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.
15,459
datasets available to search
ShareScore release 0.9.0
Dataset results
15,459 results for “Factors”
Migration on the Chessboard: Political Violence as a Decisive Factor in Coercive Migration Diplomacy (Data and Associated Files for Dissertation)
<p>This publication contains files associated with analysis for my dissertation, "Migration on the Chessboard: Political Violence as a Decisive Factor in Coercive Migration Diplomacy." The dissertation explores a potential relationship between political violence and a state leader's choice to use migration as a bargaining chip in pursuit of foreign policy objectives. "Key to datasets.docx" and "dataframes_viz.png" explain the contents of the five datasets used. These five datasets are the five .dta files. There are five log files (.txt) and five do files containing code (.do) corresponding to the five datasets. Finally, each dataset has three associated results tables (.xls) for a total of fifteen .xls files. </p>
Demographic factors and the environmental Kuznets curve: global plastic pollution by 2050 could be 2 to 4 times worse than projected
<p>These data are made of two files. One file provides the observed data we collected and cleaned from the World Bank database. The second file provides the simulation results from the STIRPAT model we designed based on the observed data abovementioned. Our results can be summarised as follows:</p> <p>Since 2015, the detrimental effects of plastic pollution have attracted media, public, and governmental attention. Considering economic growth is inevitable and a key driver of plastic contamination, it is worthwhile to analyze the environmental Kuznets curve (EKC) relationship between economic development and plastic pollution. To this end, we contribute by being the first to (i) use the Stochastic Impacts by Regression on Population, Affluence, and technology model (STIRPAT model) to investigate this EKC relationship; (ii) provide a comprehensive analysis of how demographic factors affect plastic pollution; and (iii) use panel model techniques to examine the drivers of plastic pollution. Our empirical results support an inverted U-shaped relationship between plastic pollution and income. They show that at current trends, global plastic pollution (that is, annual discard of inadequately managed plastic waste) is expected to grow from 52 million tons per year in 2020 to 257 million tons per year in 2050.</p>
Altered nanoparticle uptake by lung carcinoma cells when stimulated with epidermal growth factor
<p>This dataset provides the raw data supporting the paper "Altered nanoparticle uptake by lung carcinoma cells when stimulated with epidermal growth factor". The focus of the study was to investigate the uptake of two different sizes of silica NPs and gold NPs in lung epithelial cells A549 in the presence of epidermal growth factor (EGF). </p> <p>The data set includes:</p> <ul> <li>Screening for EGF receptor using western blot and confocal microscopy (Figure 1 and Figure S1)</li> <li>Investigating expression of RAC1/CDC42 proteins upon EGF stimulation using Western blot (Figure 2)</li> <li>Investigating expression of RAC1 gene upon EGF stimulation using RT-qPCR (Figure S2)</li> <li>Evaluating uptake of endocytic markers upon EGF stimulation using confocal laser scanning microscopy (Figure 3, Figure S4) and flow cytometry (Figure 3)</li> <li>Nanoparticle characterization using TEM (Figure 4, Figure S6) and UV-Vis (Figure S5, Figure S6)</li> <li>Evaluating silica nanoparticle uptake upon EGF stimulation using confocal laser scanning microscopy (Figure 5, Figure S8) and flow cytometry (Figure 5)</li> <li>Evaluating gold nanoparticle uptake upon EGF stimulation using dark-field microscopy and ICP-AES (Figure 6)</li> <li>Investigating expression of c-MYC gene upon EGF stimulation using RT-qPCR (Figure 6)</li> <li>Cell viability results, analysed via lactate dehydrogenase assay (Figure S3) and MTS assay (Figure S9)</li> <li>Raw integrated density data from dark-field images (Figure S9)</li> </ul>
Measurements of savanna landscap fire emission factors for CO2, CO, CH4 and N2O using a UAV-based sampling methodology
<p>This dataset contains direct measurements of biomass burning emission factors for CO<sub>2</sub>, CO, CH<sub>4</sub> and N<sub>2</sub>O. It includes over 4500 EF bag measurements sampled using an unmanned aerial system (UAS), and measured fuel parameters and fire severity proxies during 129 individual fires. The measurements cover a variety of savanna ecosystems in Brazil, Australia, Botswana, Zambia, South-Africa and Mozambique under different seasonal conditions, sampled over the course of six fire seasons between 2017 and 2022. The table in the included word file explains the individual columns in the excell file. </p> <p> </p>
