Skip to main content
Powered by ShareScore

Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

6,032

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

6,032 results for “Liking”

Learn how ShareScore rates datasets ↗
OpenNeuro52/100

Think Like an Expert

Open the record for dataset details and reuse information.

openCC0Jan 2020View details →
zenodo52/100

Human Kino-Dynamic Measurements Dataset for Factory-like Activities

<p>This dataset was created as a part of the study presented in IEEE Transactions on Human-Machine Systems with the title &quot;An Online Multi-Index Approach to Human Ergonomics Assessment in the Workplace&quot; by Marta Lorenzini, Wansoo Kim and Arash Ajoudani. This paper introduces an online approach to monitor kinematic and dynamic quantities on the workers, providing on the spot an estimate of the physical load required in their daily jobs. A set of ergonomic indexes is defined to account for multiple potential contributors to work-related musculoskeletal disorders (WMSDs), which remain one of the major occupational safety and health problems in the European Union nowadays. Thus, the continuous tracking of workers&rsquo; exposure to the factors that may contribute to their development is paramount. To evaluate the proposed framework, a throughout experimental analysis was conducted.</p> <p>Twelve healthy adult subjects were recruited in the experimental study to perform, in the laboratory settings, occupational activities that are commonly carried out by workers in the current industrial scenario. Three tasks were selected to encompass the most significant risk factors in the workplace: mechanical overloading of the body joints, variable and high-intensity interaction forces, and repetitive and monotonous movements. Accordingly, lifting/lowering of a heavy object, drilling, and painting with a lightweight tool were considered, respectively, in this study. While the subjects were carrying out such activities, the data regarding the whole-body motion and the forces exchanged with the environment (both ground reaction force (GRF) and interaction forces at the end-effector) were collected. In addition, ten surface electromyography (sEMG) sensors were placed on the body of each subject to measure muscle activity as a reference to the effective physical effort required for the tasks.</p> <p>The whole experimental procedure was carried out in accordance with the Declaration of Helsinki and the protocol was approved by the ethics committee azienda sanitaria locale (ASL) Genovese N.3 (Protocol IIT_HRII_ERGOLEAN 156/2020).</p>

opencc-by-4.0Oct 2021View details →
edi52/100

Examination of protein-like fluorophores in chromophoric dissolved organic matter (CDOM) in a wetland and coastal environment for the wet and dry seasons of the years 2002 and 2003 (FCE)

Water samples are collected at the end of the dry and the wet season from all LTER sites and stored on ice until return to the lab. They are pre-filtered through pre-combusted GF/F filters and ultrafiltered and concentrated with a Pellicon 2 Mini tangential flow ultrafiltration system.Concentrated samples were then analyzed using fluorescence and SEC-HPLC. This CDOM optical study revealed the presence of two classes of compounds associated with the protein-like peak (peak T; excitation/emission (Ex/Em) maxima at around 280 nm/325 nm), which have very different chemical structures and ecological roles. In addition to proteins, we propose phenolic compounds as possible origins of peak T in coastal and wetland environments. In this study, natural water samples were obtained from subtropical rivers and estuarine environments within the Florida Coastal Everglades (FCE) ecosystem. The samples were ultra-filtered and excitation-emission fluorescence matrices (EEMs) were obtained. The EEMs showed the presence of four peaks with Ex/Em maxima at around 280 nm/325 nm (T), less than 260 nm/460 nm (A), 300 nm/412nm (M), and 350 nm/470 nm (C). To better understand the nature of peak T, the components originating this peak were separated using size exclusion chromatography (SEC) and detected by fluorescence emission at Ex/Em = 280 nm/325 nm. The elution curves revealed the presence of two elution peaks at a molecular weight of greater than 50K (void volume; T1) and around 7.6K (T2). This result suggested the need of cautious interpretation in the use of peak T as a proxy for the detection of proteinaceous materials in wetland and estuarine environments, since significant amounts of potentially interfering phenolic compounds are leached from senescent biomass in wetland and coastal ecosystems. As such EEM spectra of gallic acid an important component of hydrolysable tannins, and condensed tannins extracted from red mangroves (Rhizophora mangle) showed the presence of a peak maxima

openCC (other)Feb 2024View details →
OpenNeuro48/100

Individual Differences in Fluid Reasoning and RAPM-like Problem Solving

Open the record for dataset details and reuse information.

