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.
2,103
datasets available to search
ShareScore release 0.7.1
Dataset results
2,103 results for “Components”
One meter integrated depth velocity (total, ageostrophy, Ekman and Stokes components) fields for the Mediterranean Sea each 6 hours
<p>One meter integrated depth surface velocity fields for the Mediterranean Sea calculated following the methodology in Morales-Marquez et al.(2020). This dataset provides the total velocity field, the ageostrophic, Ekman and Stokes components.</p>
Three-component modelling of O-rich AGB star winds I. Effects of drift using forsterite – dataset
<p>The data provided here include all parameter files, log files, and a set of the<br> binary output files that are the basis for the publication in A&A.</p> <p>The file 'file_listing.txt' contains a complete list of files and directories<br> in all gzipped tar files. Each individual gzipped tar file is formatted as<br> follows:</p> <p> Mm.m_Ll.ll_Ttttt.tar.gz</p> <p>where<br> m.m :: the assumed mass of the model, in solar masses<br> l.ll :: The assumed luminosity, in log10(solar luminosities)<br> tttt :: The effective temperature of the star, in Kelvin.</p> <p><br> The contents of the tar files vary according to the model, but here is the<br> general directory structure:</p> <p> nodr/ :: non-drift / PC models<br> drift/ :: drift models</p> <p> nodr/init<br> drift/init :: Initial model files created using John Connor.</p> <p><br> File suffixes are the following:</p> <p> .par :: Plain-text parameter file that contains all parameters that are<br> different from the respective default value in the model.<br> Consequently, to see what parameters were actually used, it is<br> necessary to look in the log file (see below).</p> <p> .bin :: Binary file that contains output of converged models. Each model is<br> stored in two versions, first the previous time step and then the<br> current time step (having access to the model code T-800, data of both<br> time steps are needed to restart model calculations at that time<br> step).</p> <p> The initial model file only contains one model; where the previous<br> time step data are the same as the current time step data.</p> <p> We provide a tool to read this file, see below.</p> <p> Note! These files can get pretty large and are therefore only<br> available for a smaller number of the models in the Zenodo dataset.<br> Please ask the corresponding author for the missing files is the<br> need should appear.</p> <p> .log :: Plain-text log file that shows the used model parameters and a number<br> of key properties for each converged model.<br> The encoding of this file is UTF-8.</p> <p> .inf :: Plain-text secondary log file that contains the header of the<br> [primary] log file as well as timing information.<br> The encoding of this file is UTF-8.</p> <p> .tpb :: Secondary binary file that contains a number of properties specified<br> at the outer boundary, typically for each consecutive time step.</p> <p> We provide a tool to read this file, see below.</p> <p> .lis :: Plain-text file with the iteration history. Unavailable here.</p> <p> .liv :: Plain-text file with values specified for a number of properties at<br> each gridpoint. Unavailable here.</p> <p> .inp :: Plain-text file that is used to launch a model; some are present.<br> This file is automatically generated by the tool that launches T-800<br> and is typically removed when T-800 launches. Unavailable here.</p> <p> .eps :: Encapsulated PostScript file created by John Connor when calculating<br> the initial model.</p> <p><br> Model evolution structure - file endings before the suffix:</p> <p> _rlx :: Files related to relaxing the T-800 calculations on the initial model<br> created by John Connor.</p> <p> _exp :: Files related to expanding the initially compact model to using the<br> full radial domain.</p> <p> _fix :: Files related to the intermediate stage where calculations are changed<br> from expansion to outflow.<br> <br> _out :: Files related to the outflow stage of the calculations; this is what<br> you want to look at to see the wind evolution. Results in the paper<br> are calculated using these data.</p> <p> Note! Some outflow stage calculations continue the evolution of the previous<br> set of files. The underlying reason for continued calculations is typically<br> that the calculated time interval is too short. Such files are typically<br> given the extension '_cont.lin_out', '_cont2.lin_out', etc.