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.
5,145
datasets available to search
ShareScore release 0.7.1
Dataset results
5,145 results for “CO₂”
FIGURE 11 in Taxonomy, ontogeny, and ecology of Tonnacypris stewarti (Daday 1908) comb. nov. (Ostracoda: Cyprididae) from Nam Co, Tibetan Plateau
FIGURE 11. Carapace ontogeny of Tonnacypris stewarti (Daday 1908) from Nam Co (reference-ID NC18-S-37), measures in length and height of (A) right valves (RV) n = 284 and (B) left valves (LV) n = 277 clearly show the different developmental stages. The histograms show the abundances of the different stages with the scatter plot (juveniles A-7 to adults).
FIGURE 8 in Taxonomy, ontogeny, and ecology of Tonnacypris stewarti (Daday 1908) comb. nov. (Ostracoda: Cyprididae) from Nam Co, Tibetan Plateau
FIGURE 8. Tonnacypris stewarti (= E. afghanistanensis), male from Lake Band-e Amir, Afghanistan (reference-ID ZMHK27716). A) right T1, interior view; B) left T1, exterior view; C) left T2, exterior view; D) right T3, interior view; E) CR with attachment; F) hemipenis; and G) Zenker organ. (See Broodbakker & Danielopol (1982) for chaetotaxy.)
FIGURE 14 in Taxonomy, ontogeny, and ecology of Tonnacypris stewarti (Daday 1908) comb. nov. (Ostracoda: Cyprididae) from Nam Co, Tibetan Plateau
FIGURE 14. Total abundance of T. stewarti from Nam Co, including both living and subfossil specimens. Bold values indicate percentages, while absolute abundance is presented within parentheses. Valve categorization includes juveniles (A-7 to A-1) and adults. Sample differentiation is based on habitat type: river, lagoon, and lake. Adult specimens are represented by the gray bar, and juvenile specimens by the black bar. (Sample codes correspond to those listed in Table 1.)
Electronic Companion - Accelerated Benders Decomposition for Enhanced Co-Optimized T&D System Planning
<p>This release is associated with a paper entitled "Accelerated Benders Decomposition for Enhanced Co-Optimized T&D System Planning".</p> <p>In this document, we include information regarding the physical parameters of the transmission and distribution power systems adopted to present the results shown in the paper. Additionally, data regarding investment and operative costs, as well as the scenarios used to describe the uncertainty of renewable-based generation availability and demand, are provided in this document.</p>
Dynamics of terminal fraying-peeling and hydrogen bonds dictate the sequential vs co-operative melting pathways of nanoscale DNA and PNA triplexes
<div>##################################################################################################</div> <div> </div> <div>The simulation dataset used for the analysis reported in the manuscript titled -</div> <div> </div> <div> "Dynamics of terminal fraying-peeling and hydrogen bonds dictate the sequential vs co-operative melting pathways of nanoscale DNA and PNA triplexes."</div> <div> by Sandip Mandal, Krishna N. Ganesh, and Prabal K. Maiti*</div> <div> </div> <div> </div> <div>*E-mail: maiti@iisc.ac.in</div> <div>Center for Condensed Matter Theory, Department of Physics,</div> <div>Indian Institute of Science, Bangalore 560012, India</div> <div> </div> <div>##################################################################################################</div> <div> </div> <div>Packages required:</div> <div> Visual Molecular Dynamics (VMD)</div> <div> xmgrace</div> <div> numpy</div> <div> matplotlib</div> <div> scipy</div> <div> tleap/xleap</div> <div> AMBER</div> <div> CPPTRAJ</div> <div> MMGBSA</div> <div> Gaussian09 for partial charge calculation of the PNA protonated cytosine residues</div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>Contents:</div> <div> </div> <div>The First main directory contains three folders and a README file--</div> <div>(1)PNA_DNA_PNA_Triplex</div> <div> (2)DNA_DNA_DNA_Triplex</div> <div>(3)Sequence_Dependence</div> <div>(4)README file</div> <div> </div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>1. PNA_DNA_PNA_Triplex directory contains sub-directory for Protonated PNA-DNA-PNA triplexes simulation data, such as ---</div> <div>(a) Setup_files</div> <div>(b) Results</div> <div> </div> <div> </div> <div>(a)Setup_files:</div> <div>It contains input coordinates (PDP_18bp_PP.pdb, Protonated_PNA_DNA_PNA.inpcrd ..), topology files, and files required to build to complete </div> <div>system for simulation including library files for non-standard PNA residues and tleap script for the system setup.