openCC0Jan 2020View details →
zenodo48/100

Global monthly catch of tuna, tuna-like and shark species (1950-2023) by 1° or 5° squares (IRD level 2) - and efforts level 0 (1950-2023)

<div>&nbsp;</div> <p>This deposit contains various datasets describing tuna fisheries activities (currently catches and efforts) and different levels of processing on 1&deg; or 5&deg; spatial grids with a monthly temporal resolution.</p>

opencc-by-nc-4.0Oct 2024View details →
zenodo48/100

Supplementary data to: "A European Monsoon-like climate in a Warmhouse World"

<p>Supplementary information belonging to the manuscript titled "A European Monsoon-like climate in a Warmhouse World" by Nick van Horebeek and colleagues, containing the following information:</p> <ul> <li>"Campanile_D47_sample_data_calc.csv" - A file containing all clumped isotope measurements carried out for this study</li> <li>"Campanile_D47_season_data_calc.xlsx" - A file containing seasonal means and uncertainties of temperature and d18Osw based on clumped isotope measurements carried out for this study</li> <li>"Campanile_d18O_season_data_calc.csv" - A file containing all incrementally sampled oxygen and carbon isotope values with seasonal characterization.</li> <li>"Intra-growthline_variability_edit.png" - An image showing the variability in d18O values repeatedly measured in the same location in the shell</li> <li>"Campanile_Winter_growth_stop_images.zip" - A folder containing all shell images used in the publication</li> <li>"Campanile_Data_figure_S1.xlsx" - A file containing the dataset needed to produce the supplementary figure showing variability in oxygen isotope values (S1).</li> <li>"SI_Campanile_d18O_d13C_depth_rev1.png" - A plot showing the variability in d18O and d13C values along the shell</li> <li>"Campanile_clumped_season_plot_rev3.r" - Script used to process clumped isotope data for seasonal statistics and plotting</li> <li>"Campanile_clumped_d18O_plots_rev2.r" - Script used to process and plot seasonal statistics and isotope data + uncertainty against shell age.</li> </ul>

opencc-by-4.0Nov 2024View details →
zenodo48/100

Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules

<p>Open challenges in computational drug design include the understanding and accurate description of solvent effects as well as collective dispersion interactions for realistic drug-like molecules. Both interactions profoundly influence the conformational stability of drug molecules and, consequently, the determination of other important quantum-mechanical (QM) observables. In this context, we here introduce the Aquamarine (AQM) dataset -- an extensive QM dataset that contains the structural and electronic information -- of 59,786 low-and high-energy conformers of 1,653 molecules containing up to 54 non-hydrogen atoms (including &nbsp;C, N, O, F, P, S and Cl). To gain insights into the solvent effects, we have carried out QM calculations of structures and properties in gas phase and in an aqueous solution modeled with implicit solvent. AQM contains over 40 global (molecular) and local (atom-in-a-molecule) physicochemical properties (including ground-state and response properties) per molecular structure computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD supplemented with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By treating both molecule-solvent and dispersion interactions, the AQM dataset can help understand the impact of both interactions in structure-property and property-property relationships of realistic drug-like molecules. Therefore, we propose the AQM dataset as a &nbsp;benchmark for current state-of-the-art machine learning methods for property prediction as well as for the <em>de novo</em> generation of large and flexible (solvated) molecules with pharmaceutical and biological relevance.</p>

opencc-by-4.0Jun 2024View details →
zenodo48/100

FASTA file containing the MYB encoding gene An2-like genomic sequences corresponding to wild and cultivated tomato accessions