Risk factor prediction for Secondary Glaucoma amongst patients presenting with Pseudo exfoliation Syndrome (PEX) at Ophthalmology OPD in a Tertiary Care Centre in Ahmedabad
<p>Here we are uploading a data sheet of the<strong> "Risk factor prediction for Secondary Glaucoma amongst patients presenting with Pseudo exfoliation Syndrome (PEX) at Ophthalmology OPD in a Tertiary Care Centre in Ahmedabad." </strong></p>
Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data
<p><strong>Abstract:</strong></p> <p>The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.</p> <p><strong>Content:</strong></p> <ul> <li><a href="https://zenodo.org/record/7858848/files/01%20StudyDescription.pdf">01 StudyDescription.pdf </a> <ul> <li>Description of the field study.</li> <li>Information about the methods and materials used.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/02%20Dataset.csv">02 Dataset.csv</a> <ul> <li>The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/03%20VariableDescriptions_EnglishPersonQuestionnaire.pdf">03 VariableDescriptions_EnglishPersonQuestionnaire.pdf</a> <ul> <li>Descriptions of all variables, their measurement scale, scale ranges and levels.</li> <li>Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.</li> <li>English translations of questions asked in the person questionnaire.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/04%20ESM-Questionnaire.pdf">04 ESM-Questionnaire.pdf</a> <ul> <li>Screenshots of the original Experience Sampling Method questionnaire with English translations.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/05%20PersonQuestionnaire_OriginalGermanVersion.pdf">05 PersonQuestionnaire_OriginalGermanVersion.pdf</a> <ul> <li>Original version of the person questionnaire in German language.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/06%20HelpTexts.pdf">06 HelpTexts.pdf</a> <ul> <li>Descriptions of the study task.</li> <li>Explanations of the scales used in the questionnaire.</li> <li>Explanations of the sound categories and the soundscape composition.</li> <li>Explanation of the operation of the recording device.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_README.md">AcousticFeatures_README.md</a> <a href="https://zenodo.org/api/files/3d784540-c0f4-412f-8742-df1db6f5401d/TimeSeries_and_Spectrograms_README.md?versionId=9291496c-d2c6-4151-96f1-a2ad99e1a540"> </a> <ul> <li>Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.</li> <li>Analyis settings used in Artemis Suite to generate the acoustic features.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_SingleValues.csv">AcousticFeatures_SingleValues.csv</a> <ul> <li>All acoustic features, aggregated to single values per feature, recording, and channel.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectra.csv">AcousticFeatures_Spectra.csv</a> <ul> <li>Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectrograms.zip">AcousticFeatures_Spectrograms.zip</a> <ul> <li>13188 .csv files with un-weighted spetrograms of each channel of each recording.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_TimeSeries.zip">AcousticFeatures_TimeSeries.zip</a> <ul> <li>A .csv file containing LAeq and LZeq time series of each channel of each recording.</li> </ul> </li> </ul> <p><strong>Publications refering to this dataset:</strong></p> <p>Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes."</p> <p><strong>Funding:</strong></p> <p>This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6. </p> <p><strong>License: </strong></p> <p>CC 4.0 BY, <a href="https://creativecommons.org/licenses/by/4.0/legalcode">https://creativecommons.org/licenses/by/4.0/legalcode</a></p> <p><strong>Version history:</strong></p> <p>Details can be found in the <a href="https://zenodo.org/api/files/a15d6a91-1a35-4b5e-a7ec-da8a9bcbee2b/Changelog.md">Changelog.md</a> file.</p> <ul> <li> V.01.0. March 7, 2023: Initial publication. <a href="https://doi.org/10.5281/zenodo.7193938">https://doi.org/10.5281/zenodo.7193938</a></li> <li> V.01.1. April 25, 2023. <a href="https://doi.org/10.5281/zenodo.7858848">https://doi.org/10.5281/zenodo.7858848</a></li> </ul>
Analysis accompanying "Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the Drosophila larval brain"