</p> <p><br> Stored data in the binary files:</p> <p> The binary files (suffix '.bin') contain the full radial structure in the<br> following 10 (PC models) or 11 (drift models) primary variables:</p> <p> mr: radius<br> mm: integrated [gas] mass<br> md: gas density<br> mu: gas velocity<br> me: internal energy<br> mj: radiative energy<br> mh: radiative flux<br> n0: dust moment, forsterite (Fo)<br> nm: number density of magnesium atoms<br> ns: number density of silicon atoms<br> v0: dust velocity, forsterite (only drift models)</p> <p> Other properties are derived from these primary variables using auxiliary code<br> that isn't part of this dataset.</p> <p><br> Load files:</p> <p> Two tools are provided here that can load the binary data files using the<br> Interactive Data Language (IDL):</p> <p> sc_load_bin (for files with the suffix '.bin'):</p> <p> Loads the full content of a T-800 binary file and returns a structure<br> with the data.</p> <p><br> sc_load_tpb (for files with the suffix '.tpb'):</p> <p> Loads the full content of a T-800 'tpb' binary file and returns a<br> structure with the data.</p> <p> Note! Due to the way models run on clusters, this file is sometimes<br> incomplete; this happens when the model code T-800 is stopped as the<br> cluster-specific walltime is reached. If this is the case, it is<br> necessary to use the binary file instead, where data are saved<br> typically every 20:th time step.</p> <p> Alternative tools for use with Python and Julia could be considered for<br> writing, but where not yet available when this dataset was made public.<br> Please contact the corresponding author for a current status on this issue.</p> <p> </p>
Quantitative results of the analysis of relevant components of the human scapholunate interosseous ligament (SLIL)
<p>This dataset corresponds to the quantification results carried out for the human scapholunate interosseous ligament (SLIL) and several control tissues analyzed in the manuscript entitled "Histological characterization of the human scapholunate ligament". The SLIL plays a fundamental role in stabilizing the wrist bones, and its disruption is a frequent cause of wrist arthrosis and disfunction. Traditionally, this structure is considered to be a variety of fibrocartilaginous tissue and consists of three regions: dorsal, membranous and palmar. Despite its functional relevance, the exact composition of the human SLIL is not well understood. In the present work, we have analyzed the human SLIL and control tissues from the human hand using an array of histological, histochemical and immunohistochemical methods to characterize each region of this structure. Results reveal that the SLIL is heterogeneous, and each region can be subdivided in two zones that are histologically different to the other zones. Analysis of collagen and elastic fibers, and several proteoglycans, glycoproteins and glycosaminoglycans confirmed that the different regions can be subdivided in two zones that have their own structure and composition. In general, all parts of the SLIL resemble the histological structure of the control articular cartilage, especially the first part of the membranous region (zone M1). Cells showing a chondrocyte-like phenotype as determined by S100 were more abundant in M1, whereas the zone containing more CD73-positive stem cells was D2. These results confirm the heterogeneity of the human SLIL and could contribute to explain why certain zones of this structure are more prone to structural damage and why other zones have specific regeneration potential. The original data obtained for the quantitative analyses of each component are shown in this dataset.</p>
Resolving the Interpretation of Magnetic Coercivity Components from Backfield Isothermal Remanence Curves Using Unmixing of Non-linear Preisach Maps: Application to Loess-Paleosol Sequences
<p>The data set includes:</p> <p>1. Non-linear Preisach measurements for Lunca and Costinești loess-paleosol sections</p> <p>2. IRM coercivity distributions from Lunca and Costinesti interpolated on a common sequence of fields</p> <p>3. Lunca granulometry data</p> <p>4. Median Grain size for Costineși section.</p> <p>5. Magnetic susceptibility data measured at Lunca section (Constantin et al., 2015)</p> <p>6. IRM acquisition curves derived from backfield IRM data through rescaling for Costinesti section</p> <p>7. Costinesti rock magnetic data (Necula et al., 2015)</p>
Data from: Genome-wide selection components analysis in a fish with male pregnancy
Open the record for dataset details and reuse information.
Are you scared yet? Variations to cue components elicits differential prey behavioral responses even when gape limited predators are relatively small.