<p>FASTA sequence corresponds&nbsp;to the MYB encoding gene&nbsp;<em>An2-like</em>. The genomic&nbsp;sequences correspond to&nbsp;<em>Solanum&nbsp;galagpagnese</em> accession LA1141 (this study), <em>S.&nbsp;lycopersicum</em> variety OH8245 (this study), <em>S. lycopersicum</em> variety Heinz 1706 reference genome (Hosmani et al., 2019),&nbsp;<em>S. lycopersicum </em>variety Indigo Rose (Yan et al., 2020), <em>S. lycopersicum</em> accession LA1996 [MN242011.1&nbsp;(Colanero et al., 2020)], <em>S. chilense&nbsp;</em>accession LA1930 [MN242012.1 (Colanero et al., 2020)], and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014).&nbsp;Local sequences databases were made and retrieved using BLAST version/2018-08 for 84 accessions from The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences corresponding to Heinz 1706 (Hosmani et al., 2018), &nbsp;Indigo Rose [MN433087 (Yan et al., 2020)], <em>S. lycopersicum </em>accession LA1996 [MN242011.1, EF433417.1 (Sapir et al., 2008; Colanero et al., 2020)], <em>S. chilense</em> accession LA1930 [MN242012.1 (Colanero et al., 2020)] were accessed using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at <a href="https://solgenomics.net/tools/blast/">https://solgenomics.net/tools/blast/</a>)&nbsp;and&nbsp;the National Center for Biotechnology Information (NCBI)(available at NCBI: <a href="https://www.ncbi.nlm.nih.gov">https://www.ncbi.nlm.nih.gov</a>).</p>

opencc-by-4.0Nov 2021View details →
zenodo48/100

Humic acid like concentration in seawater samples, collected from the trace metal rosettes in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.

<p><strong>Dataset abstract</strong></p> <p>Humic acid like concentration (abbreviated HA) measured with respect to the Suwannee River Fulvic acid standards (&micro;mol SRFA equivalent per litre).</p> <p>Seawater samples were collected from trace metal rosette (TMR) deployments at different depths in the water column during the Antarctic Circumnavigation Expedition (ACE). Humic acid like data from legs 1 and 2, from TMR cast numbers 3 to 16, were analysed by electrochemistry following standard additions of Suwannee River Fulvic Acid (standard 1, IHSS). This data is to support iron ligands and iron bioavailability as well as hydrolysable saccharides (TPZT) data, also collected during ACE.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_humics_data.csv, data file, comma-separated values</li> <li>ace_humics_data_visual_summary.png, metadata, portable network graphics</li> <li>data_file_header.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This humics dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>

opencc-by-4.0Dec 2021View details →
zenodo48/100

Data from: "Alteration of the gut microbiota's composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters"

<p>This dataset contains all data collected and used for the publication : &quot;Alteration of the gut microbiota&rsquo;s composition and metabolic output correlates with COVID-19-like severity in obese NASH hamsters&quot;. Besides the Readme, it contains 11 files.</p> <p><br> Excel files with classification (i.e. genes according to their fold induction or repression) are provided. Data include different conditions with varying number of samples per group. Data are structured according to employed methods and then stratify the data obtained within the individual work packages.</p>

opencc-by-4.0Mar 2022View details →
zenodo48/100

SH3-like domain from Penicillium virgatum muramidase: X-ray diffraction images

<p>This submission includes a zip archive of diffraction images recorded with the ADSC QUANTUM 315 CCD detector at the DIAMOND beamline I04 on 2017-06-27. The model of the crystal structure and associated information can be found in the Protein Data Bank entry 8B2G. This is a case of crystal twinning. The data are used in CCP4 Tutorials.</p>

opencc-by-4.0May 2024View details →
zenodo48/100

S48 | CPPDBLISTA | Database of Chemicals likely (List A) associated with Plastic Packaging (CPPdb)