<p>This repository documents the raw data processing and figure generation for the article “Dynamically regulated transcription factors are encoded by highly unstable mRNAs in the <em>Drosophila </em>larval brain”, doi: 10.1261/rna.079552.122.</p>
New Zealand Seismic Hazard Z Factors
<p>This dataset presents our interpretation of the <em>Z</em> factor as a continuous surface across New Zealand. The GeoTiFF has been derived through a range of publicly available online resources including the MBIE website, reports, journal publications, and the <a href="https://gazetteer.linz.govt.nz/">New Zealand Gazetter</a> for matching placenames to locations, amongst others. The coordinate system is EPSG:2193 with ~5 km resolution. The raster has a single band and values are rounded to two decimal places.</p> <p>The <em>Z</em> factor is used to scale the 5% damped design seismic response spectrum based on the magnitude of the expected seismic hazard in different regions in New Zealand, as demonstrated through <a href="https://www.standards.govt.nz/shop/nzs-1170-52004/">NZS 1170.5:2004</a> and referred to in the seismic assessment of potentially earthquake prone buildings (EPB). It is underpinned by the 2001 National Seismic Hazard Model and is influenced by a wide range of factors such as proximity to faults and fault rupture mechanisms, geological and soil characteristics, and topography, amongst others. <em>Z</em> ranges from 0.10 (Northland Region) to 0.60 (Otira/Arthur’s Pass surrounds). Generally speaking, low seismic risk is where <em>Z</em> < 0.15; medium seismic risk where 0.15 ≤ <em>Z</em> < 0.30; and high seismic risk where <em>Z</em> ≥ 0.30.</p> <p>This GIS dataset is intended for educational purposes where students can download the dataset, create their own contours, or directly sample the raster. For more information see the numerous online resources and the official standard <a href="https://www.standards.govt.nz/shop/nzs-1170-52004/">NZS 1170.5:2004</a> where it is available for purchase from Standards NZ.</p>
Common factor GWAS and TWAS output for nociplastic type pain
<p>file: GSEM_commonFactorGWAS_COPC_6trait_30MAY2023.csv.gz</p> <p>description: Common factor GWAS output for GenomicSEM analyses of 6 COPC traits (see doi: https://doi.org/10.1101/2023.06.27.23291959)</p> <p>columns:</p> <p>SNP = rsID SNP identifier </p> <p>CHR = chromosome</p> <p>BP = base pair position</p> <p>MAF = minor allele frequency</p> <p>A1 = effect allele</p> <p>A2 = other allele</p> <p>i = index (1 - n SNPs)</p> <p>lhs = left hand side of equation </p> <p>op = equation operator (lavaan syntax)</p> <p>rhs = right hand side of equation</p> <p>est = effect size (beta)</p> <p>se_c = standard error of effect estimate</p> <p>Z_Estimate = Z value</p> <p>Pval_Estimate = p value of effect</p> <p>Q = Q (heterogeneity) value</p> <p>Q_df = degrees of freedom for Q</p> <p>Q_pval = Q p value</p> <p>fail = GSEM fail message if applicable</p> <p>warning = GSEM warning message if applicable </p> <p>Z_smooth = smoothing parameter if applicable </p> <p>N_estimate = N estimate </p> <p> </p> <p>file: GSEM_commonFactorTWAS_COPC_6trait_30MAY2023.csv.gz</p> <p>description: Common factor TWAS output for GenomicSEM analyses of 6 COPC traits (see doi: https://doi.org/10.1101/2023.06.27.23291959)</p> <p>columns:</p> <p>Gene = ensembl gene ID </p> <p>Panel = which model (tissue+gene) i.e. reference weights file</p> <p>HSQ = gene heritability </p> <p>i = index (1 - n gene-tissue models)</p> <p>lhs = equation left hand side</p> <p>op = operator (lavaan syntax)</p> <p>rhs = equation right hand side</p> <p>est = association estimate (beta)</p> <p>se_c = standard error of beta</p> <p>Z_Estimate = Z value </p> <p>Pval_Estimate = p value of association test</p> <p>Q = Q (heterogeneity) value</p> <p>Q_df = degrees of freedom for Q</p> <p>Q_pval = p value for Q</p> <p>fail = GSEM fail message if applicable </p> <p>warning = GSEM warning message if applicable </p> <p>tissue = tissue</p> <p>p_bonf_tissue = adjusted p value - bonferroni adjustment within tissue </p> <p>p_fdr_tissue = adjusted p value - false discovery rate adjustment within tissue</p> <p>threshold_bonf_tissue = p value threshold for bonferroni adjustment within tissue </p> <p>p_bonf_experiment = adjusted p value - bonferroni adjustment experiment-wide</p> <p>p_threshold_bonf_experiment = p value threshold (bonferroni, experiment-wide)</p> <p>Q_bonf_tissue = adjusted p value for Q, bonferroni within-tissue </p> <p> </p>
CollecTRI Data for Investigation of SETBP1 gene expression and transcription factor activity across human tissues