Anti-predator behavior is often evoked based on measurements of risk calculated from sensory cues emanating from predators independent of physical attack. Yet, the exact sensory indices of cues used in risk assessment remain largely unknown. To examine how different predatory cue indices of information are used in risk assessment, we presented prey with various cues from sublethal gape-limited predators. Rusty crayfish (Faxonius rusticus (Girard, 1852)) were exposed to predatory odors from sublethal-sized largemouth bass (Micropterus salmoides (Lacepède, 1802)) to test effects of changing predator abundance, relative size relationships, and total predator length in flow through mesocosms. Foraging, shelter use, and movement behavior were used to measure cue effects. Foraging time depended jointly upon predator abundance and total predator size (p = 0.030). Specifically, high predator abundance resulted in decreased foraging efforts as gape ratio increased. Similarly, sheltering time depended on the interaction between predator abundance and gape ratio when predator abundance was highest (p = 0.020). Crayfish significantly increased exploration time when gape ratio increased (p = 0.010). Thus, this study shows crayfish can use different indices of predatory cues, namely total predator abundance and relative size ratios, in risk assessment but do so in context-specific ways.
ePSproc: Naphthalene S1 component Orb 34 > 42 (B3u) ionization, 1.0 - 30.1 eV
Naphthalene S1 component Orb 34 > 42 (B3u) ionization, 1.0 - 30.1 eV - photoionization calculations with ePolyScat (ePS) + ePSproc.<br><br>*Web version*: <a href="https://phockett.github.io/ePSdata/naphthalene/naphthalene_wf_1.0-30.1eV_orb34-42_S1.html">https://phockett.github.io/ePSdata/naphthalene/naphthalene_wf_1.0-30.1eV_orb34-42_S1.html</a><br><br>For more details of the calculations, see readme.txt, or: <ul><li><a href="https://phockett.github.io/ePSdata/about.html">About ePSdata</a></li><li><a href="http://epsproc.readthedocs.io/en/latest/about.html">About ePSproc</a></li><li><a href="http://www.chem.tamu.edu/rgroup/lucchese/ePolyScat.E3.manual/manual.html">About ePS</a></li></ul>
Defects in Power Distribution Components
<p>A set of 708 images and respective labels of defects on components from a electric distribution system. Each image contains one defect and the respective area is in the file "image-filename.txt".</p> <p>Each label contains the class of the defect, the coordinates <strong>x</strong> and <strong>y</strong> of the center in the image and the values of <strong>width </strong>and <strong>height </strong>of the defect area.</p> <p>The defects currently in the dataset are:</p> <p>0 - Cable out of spacer;</p> <p>1 - Cable out of insulator;</p> <p>2 - Insulator withour ring;</p>
Figure 3 in Biotic components of dung beetles (Insecta: Coleoptera: Scarabaeidae: Scarabaeinae) from Pantanal - Cerrado Border and its implications for Chaco regionalization
Figure 3. Generalized tracks (GT A–L) of dung beetles, different biotic components observed in the Pantanal–Cerrado Border.