<p>This is the collection associated with list S48 CPPDBLISTA on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S48 | CPPDBLISTA | <strong>Database of Chemicals associated with Plastic Packaging (CPPdb)</strong></p> <p>CPPdb Original File (List A and B) <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListA_ListB_181009_ZenodoV1.xlsx">XLSX</a> (06/03/2019)<br> Mapped Files (06/03/2019):<br> Table 2 from Groh et al as <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/Table2_Groh_etal_stoten_mapped.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/Table2_Groh_etal_stoten_mapped.csv">CSV</a>&nbsp;<br> CPPdb List A <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListA_Mapped_06032019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListA_Mapped_06032019.csv">CSV</a>&nbsp;<br> CPPdb List B <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListB_Mapped_06032019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListB_Mapped_06032019.csv">CSV</a></p> <p>Table 2 Groh et al. <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/Table2_Groh_etal_InChIKeys.txt">InChIKeys</a><br> CPPdb List A <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListA_InChIKeys.txt">InChIKeys</a><br> CPPdb List B <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/220319Update/CPPdb_ListB_InChIKeys.txt">InChIKeys</a><br> (all 06/03/2019)</p> <p>A database of chemicals likely (List A, 903) and possibly (List B, 3353 - in another upload) associated with plastic packaging, with hazard data, from Groh et al 2019 DOI: <a href="https://doi.org/10.1016/j.scitotenv.2018.10.015">10.1016/j.scitotenv.2018.10.015</a>. Mapped to structures by CAS/Name by K. Groh &amp; E. Schymanski.</p> <p>Latest version of original data (last update Oct 2018): DOI: <a href="http://doi.org/10.5281/zenodo.1287773">10.5281/zenodo.1287773</a></p> <p>&nbsp;</p>

opencc-by-4.0Mar 2019View details →
zenodo48/100

Dataset for Evaluation of a novel microfluidic chip-like device for purifying bovine frozen-thawed semen for in vitro fertilization

<p>VetCount<sup>TM</sup> Harvester (MotilityCount ApS, Copenhagen, Denmark) is a novel sperm<br> purification device. It consists of two chambers separated by a 10 &mu;M microporous<br> membrane. Untreated semen is applied in one chamber, sperm collection medium<br> in the other. Motile sperm cells are selected by actively swimming through the<br> membrane pores into the medium containing chamber. After 30 min incubation,<br> the sperm collection medium can be aspirated and the purified sperm is ready<br> for further use.<br> In a first experiment we assessed sperm quality and recovery of frozen-thawed semen<br> from six different bulls (n = 6) prior to and after purification with the<br> VetCount<sup>TM</sup> Harvester or BoviPure<sup>TM</sup> gradient centrifugation,&nbsp;a commercial available&nbsp;standard technique. In a second approach, a competitive fertilization assay was performed. Ten straws per bull were pooled, split<br> in two subsamples, and simultaneously purified either with the VetCount<sup>TM</sup> Harvester<br> or BoviPure<sup>TM</sup> gradient centrifugation. Following purification, sperm cells<br> from each treatment group were fluorescently labeled with either MitoTracker<sup>TM</sup><br> Red FM or MitoTracker<sup>TM</sup> Green FM. <em>In vitro</em> matured oocytes were inseminated&nbsp;with<br> equal numbers of red and green labeled sperm. Eighteen hours after fertilization,<br> fluorescence microscopy was used to determine the origin of the fertilizing spermatozoon.</p>

opencc-by-4.0Jun 2023View details →
zenodo44/100

SIRAH-CoV2 initiative: Papain-like Protease (PDB id:6W9C)

<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 Papain-like protease in its APO form with Zn ions bound (PDB id: 6W9C).&nbsp;Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in&nbsp;<a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to&nbsp;<a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado &amp; Pantano JCTC 2020</a>.&nbsp;Zinc ions were parameterized as reported in&nbsp;<a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 6W9C_SIRAHcg_rawdata_0-5us.tar, &nbsp;6W9C_SIRAHcg_rawdata_5-10us.tar,&nbsp;contain&nbsp;all the raw information required to visualize (on VMD), analyze,&nbsp;backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing&nbsp;CG trajectories using&nbsp;<a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a>&nbsp;can be found at www.sirahff.com.</p> <p>Additionally, the&nbsp;file&nbsp;6W9C_SIRAHcg_10us_prot.tar&nbsp;contains only the protein coordinates, while&nbsp;6W9C_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar&nbsp;the file&nbsp;6W9C_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD using the command line:</p> <p>vmd 6W9C_SIRAHcg_prot.prmtop 6W9C_SIRAHcg_prot.ncrst 6W9C_SIRAHcg_10us_prot_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc.,&nbsp;and coloring by&nbsp;restype, element, name, etc.&nbsp;</p> <p>This dataset is part of the SIRAH-CoV2&nbsp;initiative.</p> <p>For further details, please contact Florencia&nbsp;Klein (fklein@pasteur.edu.uy) or Sergio Pantano (spantano@pasteur.edu.uy).</p>

