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.
263
datasets available to search
ShareScore release 0.7.1
Dataset results
263 results for “impact factor”
JCR Journals, sorted by Impact Factor 2011 with the JCR edition indication
Description of the spreadsheet: “Journals in JCR sorted by IF’11” lists the journals from Thomson Reuters JCR website; it’s sorted by edition (science and social science) and Impact Factor 2011 descending (but not difunded). Fields: Abbreviated Journal title, ISSN, JCR ed. Methodology: 1. We copy and paste from the web pages the list in a unique spreadsheet. 2. We agregate the JCR edition: SCI=1 and SSCI=2. 3. We sort by Edition and Impact Factor and delete this column values. 4. We upload the excel file to data banks
Global dataset for evaluating impact of topographic factors on hydrologic response to climate variability
<p>The dataset contained here was used to document the biomes in the world that show high sensitivity in their hydrologic response to interannual changes in climatic forcing during the 2001-2016 period, while evaluating the role of major topoclimatic factors in modulating these responses. To do this we generated a hydrologic sensitivity index (HSi). HSi evaluates the absolute ratio between the changes of the climatic conditions (dryness index, DI) and hydrologic response (evaporative index, EI<sub>R</sub>) between consecutive years (e.g. HSi= |∆ EI<sub>R</sub> /∆ DI|). HSi was computed for every successive pair of years from 2001 to 2016. A total of 15 HSi maps were obtained representing the HSi for each consecutive pair of years. For each map, where HSi >1, regions are classified as <strong><em>Sensitive</em></strong> and for HSi ≤1, <strong><em>Resilient</em></strong>. To provide a synthesis of the general trend of global hydrologic sensitivity, we display the frequency of HSi, showing the recurrence of HSi >1 for every non-ocean location with a range of 0 (low frequency) to 15 (high frequency). Regions where frequency HSi≥7 are considered highly recurring and as such are deemed as the most hydrologically sensitive. </p> <p><strong>This dataset includes the code and raster data to evaluate the effect of the topography on HSi to plot the average frequency HSi for all elevations, aspects, and slope steepness against latitudinal change.</strong> We used global digital elevation models (DEMS) from the Shuttle Radar Topography Mission (SRTM) data (90 m resolution; version 4, for latitudes < 60◦ N and GTOPO30 (1◦ resolution; http://lta.cr.usgs.gov/GTOPO30) for latitudes > 60◦ N. Slope and aspect maps were derived from the DEMs using standard GIS-based methods in ArcMap 10.7.Elevation range used is [0,7000] meters above sea level (m.a.s.l), aspect (N, NE, E, SE, S, SW, W, NW) specifically above slope values greater than 10-degrees (no flat areas used), and slope [0,90] degrees.</p> <p><strong>Contents:</strong></p> <ul> <li>1 MATLAB with the code ready to use</li> <li>1 PDF file with the same code</li> <li>27 geotiff files for elevation (dem#1-27.tif)</li> <li>27 geotiff files for frequency HSi (freq#1-27.tif) </li> </ul> <p>Note: the following files of slope and aspect could not upload in repository due to exceedance in storage limit: 50MG. The DEM files must be run in ArcMap using slope and aspect tool to produce the following files with the following names.</p> <ul> <li>27 geotiff files for slope (slope#1-27.tif)</li> <li>27 geotiff files for aspect (aspect#1-27.tif)</li> </ul>
Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic
<ul> <li>Supporting datasets for paper "Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic". </li> <li>Those are a subset of the (analyzed) datasets from WRF control simulation "ERA5" in netcdf format. See manuscript for more details. <ul> <li>cld_size.nc: cloud object size</li> <li>cld_ort_2020-03-01_15_00_00.nc: cloud object at 15:00 UTC</li> <li>hydro-02-2020-03-01_15/00/00.nc: water path sample data at 15:00 UTC</li> <li>wrfout_d02_2020-03-01_15/00/00: wrf output sample data at 15:00 UTC</li> </ul> </li> </ul>
Toward a base-resolution panorama of the in vivo impact of cytosine methylation on transcription factor binding
<p>TF binding models built by JAMS (https://github.com/csglab/JAMS), ChIP-seq peak files (from ENCODE, Najafabadi et al. 2015, Schmitges et al. 2016, and Imbeault et al. 2017; called by MACS 1.4v), ChIP-seq pulldown and control tags from said peaks, input data for JAMS, and RCADE2 motifs for C2H2 zinc finger proteins. </p>
Evaluation of transcription factor knockout impact on paclitaxel response for Triple Negative Breast Cancer
<div>Data and code related to Zenodo repository: 10.5281/zenodo.11238552</div> <div> </div> <div>Two experimental formats included:</div> <div>'fixed' prefix: data from terminal time point of siRNA screen applied to HCC1143, HCC1806, and MDA-MB-468 Triple Negative Breast Cancer cell lines.</div> <div>'live' prefix: data from live-cell imaging of cell cycle reporter (HDHB-mClover/NLS-mCherry) HCC1143 Triple Negative Breast Cancer cell line.</div> <div>Note: 'live' level 1 data is available upon request (heiserl@ohsu.edu, calistri@ohsu.edu).</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate whether siRNA knockdown of transcription factors elevated during paclitaxel response impact cell count, cell morphology or cycling dynamics.</div> <div> </div> <div>Methods:</div> <div>siRNA Knockdown: Cells were plated in 90ul of serum free media per well of a 96 well plate. 