</div> <div> </div> <div>(b)Results: --folder contains simulation data and analysis scripts for Protonated PNA-DNA-PNA triplexes -----</div> <div> </div> <div>(a)Simulation Trajectories ( It contains simualtion trajectories for three independent trial runs named run1, run2, and run3)</div> <div>(b)RMSD</div> <div>(c)Hbond (hydrogen bonding)</div> <div>(d)Stiffness </div> <div>(e)Free Energy Landscape (FEL)</div> <div>(f)PCA (Principal Component Analysis)</div> <div>(g)Simulation Movies for the Protonated PNA-DNA-PNA triplex</div> <div> </div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>2. DNA_DNA_DNA_Triplex directory contains sub-directory for Protonated DNA-DNA-DNA triplexes simulation data, such as ---</div> <div> (a) Setup_files</div> <div> (b) Results</div> <div> </div> <div> </div> <div>(a)Setup_files :</div> <div>It contains input coordinates (DDD_18bp_Protonated_NAB.pdb, 18bp_Protonated_NAB.inpcrd ...) and files required to build to complete</div> <div>system for simulation, including amber input files in the "Sander-Input-files" subdirectory, a script to run the simulation in a GPU clusters, and a leap script for the system setup.</div> <div> </div> <div>(b)Results --folder contains simulation data and analysis scripts for-----</div> <div> </div> <div> (a)Simulation Trajectories ( It contains simulation trajectory for three independent trial runs named run1, run2, and run3)</div> <div> (b)RMSD</div> <div> (c)Hydrogen bonding</div> <div> (d)Stiffness</div> <div> (e)Free Energy Landscape</div> <div> (f)PCA</div> <div> (g)Simulation Movies for the Protonated DNA-DNA-DNA triplex</div> <div> </div> <div> </div> <div>##################################################################################################</div> <div> </div> <div>3. Sequence_Dependence directory contains sub-directory for triplexes with all TAT base triples simualtion data, such as ---</div> <div> (A) DNA_DNA_DNA_with_all_TAT_sequence</div> <div> (B) PNA_DNA_PNA_with_all_TAT_sequence</div> <div> </div> <div>(A)Sub-directory contains simulation data for DNA-DNA-DNA triplexes with 18 TAT base triples (no protonated cytosine residues) ---</div> <div>(a)Set_up_files</div> <div>(b)Trajectory</div> <div>(c)RMSD</div> <div>(d)Hbond</div> <div> </div> <div> </div> <div>(B)Sub-directory contains simulation data for PNA-DNA-PNA triplexes with 18 TAT base triples (no protonated cytosine residues) ---</div> <div> (a)Set_up_files</div> <div> (b)Trajectory</div> <div> (c)RMSD</div> <div> (d)Hbond</div> <div> </div> <div>##################################################################################################</div> <div> </div> <div> </div> <div>The trajectories are sampled at 1 ns intervals (since the full trajectory dumped at 10 ps interval, will take huge dataspace) in each trajectory file:</div> <div>(a)The first frame corresponds to the structure immediately after 5 ns NPT equilibriation.</div> <div>(b)The next 200 frames are from the production run, at 1 to 200 ns.</div> <div> </div> <div> </div> <div>References:</div> <div>The DNA-DNA-DNA and PNA-DNA-PNA models were constructed using NAB code [1].</div> <div>System preparation with water and ions are done with the help of tleap/xleap [2].</div> <div>All simulation runs are conducted in AMBER2020 [3].</div> <div>For visualization we have used VMD [4].</div> <div> </div> <div>[1] T. J. Macke and D. A. Case, Modeling unusual nucleic acid structures, 1998</div> <div>[2] D. R. Roe and T. E. Cheatham III, Journal of chemical theory and computation, 2013, 9, 3084–3095</div> <div>[3]D. A. Case, H. M. Aktulga, K. Belfon, I. Ben-Shalom, S. R.Brozell, D. S. Cerutti, T. E. Cheatham III, V. W. D. Cruzeiro,T. A. Darden, R. E. Duke et al., Amber 2021, University of California, San Francisco, 2021</div> <div>[4] Humphrey W, Dalke A, Schulten K. VMD: visual molecular dynamics. J. Mol. Graph. 14(1), 33-38 (1996).</div>
3D chromatin structures associated with ncRNA roX2 for hyperactivation and co-activation across the entire X chromosome
<p>The SMLM datasets of roX2 and roX2/H3K27me3.</p>
Temporal overlays of the co-word analysis network on Ecotourism, Sustainable Tourism and Nature Based Tourism (1986-2022): Map and network for VOSViewer visualization
Open the record for dataset details and reuse information.
Geographical overlays of the co-word analysis network on Ecotourism, Sustainable Tourism and Nature Based Tourism (1986-2022): Map and network for VOSViewer visualization
Open the record for dataset details and reuse information.
Base outline of the co-word analysis network on Ecotourism, Sustainable Tourism and Nature Based Tourism (1986-2022): Map and Network for VOSViewer visualization
Open the record for dataset details and reuse information.