<p>Here we provide the human CollecTRI prior (accessed May 2023) for inference of TF activity across 31 GTEx tissues using multivariate linear modeling method decoupleR.<br> <br> The `human_prior_tri.csv` includes 1,178 unique TFs (referred to as the source) that target 6,627 unique genes (referred to as targets) to give us 42,595 interactions in the CollecTRI prior input. Interactions are represented as a + or - 1 (mor).</p>
Identification of factors determining the process of aggregation/agglomeration of metal oxide nanoparticles in a biological medium
<p>The model allows to identify factors determining the process of aggregation/agglomeration of metal oxide nanoparticles in a biological medium and to verify the importance of ion adsorption and protein adsorption in this process. </p> <p>Model confirms the significant effect of protein adsorption on the hydrodynamic diameter of metal oxide particles in the biological medium, and does not confirm the significant effect of ion adsorption in this process. It’s an example of modeling the properties of nanoparticles, where apart from the descriptors describing the structure of nanoparticles, there are also parameters characterizing the medium.</p>
The influence of environmental factors on the distribution and density of invasive Centaurea stoebe across Northeastern USA, 2013 - 2018
Centaurea stoebe (Asteraceae; spotted knapweed) is an emerging invader in northeast US, and is a major invasive plant in the northern Midwest and western USA. Although it has been present in New York State (NYS) for over 100 years, its apparent recent population increases and spread provide a rare opportunity to study a plant in the early stages of invasion. Therefore, a study was carried out understand how distinct environmental factors influence the distribution, density and change in density C. stoebe at different spatial scales within its novel range in the northeastern USA. First, we collected field data on the occurrence, density and change in density of this species in North Eastern United States, from 2013 to 2014. Then, using species distribution models, we assessed the potential influence of environmental factors on the invasion of spotted knapweed in northeast US. Within different parts of C. stoebe‘s range, different factors explained its occurrence, density and change in density over 2 years. Across northeast US, climate and soil factors were the most influential predictors explaining C. stoebe‘s distribution, while within Long Island in southeastern NYS and the Adirondack Mountains in northern NYS, precipitation and disturbance respectively were the most important. These results are published in the paper titled The influence of environmental factors on the distribution and density of invasive Centaurea stoebe across Northeastern USA (Akin-Fajiye and Gurevitch, 2018).
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
<p>Data used to test the robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data, described in <a href="https://doi.org/10.1186/s13059-020-1949-z">Holland et al. 2020</a>.</p> <p>The folder <em>data </em>contains<em> </em>raw data and the folder <em>output</em> contains intermediate and final results of all analyses. </p> <p>The associated analyses code and more information are available on <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq">GitHub</a>.</p> <p> </p> <p><strong>Abstract</strong></p> <p><strong>Background</strong></p> <p>Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.</p> <p><strong>Results</strong></p> <p>To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.</p> <p><strong>Conclusions</strong></p> <p>Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.</p> <p> </p> <p>For questions related to the data please write an email to christian.holland@bioquant.uni-heidelberg.de or use the <a href="https://github.com/saezlab/FootprintMethods_on_scRNAseq/issues">GitHub issue system</a>.</p>
Factors affecting altmetrics attention to scholarly publication in peer-reviewed journals published in Iran and Turkey
<p>The goal of this study was to trace the altmetric measures of peer-reviewed journals in two non-English speaking countries na,ely Iran and Turkey, in order to understand their correlation with some website structure and design determinants, as well as the subject and the full-text language of the journals.</p> <p> </p>
Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"
<p>Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"</p>