24 hours later, siRNA knockdown mixture was prepared using a cell-line optimized concentration of Lipofectamine RNAiMAX (cat 13778075-075, Invitrogen) and siRNA (Horizon Discovery ON-TARGETplus) following RNAiMAX recommended protocol. The final concentration of siRNA per well was 1pmol and the final volume of RNAiMAX per well was 75nL for HCC1143, and 37.5nL for HCC1806 or MDA-MB-468 in 100uL of cell containing volume. 24 hours after siRNA transfection cells were treated with an addition of 100uL complete media containing either DMSO vehicle control or paclitaxel. </div> <div> </div> <div>Fixed-cell assays: Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask Green in PBS (H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 50, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>HDHB reporter live-cell assays: siRNA knockdown and drug treatment was performed as described above, and then the plate was loaded on an Incucyte S3 (Sartorious) and cells imaged every 15 minutes for 72 hours post drug treatment. At each timepoint 4 fields of view were captured at 20x magnificantion in each well using the phase, red and green channels. A cytoplasmic mask was computed from the mean of normalized red/green channel (cellpose parameters: diameter = 57, chan = mean(normalized(red), normalized(green)), and a nuclear mask was computed from the red channel (cellpose parameters: diameter = 30, chan = DAPI) using custom trained Cellpose models. Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0). An additional perinuclear ring mask was computed as the 11 pixel dilation from the nuclear mask, but still bound by the cytoplasmic mask. To determine mClover localization thresholds for cell cycle assignment, 250 cell images were randomly selected and manually assigned to the G1, S/G2 or M cell cycle state based on mClover localization. The mClover intensity ratios were then used to determine thresholds for automated cell cycle phase calling which was applied to the rest of the data set (Supplemental Figure 5A). Mononuclear cells with a Perinuclear:Nuclear mean intensity ratio greater than 0.8 and Nuclear:Cytoplasmic total intensity less than 0.5 were assigned to the S/G2 phase. Mononuclear and Multinuclear cells with a Nuclear:Cytoplasmic total intensity ratio greater than 0.8 and Perinuclear:Nuclear mean intensity ratio less than 0.8 were assigned to the ‘M’ phase. The remainder of mononuclear cells were assigned ‘G1’, and the remainder of multinucleated cells were assigned ‘Multinucleated’. </div> <div> </div> <div>Included files:</div> <div>fixed_level_1-plate_#.zip : Six .zip archives containing the raw images (DAPI/CellMask/Brightfield) from fixed-cell experiments.</div> <div>plate 1: HCC1143 cells treated with plate A schema</div> <div>plate 2: HCC1143 cells treated with plate B schema</div> <div>plate 3: HCC1806 cells treated with plate A schema</div> <div>plate 4: HCC1806 cells treated with plate B schema</div> <div>plate 5: MDA-MB-468 cells treated with plate A schema</div> <div>plate 6: MDA-MB-468 cells treated with plate B schema</div> <div>fixed_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>fixed_level_3: Data from 'fixed_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>fixed_incell_to_cellpose.rmd: R markdown code for converting original incell files (fixed_level_1) to RGB images for cellpose segmentation</div> <div>fixed_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (fixed_level_1)</div> <div>fixed_cellpose_models.zip: Archive including cellpose models used for fixed experiment</div> <div>live_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>live_level_3: Data from 'live_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>live_level_4: Data from 'live_level_3.csv' collapsed to the single condition level summarizing the number, multinucleation status and phase of cells at each time point.</div> <div>live_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (live_level_1).</div> <div>l ive_incu_archive2rgb.rmd: R markdown code for converting incucyte archive formatted data into RGB images, where the blue channel is the arithmetic mean of the min-max (0-1) normalized red and green channels.</div> <div>live_cellpose_models.zip: Archive including cellpose models used for live experiment.</div> <div> </div> <div> </div>
Datasets for publication: 'Measuring the excellence contribution at the journal level: An alternative to Garfield's Impact Factor'
<p>Datasets for publication: 'Measuring the excellence contribution at the journal level: An alternative to Garfield's Impact Factor'.</p> <p><strong>Overview.</strong> Overview of the number of journals, publications, excellent publications and multidisciplinarity for each category considered.</p> <p><strong>ALL.</strong> Journal indicators for all the document types by JCR category.</p> <p><strong>ALL_JCR.</strong> Journal indicators for all the document types by JCR category (only journals indexed in the JCR category are taken into account).</p> <p><strong>AR.</strong> Journal indicators for only articles and reviews by JCR category.</p> <p><strong>AR_JCR. </strong> Journal indicators for only articles and reviews by JCR category (only journals indexed in the JCR category are taken into account).</p>
Fig. 3 in Waterbird Distribution Patterns And Environmentally Impacted Factors In Reclaimed Coastal Wetlands Of The Eastern End Of Nanhui County, Shanghai, China
Fig. 3. Non-metricmulti-dimensionalscaling(NMDS) ordinationplotsshowingwaterbird communitystructurefromsixstudysites.