opencc-by-4.0Apr 2020View details →
zenodo44/100

Tutorial and dataset for gigapixel-like imaging strategies for dental anthropology

<p>This tutorial and image dataset to accompany the following publication: Willman JC, Lozano M, Hernando R, Verg&egrave;s JM. Gigapixel-like imaging strategies for dental anthropology: Applications for scientific communication and training in digital image analysis. Quaternary International, <a href="https://doi.org/10.1016/j.quaint.2020.05.027">https://doi.org/10.1016/j.quaint.2020.05.027</a>. Part of the Special Issue: Not Only Use.</p> <p><strong>Contains: </strong>tutorial,<strong> </strong>183 images files for reconstructing three examples of gigapixel-like images, and one &ldquo;READ ME&rdquo; file describing the images.</p> <p>The tutorial is meant to be used as a guideline for the creation of gigapixel-like (GPL) images of dental surfaces based on our experience. The methodology can be extrapolated to other types of materials and surfaces, but you may need to augment these guidelines according to the specificity of your own research needs. We hope that the inclusion of this supplement will stimulate other researchers to include specific guidelines and step-by-step processes for how they created their own GPL images. While this study concentrates on scanning electron microscopy (SEM) images, there are many other ways to acquire two-dimensional images (e.g., digital photography, optical light microscopy, etc.) that can be used to create GPL images. Likewise, the number of software packages and their numerous built-in parameters for creating extended focus and mosaic images vary greatly. Therefore, more tutorials/guidelines will surely improve the transparency and accessibility of the GPL methodology in the archaeological sciences, biological anthropology, and allied fields.</p>

opencc-by-4.0Jun 2020View details →
zenodo44/100

829 drug-like molecules intrinsic solubility dataset

<p>Data collection, curation as well as feature calculation are described in 10.26434/chemrxiv.12746948</p> <p>Please cite the paper if you intend to use the data.</p> <ol> <li>descriptors.csv - File with SMILES index and descriptor data</li> <li>fingerprints.csv - File with SMILES index and fingerprints</li> <li>solubility_data.csv - File with SMILES index, splitting indices and target: logS<sub>0</sub></li> </ol>

opencc-by-4.0Jul 2020View details →
zenodo44/100

Simulation results for Sars-CoV2 3C-like main protease: TRAPP analysis of the binding site flexibility and results of the docking study