Three-component modelling of C-rich AGB-star winds V. – dataset
<p>The provided data include all parameter files, binary output files, and log<br> files that are the basis for the publication in MNRAS.</p> <p>The file 'file_listing.txt' contains a complete list of files and<br> directories in all gzipped tar files. Each individual gzipped tar file is<br> formatted as follows:</p> <p> Mm.m_Ll.ll_Ttttt_CtOc.cc.tar.gz</p> <p>where<br> m.m :: the assumed mass of the model, in solar masses<br> l.ll :: The assumed luminosity, in log10(solar luminosities)<br> tttt :: The effective temperature of the star, in Kelvin.<br> c.cc :: The carbon-to-oxygen excess, in log10(n_C/n_H-n_O/n_H)+12</p> <p><br> The contents vary according to the model, but here is the general directory<br> structure:</p> <p> nodr/ :: non-drift / PC models<br> drift/ :: drift models</p> <p> nodr/init<br> drift/init :: Initial model files created using John Connor.</p> <p><br> File suffixes are the following:</p> <p> .par :: Plain-text parameter file that contains all parameters that are<br> different from the respective default value in the model.<br> Consequently, to see all used parameters it is necessary to look in<br> the log file (see below).</p> <p> .bin :: Binary file that contains converged models. Each model is stored in<br> two versions, first the previous time step and then the current time<br> step (both are needed to restart model calculations at that time<br> step).</p> <p> The initial model file only contains one model; where the previous<br> time step data are the same as the current time step data.</p> <p> The format of this file is explained below.</p> <p> Note! These files can get pretty large and are therefore only<br> available for a smaller number of the models here. Please ask the<br> corresponding author for the missing files should the need appear.</p> <p> .log :: Plain-text log file that shows the used model parameters and a number<br> of key properties for each converged model. The encoding of this file<br> is UTF-8.</p> <p> .inf :: Plain-text secondary log file that contains the header of the<br> [primary] log file as well as timing information.</p> <p> .tpb :: Secondary binary file that contains a number of properties specified<br> at the outer boundary, typically for each consecutive time step.</p> <p> .lis :: Plain-text file with the iteration history. Available for some files.</p> <p> .liv :: Plain-text file with values specified for a number of properties at<br> each gridpoint. Available for a smaller number of files.</p> <p> .inp :: Plain-text file that is used to launch a model; some are still there.</p> <p> .eps :: Encapsulated PostScript files created by John Connor when calculating<br> the initial model.</p> <p><br> Model evolution structure - file endings before the suffix:</p> <p> _rlx :: Files related to relaxing the T-800 calculations on the initial model<br> created by John Connor.</p> <p> _exp :: Files related to expanding the initially compact model to using the<br> full radial domain.</p> <p> _fix :: Files related to the intermediate stage where calculations are changed<br> from expansion to outflow.<br> <br> _out :: Files related to the outflow stage of the calculations; this is what<br> you want to look at to see the wind evolution. Results in the paper<br> are calculated using these data.</p> <p> <br> Note! Some outflow stage calculations continue the evolution of the previous<br> set of files. The underlying reason for continued calculations is typically<br> that the calculated time interval is too short. Such files are typically<br> given the extension '_cont.lin_out', '_cont2.lin_out', etc.</p> <p><br> Load files:</p> <p> Two tools are provided here that can load the binary data files using the<br> Interactive Data Language (IDL):</p> <p> sc_load_bin (for files with the suffix '.bin'):</p> <p> Loads the full content of a T-800 binary file and returns a structure<br> with the data.</p> <p><br> sc_load_tpb (for files with the suffix '.tpb'):</p> <p> Loads the full content of a T-800 'tpb' binary file and returns a<br> structure with the data.</p> <p> Note! Due to the way models run on clusters, this file is sometimes<br> incomplete; this happens when the model code T-800 is stopped as the<br> cluster-specific walltime is reached. If this is the case, it is<br> necessary to use the binary file instead, where data are saved<br> typically every 20:th time step.</p> <p> Alternative tools for use with Python and Julia could be considered for<br> writing, but where not yet available when this dataset was made public.<br> Please contact the corresponding author for a current status on this issue.</p>
Supplementary data: Quantifying and numerically representing recharge and flow components in a karstified carbonate aquifer