Capacity factors for wind turbines
<p>Simulated capacity factors in Finland for six wind turbine models, Vestas V90-3.0 MW, V90-2.0 MW, V112-3.3 MW, V126-3.3 MW, V117-3.45 MW and V136-3.45 MW at four turbine hub heights 75, 100, 125, 150 m. Wind speed data are from Finnish Wind Atlas [1, 2], from which the Weibull distribution shape and scale parameters (labelled ‘Weibull all data k’ and ‘Weibull all data A’, respectively) and the frequencies of the wind sectors (‘Frequency all data’) were used.</p> <p>File <em>FWA_coordinates_2500m.csv</em> holds the geographical coordinates (WGS 84) of the Wind Atlas in 2.5×2.5 km<sup>2</sup> resolution.</p> <p>To simulate a wind farm where each turbine experiences a slightly different wind speed, we used a normal distribution with variance <span class="math-tex">\(\sigma^2(v) = 0.2v + 0.6\,\mathrm{m/s}\)</span>, (where <em>v</em> is wind speed) to smooth (convolute) the original power curves [3, 4].</p> <p>The calculation of capacity factor cf at wind atlas grid point k is described by the formula<br> <span class="math-tex">\(\mathit{CF}_k = \mathop{\mathbb{E}}_{i, s} g(v_i) \approx \sum_{s=1}^{12} f_{k,s} \sum_{i=1}^N p_{k,s}(v_i) g(v_i) \Delta v\)</span>,<br> where g(v) is the power curve function for current wind turbine model, vi the mean wind speed of bin i, fk,s the frequency of occurrence of wind direction s at point k, N the number of wind speed bins, pk,s(v) the Weibull probability density function for sector s at point k at the hub height and Δv the width of the wind speed bin.</p> <p><strong>References</strong></p> <ol> <li>Finnish Meteorological Institute, “Finnish Wind Atlas,” 2008. [Online]. Available: <a href="http://www.windatlas.fi">http://www.windatlas.fi</a>. [Accessed: 28-Jun-2016]</li> <li>B. Tammelin, T. Vihma, E. Atlaskin, J. Badger, C. Fortelius, H. Gregow, M. Horttanainen, R. Hyvönen, J. Kilpinen, J. Latikka, K. Ljungberg, N. G. Mortensen, S. Niemelä, K. Ruosteenoja, K. Salonen, I. Suomi, and A. Venäläinen, “Production of the Finnish Wind Atlas,” Wind Energy, vol. 16, no. 1, pp. 19–35, Jan. 2013.</li> <li>Staffell, Iain, and Richard Green. 2014. “How Does Wind Farm Performance Decline with Age?” Renewable Energy 66. Elsevier Ltd: 775–86. doi:10.1016/j.renene.2013.10.041.</li> <li>Staffell, Iain, and Stefan Pfenninger. 2016. “Using Bias-Corrected Reanalysis to Simulate Current and Future Wind Power Output.” Energy 114 (November): 1224–39. doi:10.1016/j.energy.2016.08.068.</li> </ol> <p> </p>
Convolutional Neural Net (CNN) models for ENCODE-Roadmap DNase-seq peaks and Transcription Factor ChIP-seq peaks - Basset architecture
<p>Deep learning models trained on epigenomic landscapes from ENCODE and Roadmap Epigenomics. The models are Basset convolutional neural networks (Kelley, et al 2016). The dataset used to train these models can be found at https://doi.org/10.5281/zenodo.4059038. The file `nn.encode-roadmap.models.basset.clf.tar.gz` contains 10 cross-validated models in Tensorflow framework files as well as details on the architecture, cross-validation scheme, and training of these models. The file `nn.encode-roadmap.models.basset.clf.np_weights.tar.gz` contains the 10 cross-validated models' weights extracted to numpy array files (.npz).</p>
The Psychological Burden of the COVID-19 Pandemic and Its Associated Factors among the Frontline Doctors of Bangladesh: A Cross-sectional Study-Extended Data
<p>Using this document, we tried to assess the mental health status of the frontline doctors of Bangladesh during Coronavirus 2019 pandemic.</p>
Supplementary Videos: The N-Terminal Helix-Turn-Helix Motif of Transcription Factors MarA and Rob Drives DNA Recognition
<p>Supplementary Movies associated with the following work: "The N-Terminal Helix-Turn-Helix Motif of Transcription Factors MarA and Rob Drives DNA Recognition", available as a preprint on chemRxiv: https://chemrxiv.org/articles/preprint/The_N-Terminal_Helix-Turn-Helix_Motif_of_Transcription_Factors_MarA_and_Rob_Drives_DNA_Recognition/12195372 </p>
Association between meteorological factors and the number of tuberculosis notifications: a time-series study in Hong Kong
<p> Using a 22-year consecutive surveillance data in Hong Kong, including monthly averages of meteorological factors, air pollution concentrations , total number of TB cases notified, to analyze the association of monthly average temperature and relative humidity with temporal dynamics of monthly total number of TB cases notified. </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.