<p>Collection of data and scripts related to the paper:</p> <p>Jonas&nbsp;Gossen et al. &quot;A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics&quot;&nbsp;</p> <p>https://www.biorxiv.org/content/10.1101/2020.12.14.422634v2&nbsp; &nbsp;doi:&nbsp;https://doi.org/10.1101/2020.12.14.422634</p> <p>ACS Pharmacology and Translational Science&nbsp; 2021 DOI:&nbsp;10.1021/acsptsci.0c00215</p> <p>&nbsp;</p> <p>&nbsp;</p> <p><strong>1. TRAPP simulation results for Sars-CoV2 3C-like main protease:</strong></p> <p>include simulation of the binding pocket druggability, physical-chemical properties, &nbsp;and the binding site composition</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/Protease_clean.ipynb">Protease_clean.ipynb</a>&nbsp; - Jupyter Notebook containing&nbsp; analysis of the generated data</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/allTables.zip">allTables.zip</a>&nbsp; - results of TRAPP simulations of the binding site flexibility using LRIP and tConcoord methods</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/Every10-ligand_6LU7_R3.5.zip">Every10-ligand_6LU7_R3.5.zip</a>&nbsp;-&nbsp;results of TRAPP pocket analysis on the MD frames</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/PDB-Giulia.zip">PDB-Giulia.zip</a>&nbsp;- TRAPP pocket analysis of 40 PDB complexes of main protease</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/TRAPP_properties_PDB.xlsx">TRAPP_properties_PDB.xlsx</a>&nbsp;- binding pocket properties for&nbsp;40 PDB complexes of main protease summarized in a table</p> <p><a href="https://zenodo.org/api/files/f6c0a0ae-d53a-4e78-aaaf-b3ff674171a5/DrugPDB_3structures.xlsx">DrugPDB_3structures.xlsx</a>&nbsp;-&nbsp;binding pocket properties for 3 PDB structures&nbsp;</p> <p><strong>2. Docking &amp; Screening Results</strong></p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/TRAPP_secondSelection_VS.csv">TRAPP_secondSelection_VS.csv</a>&nbsp;- docking/screening of selected structures from TRAPP analysis</p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/Fred_VS.csv">Fred_VS.csv</a>&nbsp;- docking of PDB structures using Fred</p> <p><a href="https://zenodo.org/api/files/9165535d-aec5-4f1e-8ad1-6ca11a90e595/Glide_VS.csv">Glide_VS.csv</a>&nbsp;- docking of PDB structures using Glide</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS1.xlsx">TableS1.xlsx</a> -&nbsp;&nbsp;Available structures of SARS-CoV-2 Mpro selected for binding site analyses.&nbsp;</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS2A.xlsx">TableS2A.xlsx</a>&nbsp;-&nbsp;SiteScore&nbsp;analysis of all the deposited X-ray crystal structures for the Mpro.</p> <p><a href="https://zenodo.org/api/files/77b1679d-ccc9-4e30-add2-5f7420e04ed1/TableS2B.xlsx">TableS2B.xlsx</a>&nbsp;-&nbsp;&nbsp;SiteScore&nbsp;analysis of the MSM ensemble (4-macrostates).</p>

opencc-by-4.0Nov 2020View details →
zenodo44/100

Fibrinogen-like globe domain of human Tenascin-C (hFBG-C); A Target Enabling Package

<p>Chronic activation of the innate immune system by the damage-associated molecular pattern FBG-C (C-terminal fibrinogen-like globe domain of Tenascin-C) contributes to a variety of inflammatory diseases including arthritis, systemic sclerosis, and cancer. This TEP summarizes the first reported efforts to develop small-molecule FBG-C binders, with the aim to disrupt FBG-C-mediated pro-inflammatory protein-protein interactions (PPIs). We present the soluble expression of disulphide-containing human FBG-C (hFBG-C) in <em>E. coli</em>, the novel structure of hFBG-C, and preliminary chemical matter against hFBG-C derived from a crystallographic fragment screen. Finally, we introduce two robustly validated cellular assays, in either immortalized monocytes or primary human macrophages, which provide a route to development of small molecules which inhibit hFBG-C-activated inflammation.</p>

opencc-by-4.0Nov 2020View details →
zenodo44/100

Binaural room impulse responses of an apartment-like environment

<p>Measured Binaural Room Impulse Responses (BRIR) of the ADREAM Laboratory, LAAS-CNRS, Toulouse, France. The measurements are described in detail in this publication:</p> <p>F. Winter, H. Wierstorf, A. Podlubne, T. Forgue, J. Manh&egrave;s, M. Herrb, S. Spors, A. Raake, and P. Dan&egrave;s, &quot;Database of binaural room impulse responses of an apartment-like environment,&quot; Proc. of 140th Aud. Eng. Soc. Conv., Paris, 2016</p> <p>Abstract of the Publication:</p> <p>We present a database of measured binaural room impulse responses (BRIRs) captured in an apartment-like environment. The BRIRs were measured for four different sound source positions, each combined with four listener positions with a head-orientation varying in the range of +-78&deg; with 2&deg; resolution. &nbsp;Additionally, &nbsp;BRIRs for 20 listener positions along a trajectory connecting two of the four positions were measured, each with a fixed head-orientation. The data is provided in the Spatially Oriented Format for Acoustics (SOFA) and it is freely available under Creative Commons (CC-BY-4.0). It can be used to simulate complex acoustic scenes in order to study the process of auditory scene analysis for humans and machines.</p>

opencc-by-4.0Apr 2016View details →
zenodo44/100

A machine learning-based high-precision density functional method for drug-like molecules