<p>The following data and information is related to the paper "Quantifying and numerically representing recharge and flow components in a karstified carbonate aquifer":</p> <p>Observed and estimated hydrological time series (hourly spring discharge, hourly rainfall, daily minimum and maximum air temperature and evapotranspiration) and modelling results (hourly discharges and flows) using an InfoWorks ICM pipe network model, as well as R code to compute Fourier transform / power spectrum of time series.</p> <p>See 'readme_Schuler_etal_WRR_supplementary data.pdf' for more information.</p>
Monthly values of absolutely measured geomagnetic components (D, I, H, Z, F) of Coimbra observatory in the period1866-2015
<p>Monthly values of the absolutely measured geomagnetic components D, I, H, Z, F for the period 1866-2015 of Coimbra observatory. Different combinations of three magnetic elements were used to fully specify the geomagnetic field vector: the HDI components were measured in 1866-1951; HDZ in 1951-2006; and DIF in 2006-2015. It should also be noted that the monthly values of the first period (1866-1951) are based exclusively on the averages of absolute measurements (made on average 3 per month), while from 1951 onwards the monthly averages were calculated from the hourly averages obtained from the magnetograms after baseline calibration.</p>
Software Product Line Configuration and Traceability: an Empirical Study on SMarty Class and Component Diagrams
<p>Software Product Line Configuration and Traceability: an Empirical Study on SMarty Class and Component Diagrams</p>
Artefacts for ICSE 2021 technical paper: "RAICC: Revealing Atypical Inter-Component Communication in Android Apps"
<p>This repository represents our artefacts to replicate our paper which is in the proceedings of ICSE 2021: "RAICC: Revealing Atypical Inter-Component Communication in Android Apps"</p>
Research data supporting for Application of electron tomography of dislocations in beam-sensitive quartz to the determination of strain components
<p>This archives contains all the raw data (micrographs) supporting the publication</p>
Dataset for project Lipobodies, related to the development of a new multicomponent process based on the combination of the isonitrile-tetrazine (4+1) cycloaddition and the Ugi four-component reaction
<p>This dataset contains primary (including raw data) that supports the results of the design and development of a new multicomponent process based on the combination of the isonitrile-tetrazine (4+1) cycloaddition and the Ugi four-component reaction</p>
Phylogenomic analysis of cell-surface receptors and downstream signaling components in the plant lineage
<p>Here we identified cell-surface receptors and downstream signaling components from the genomes of 350 plant species. </p><p>Zip file contains:</p><p>Folder 'seqeunces for downstream signaling components' - FASTA and TREE files of the identified downstream signaling components.</p><p>Folder 'sequences for cell-surface receptors' - FASTA and TREE files of the identified cell-surface receptors.</p><p>Folder 'Specific analysis' - Contains specific analysis for the identified cell-surface receptors.</p><p>Subfolder 'ID analysis' - Contains information on ID clusters and motifs analysis in IDs.</p><p>Subfolder 'LRR motif gap analysis' - Contains information on small (10-29 aa) and large (30-90) gaps between LRR motifs in RLPs and RLKs.</p><p>Subfolder 'LRR-RLK & LRR-RLP phylogenetic analysis' - Contains FASTA and TREE files of the specific domain/regions (C3, C3-F, eJM-TM-cJM, and all) in LRR-RLPs and LRR-RLKs. This subfolder also contains the specific amino acid, charge and motif analysis in this region (see C3F-end features.xlsx).</p><p>Protein counts per species file - Contains the total number of each protein family/subfamily in each of the 350 species.</p><p>simpleToFullNames (translator file)- Translator file for the original ID of each gene. </p>
Dataset Repository for a Botanical Garden Project: Project Based Learning Assessment from a Blended Approach of PBL with the 5E Model Components