PRISMA Flowchart for the systematic review: Impact on postoperative complications of combined prehabilitation targeting co-exisiting smoking, malnutrition, obesity, alcohol drinking, and physical inactivity: a systematic review and meta-analysis of randomised trials
<p><strong><span>PRISMA 2020 Flow Chart.</span></strong><span> “No predefined risk factors” covers studies with relevant multimodal interventions that were excluded due to lack of predefined risky lifestyles in the population. “Only trial registration or conference abstract” covers reports of these but with no subsequent publication. SNAP: Smoking, Nutrition (overweight/obesity, malnutrition), Alcohol or Physical inactivity.</span></p>
PXRD data of the La1-xCex/2Cax/2 solid solution produced by solid state and co-precipitation synthesis
<p>These datasets were recorded on a laboratory powder X-ray machine. The fundamental parameters are given in the input files. Rietveld refinement was carried out in the Topas software.</p>
Dataset S4-6: Leveraging co-evolutionary insights and AI-based structural modeling to unravel receptor-peptide ligand-binding mechanisms
<h3>Significance statement:</h3> <p>This study presents proof-of-concept for a rapid and inexpensive alternative to classical structure-based approaches for resolving ligand-receptor binding mechanisms. It relies on a multilayered bioinformatic approach that leverages genomic data across diverse species in combination with AI-based structural modeling to identify true ligand and receptor homologues, and subsequently predict their binding mechanisms. <em>In silico </em>findings were validated by multiple experimental approaches, which investigated the effect of amino acid changes in the proposed binding pockets on ligand-binding, complex formation with a co-receptor essential for downstream signaling, and activation of downstream signaling. Our analysis combining evolutionary insights, <em>in silico</em> modeling and functional validation provides a framework for structure-function analysis of other peptide-receptor pairs, which could be easily implemented by most laboratories.</p> <h3><span>Zip file contains:</span></h3> <p><span>Dataset S4:</span><span> </span><strong><span>Plasmid maps of constructs used in this study.</span></strong></p> <p><span>Dataset S5:</span><span> </span><strong><span>AFM and AF3 predicted structures (.pdb) and AFM confidence metrics (.pae)</span></strong></p> <p><span>Dataset S6:</span><span> </span><strong><span>Unedited files (.tiff) of co-IP and western blotting.</span></strong></p>
Complementary dataset to Granulation compared to co-application of biochar plus mineral fertilizer and its impacts on crop growth and nutrient leaching.
Open the record for dataset details and reuse information.
Implementation of K-Nearest Neighbor Algorithm and Gray Level Co-Occurance Matrix Method in Mushroom Type Classification
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Electronic Companion - Accelerated Benders Decomposition for Enhanced Co-Optimized T&D System Planning
<p>This release is associated with a paper entitled "Accelerated Benders Decomposition for Enhanced Co-Optimized T&D System Planning".</p> <p>In this document, we include information regarding the physical parameters of the transmission and distribution power systems adopted to present the results shown in the paper. Additionally, data regarding investment and operative costs, as well as the scenarios used to describe the uncertainty of renewable-based generation availability and demand, are provided in this document.</p>
Supporting data for "Femtosecond core-level spectroscopy reveals involvement of triplet states in the gas-phase photodissociation of Fe(CO)5"
Open the record for dataset details and reuse information.
Don't Forget to Change These Functions! Recommending Co-Changed Functions in Modern Code Review
Open the record for dataset details and reuse information.
Co-condensation of silica and lignin: STEM tomography and EDS analysis
<p>Data files associated with publication: "Silica Biomineralization with Lignin Involves Si−O−C Bonds That<br>Stabilize Radicals", https://doi.org/10.1021/acs.biomac.4c00061 </p> <p>A) Tomographic reconstruction of the silica-lignin particles by HAADF-STEM, a single .mrc volume file.</p> <p>B) Energy Dispersive X-ray Spectroscopy analysis of in vitro condensation of silica and lignin. Bruker format .emd files with screen previews included. </p>
ICSME 2024 Research Track: "What Happened to my Models?" History-Aware Co-Existence and Co-Evolution of Metamodels and Models