<h2><strong>Models</strong></h2><p>The repo contains the models and test datasets for our aticles. The energy unit is in <strong>Hartree,</strong> The coordinate unit is in<strong> Bohr.</strong></p><p><strong>## DeePHF</strong></p><p>you need first prepare the `dm_eig.npy` in data_test and do predict `l_e_delta.npy`, you can use</p><p>```</p><p>deepks test -m model.pth -o test/test -d data_test/* -D dm_eig -G</p><p>```</p><p><strong>## DeePKS</strong></p><p>first you should prepare the `atom.npy`, and `energy.npy` in data_test. you can test the datasets by command.&nbsp;</p><p>```</p><p>deepks scf scf_input.yaml -m model.pth -s data_test -d test_out</p><p>```</p><p><strong># Datasets</strong></p><p>All datasets only have `atom.npy` and `energy.npy`. The coordinate unit is `bohr`, and energy unit is `Hartree`.</p><p><strong>## small molecules torsion</strong></p><p>Contains 62 small molecules with 36 conformation for each under CCSD(T)/def2-TZVP.</p><p><br>&nbsp;</p><p>[1] B. D. Sellers, N. C. James, A. Gobbi, A comparison of quantum and molecular mechanical methods to estimate strain energy in druglike fragments, Journal of chemical information and modeling 57 (6) (2017) 1265–127</p><p><br>&nbsp;</p><p><strong>## MPCONF91</strong></p><p>Contains 6 molecules with 91 conformations under LNO-CCSD(T)/def2-TZVP.</p><p><br>&nbsp;</p><p>[1] J. Rezac, D. Bím, O. Gutten, L. Rulisek, Toward accurate conformational energies of smaller peptides and medium-sized macrocycles: Mpconf196 benchmark energy data set, Journal of chemical theory and computation 14 (3) (2018) 1254–1</p><p><br>&nbsp;</p><p><strong>## torsionNet206</strong></p><p>Contains 206 molecules with 4494 conformations under CCSD(T)/def2-TZVP.</p><p><br>&nbsp;</p><p>[1] B. K. Rai, V. Sresht, Q. Yang, R. Unwalla, M. Tu, A. M. Mathiowetz,G. A. Bakken, Torsionnet: A deep neural network to rapidly predict small-molecule torsional energy profiles with the accuracy of quantum mechanics, Journal of Chemical Information and Modeling 62 (4) (2022) 785–80</p><p><br>&nbsp;</p><p><strong>## Out-of-plane bending</strong></p><p>Contains 242 molecules with 3315 conformations under CCSD(T)/def2-TZVP.</p><p><br>&nbsp;</p><p>[1] X. Yang, C. Liu, P. Ren, High order ab initio valence force field with chemical pattern based parameter assignment., Journal of Computational Biophysics and Chemistry 21 (4) (2021) 43</p><p><br><br>&nbsp;</p><p><strong>## DrugBank-T</strong></p><p>Contains 165 molecules with 1155 conformations under CCSD(T)/def2-TZVP.</p><p><br>&nbsp;</p><p>[1] V. Law, C. Knox, Y. Djoumbou, T. Jewison, A. C. Guo, Y. Liu, A. Maciejewski, D. Arndt, M. Wilson, V. Neveu, et al., Drugbank</p><p>4.0: shedding new light on drug metabolism, Nucleic acids research 42 (D1) (2014) D1091–D1097 &nbsp;</p><p>[2] Z. Qiao, M. Welborn, A. Anandkumar, F. R. Manby, T. F. Miller III, Orbnet: Deep learning for quantum chemistry using symmetry adapted atomic-orbital features, The Journal of chemical physics 153 (12) (2020) 124111</p><p><strong>## Notice</strong></p><p>if you use above datasets, please cite the original articals too</p>

opencc-byAug 2023View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record