<p><strong>Title:</strong> Botanical Explorers: A Journey Through Our School's Flora - Assessment Data</p><p><strong>Description:</strong> This Excel spreadsheet contains the assessment data for the educational project titled "Botanical Explorers: A Journey Through Our School's Flora", a hands-on science initiative for Grade 9 students at Chalermkwansatree School. The project, conducted under the guidance of Teacher Hasan and aligned with the Additional Science subject focusing on Fuel Energy, is designed to engage students in active learning about local plant life, while developing their research and presentation skills, and fostering environmental appreciation.</p><p>The dataset is part of a comprehensive project contributing 20% to the Term 1, Midterm Score of 50 marks. It encompasses a detailed breakdown of the marks distribution across different tasks such as Data Collection, Book Report, Presentation, and Poster creation, reflecting the multifaceted approach to evaluating student learning and engagement.</p><p><strong>Data Organization:</strong> The spreadsheet is meticulously organized to include:</p><ul><li>A plant list with identifiers like school plant name, location, and space for pictures.</li><li>A marks distribution table indicating the scoring for each project component.</li><li>A timeline for group formation, research, data collection, and submission deadlines.</li><li>Details of the Book Report, Presentation, and Poster requirements.</li></ul><p><strong>Methodology:</strong> Students formed groups to research seven specific plants found within the school premises, examining their identification, classification, ecological roles, growth, and development. The data was collected through a blend of direct observations and scholarly research, ensuring a robust and educational exploration of botany.</p><p><strong>Intended Audience:</strong> The dataset is intended for educational purposes, serving as a valuable resource for educators, students, and researchers interested in project-based learning, botany education, and student assessment methods.</p><p><strong>Usage Notes:</strong> The data provided in this spreadsheet is anonymized, with no personal student information disclosed. It serves as an exemplar model for similar educational initiatives and can be adapted for comparative studies or further educational research.</p><p><strong>Conditions for Use:</strong> The dataset is shared openly with the intention that it will be used for educational and research purposes. Users are requested to cite the dataset appropriately and adhere to any academic and ethical guidelines when utilizing the data for their work.</p>
Additional information to Research Letter "Immunogenicity of Cephalosporin Components in Non-IgE Mediated Cephalosporin Allergy"
<p>The file contains the following information:</p><p><strong>Additional information</strong></p><p><strong>Description</strong></p><p><strong>Page</strong></p><p>Materials and Methods</p><p>Study materials, syntheses of degradation products and enzyme-linked immunospot assay used in the study</p><p>1-3</p><p>Table 1</p><p>In vitro reactivity to different components of ceftriaxone degradation products in patients with positive ELISpot to ceftriaxone or cephalosporine with similar R1 side chain to ceftriaxone (N=21), categorized by underlying diseases and drug allergic phenotypes</p><p>4</p><p>Table 2</p><p>Clinical characteristics of patients with a confirmed non-IgE mediated hypersensitivity reaction to ceftriaxone or cephalosporin with a similar R1 side chain to ceftriaxone</p><p>5-6</p><p>Table 3</p><p>Frequencies of IFN-γ releasing cells and proportions of positive IFN-γ ELISpot assay upon stimulation with the suspected culprit drugs and different cephalosporins in patients with a history of non-IgE mediated reaction to ceftriaxone or cephalosporins with a similar R1 side chain (N= 21)</p><p>7-9</p><p>Figure captions and Figure legends</p><p> </p><p>Figure captions and figure legends for figures 1, 2, 3A, 3B, and 4.</p><p> </p><p>10</p><p> </p><p>Figure 1</p><p> </p><p>Mass spectrum results of the synthesized octalysine-ceftriaxone conjugates</p><p> </p><p> Figure 2</p><p>Chemical structures of cephalosporin components in this study</p><p> </p><p>Fig 3</p><p>Representative figures of IFN-γ releasing cells after stimulating PBMCs with different cephalosporins and their components, as demonstrated by ELISpot assay in two patients with a history of ceftriaxone-induced DRESS. Figure 3A shows the results for a single cephalosporin reactor, and Figure 3B shows the results for a multiple cephalosporin reactor.</p><p> </p><p>Fig 4</p><p>The frequencies of IFN-γ releasing cells and proportions of positive IFN-γ ELISpot assay upon stimulation with the suspected culprit drugs, other cephalosporins, and different components in patients with a history of non-IgE mediated reaction to ceftriaxone or cephalosporins with a similar R1 side chain (N= 21)</p><p> </p><p> </p>
FIGURE 3 Principal Component Analyses plots. A in Does polyxenous symbiosis promote sympatric divergence? A morphometric and phylogeographic approach based on Oxydromus okupa (Annelida, Polychaeta, Hesionidae)
FIGURE 3 Principal Component Analyses plots. A: Based on size independent data. B: Based on character proportions. CI: Cadiz Intertidal (Scrobicularia plana); CS: Cadiz Subtidal (Macomopsis pellucida); CH: Chipiona intertidal (M. pellucida). Character abbreviations as in fig. 2.
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.