,582 (79%) indirect quotes, 178 (9%) mixed quotes, and 240 direct quotes (12%). The following table shows the statistics of our final NewsQuote dataset. In summary, we have a total of 23,571 English speaker-quote pairs with 2,843 speakers from 263 global domains.</p> <p>For each sample in the dataset:</p> <ul> <li>'ID': sample id. In the id, number before '-' is the corresponding article id in the <a href="https://aylien.com/resources/datasets/coronavirus-dataset">AYLIEN coronavirus dataset</a></li> <li>'Sentence': main sentence.</li> <li>'Tags': semantic role labels.</li> <li>'Left_sentence': context before the sentence.</li> <li>'Right_sentence': context after the sentence.</li> <li>'Speaker': speaker.</li> <li>'Words': words in the sentence.</li> <li>'Verb': trigger verb that indicates the quotation.</li> <li>'Quotation1': possible quotation.</li> <li>'Time': publish time.</li> <li> <div> <div>'Entity_link': speaker's Dbpedia link.</div> <div> </div> </div> </li> </ul> <p> </p>
Dataset: Sunrise New Energy Co., Ltd. (EPOW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: iShares MSCI New Zealand ETF (ENZL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Western New England Bancorp, Inc. (WNEB) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: New York Mortgage Trust, Inc. (NYMTL) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: New York Mortgage Trust, Inc. (NYMT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: New York Mortgage Trust, Inc. (NYMTZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: New York Mortgage Trust, Inc. (NYMTM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: New York Mortgage Trust, Inc. (NYMTN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: New Providence Acquisition Corp. II (NPAB) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: New Mountain Finance Corporation 8.250% Notes due 2028 (NMFCZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: New Mountain Finance Corporation (NMFC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.