<p> </p> <h1>ICSME 2024 Research Track: “What Happened to my Models?” History-Aware Co-Existence and Co-Evolution of Metamodels and Models</h1> <p> </p> <p>This repository provides the dataset and results for the evaluation of the paper “What Happened to my Models?” of the ICSME 2024 Research track.<br>The dataset consists of the following files:</p> <ul> <li><strong>RQ1-Type-Refactors.zip </strong>contains the operations and refactoring performed on each of the given metamodels used by our approach in RQ1.</li> <li><strong>RQ1-Type-Results.zip</strong>: contains the group results of RQ1 as shown in our paper with additional metrics of other operations not highlighted in our paper due to space limitations.</li> <li><strong>RQ2-3-PlantUML-Models.zip</strong>: contains the 250 PlantUML models and the metamodel. This folder contains the co-evolved PlantUML models, the operations performed during the co-evolution, their metrics and the state of the models bore and after the co-evolution. </li> <li><strong>RQ2-3-PlantUML-Results.zip:</strong> contains the group results of RQ2 and RQ3 as shown in our paper with additional metrics of other operations not highlighted in our paper due to space limitations</li> <li><strong>RQ2-3-FHIR-Models.zip</strong>: contains the 1180 FHIR models and the metamodel. This folder contains the co-evolved FHIR models, the operations performed during the co-evolution, their metrics and the state of the models bore and after the co-evolution.</li> <li><strong>RQ2-3-Results.zip:</strong> contains the grouped results of RQ2 and RQ3 as presented in our paper.</li> <li><strong>Additionalnformation.pdf:</strong> contains additional information on how to read the files extracted by our approach, i.e., how to read the models and operations and how our executable refactoring catlaog works since we only focused on Property Refactorings in the paper.</li> <li><strong>Results.pdf</strong>: Contains an overview of the results (the results from our paper + additional results)</li> <li><strong>Tools.zip: </strong>Contains the tools used for the evalution. For an explaination how to use it read the <strong>Additionalnformation.pdf, </strong>see below<strong> </strong>or contact the authors</li> </ul> <p><strong>Running the tools:</strong></p> <p><em>Windows 10/11<br></em><em>JDK 20 or above</em></p> <p>The tools consist of two programs: </p> <ul> <li><strong>importer.jar<br></strong>This file is used to import a FHIR or PlantUML file into the server and co-evolve it.</li> <li><strong>server_FHIR_.jar<br></strong>The server stores the models and co-evolves them. The files provided already have the metamodels preloaded, that are used in RQ2 and RQ3, i.e., FHIR_STU3 contains the FHIR metamodel version DSTU2 and STU3 and our hybrid PlantUML, while FHIR_ synthetic contains the FHIR metamodel DSTU2 and our synthetically created one.</li> </ul> <p><strong>How to use the Tools</strong></p> <p>First, start the server by starting the jar. The server also has an experimental GUI mode that allows engineers to check the types and instances that were created. <em><strong>Note: </strong>This mode is currently under development and is still unstable. The mode <em>is accessible</em> by adding -gui as a parameter.</em></p> <p><em>java -jar server_FHIR_STU3.jar -gui</em></p> <p>Otherwise, just run the server normally:</p> <p><em>java -jar server_FHIR_STU3.jar</em></p> <p>After the server has booted up, it exports InstanceTypes and operations created for the FHIR and PlantUML metamodels. Next, you start the importer. The importer has two modes: the PlantUML mode, where it imports a PlantUML state machine and co-evolves it and the FHIR mode, where it imports an FHIR file and co-evolves it into either STU3 or our synthetic version (depending on which of the preloaded servers is running). Just run the tool by providing either FHIR or PlantUML, the imported file and the path where the output should be stored.</p> <p><strong><em>FHIR-Mode:</em></strong></p> <p><em>java -jar importer.jar FHIR C:\Users\Admin\Desktop\Aaron697_Brekke496_2fa15bc7-8866-461a-9000-f739e425860a.json C:\Users\Admin\Desktop\results</em></p> <p><strong>PlantUML Mode:</strong></p> <p><em>java -jar importer.jar PlantUML C:\Users\Admin\Desktop\branch.puml C:\Users\Admin\Desktop\results</em></p> <p><em><strong>Note: </strong></em><em>Please run both the tool and the server in a command line to receive additional information about the importing and co-evolution since the tool is otherwise without a user interface.</em></p> <p> </p>
Dataset: Infrared Spectroscopy and Quadrupole Mass Spectrometry during Temperature Programmed Desorption of Mixed Hyper-volatile (CO, N2, Ar) and Amorphous Ices (H2O, CO2)
<p>IR spectra of H2O:CO and CO2:CO ices mixed at 2 concentrations (5:1 and 15:1) and grown to 7 thicknesses (50 to 3000 ML). Spectra are also included for a smaller set of H2O:N2, H2O:Ar, CO2:N2, and CO2:Ar ices. </p> <p>The files are .txt files, where the first column is wavenumber (cm-1) and the second is IR absorbance.</p> <p>QMS data taken during TPD of H2O:CO and CO2:CO ices mixed at 2 concentrations (5:1 and 15:1) and grown to 7 thicknesses (50 to 3000 ML). Data are also included for a smaller set of H2O:N2, H2O:Ar, CO2:N2, and CO2:Ar ices.</p> <p>Constructing TPD curves require 2 files for each ice, a .asc file and a .xls file. The .asc files contain the relative time (s) and ion count for many relevant m/z. The .xls files contain time (s) and temperature (K). Time from each file can be interpolated to produce ion count as a function of temperature.</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.