The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long-term Streamflow Components
<p>The excel file <em>Attributes_manuscript.csv</em> contains the mean annual variables and the catchments' attributes used in the manuscript: "<strong>The Impact of an Open Water Balance Assumption on Understanding the Factors Controlling the Long-term Streamflow Components</strong>". Details of each attributes are indicated in the .txt file <em>Attributes_description.txt</em>.</p>
Fig. 1 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 1. Spatial distribution of ignitions in 2010–2014 on studied area (dotted line is ATO zone's limits in 1.06– 30.09.2014).
Fig. 5 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 5. Distribution of two snake species, H. caspius and E. dione, in Ukrainian East (ATO zone is indicated by dotted line, burnt area marked inside zone).
Fig. 3 in A Review Of Major Impact Factors Of Hostilities Influencing Biodiversity In The Eastern Ukraine (Modeled On Selected Animal Species)
Fig. 3. Spatial local distribution of ignitions in 2010–2014 in the outskirts of Slavyanoserbsk, Luhansk Region.
The impact of stress and its influencing factors among dentists during the COVID-19 pandemic in Kingdom of Bahrain
<p><strong><span>Background:</span></strong><span> It is well known that all medical professions are linked to work stress, including dentistry, which is seen as facing higher risk due to the nature of the job, especially the working conditions. </span></p> <p><span><strong>Objective:</strong> This study aimed to assess the impact of stress and its influencing factors among dentists during the COVID-19 pandemic in Bahrain.</span></p> <p><strong><span>Methods</span></strong><span><strong>:</strong> A cross-sectional survey was designed to assess the impact of stress and its influencing factors among Bahraini dentists. A total of 306 participants were randomly selected from 1489 registered professionals in the NHRA (National Health Regulatory Authority Bahrain). In addition, an online survey was used to minimise face-to-face communication as well as to accommodate dental practitioners who work in private and government hospitals in Bahrain and a convenient sample of dentists was requested to participate in this study.</span></p> <p><strong><span>Results</span></strong><span><strong>:</strong> Out of 306 participants invited in the survey, only 253 responded, which was adequate for the study. Overall, the participants have reported moderate stress. All the variables of the study showed different effects, but the highest stressor with a strong correlation was "fear of social isolation "(FI) at the significance level of 0.01 (β= 0.393, t= 5.090, p < 0.05= (0.000) with </span> <span>= 0.201 above 0.15 and less than 0.35 which was considered as a moderate effect size approximately (20%), which strongly supported the hypothesis that researchers have proposed. Overall, the total effect for all stressors were (30 %) which was considered as a moderate effect size. All hypotheses were supported except BCP -> OUTCOME due to insufficient evidence at the insignificant level of 0.01 (β= -0.184, t=1.560, p > 0.05 = (0.060). whereas the R² values of independent variables were above 95% for the variance of dentists' outcome, which is considered an excellent fit to the data as evidenced by the squared multiple correlations (</span> <span>) values for the dependent variables.</span></p> <p><span><strong>Conclusions:</strong> </span><span>The study is unique based on its findings that reveal the impact of stress among dentists. Moreover, the results of this study may serve as guidance for future monitoring of dental practitioners' burnout, anxiety, and workload. Furthermore, it may provide supports in different aspects.</span></p>
Impacts of abiotic and biotic factors on terrestrial leeches in
<p>Haemadipsid leeches are ubiquitous inhabitants of tropical and sub-tropical forests in the Indo-Pacific region. They are increasingly used as indicator taxa for biomonitoring, yet very little is known about their basic ecology. For example, to date no study has assessed the occurrence and distribution of haemadipsid leeches across naturally occurring gradients within intact habitats. We analysed a long-term data set (2012-2020) on the closely related tiger (Haemadipsa picta) and brown (Haemadipsa spp.) leech species to investigate if and how abiotic and biotic factors influence their occurrence across a gradient of forest types at an undisturbed tropical rainforest site in Indonesian Borneo. We compared a series of negative binomial mixed models and found that, of the abiotic factors, soil moisture had the largest positive effect on encounter rates of both leech species. Among biotic factors, forest type had differential effects on counts of the two species: while tiger leech counts were greater in low elevation forest types, brown leech counts were greater in high elevation forest types. Additionally, we found that the presence of one species had a positive effect on the presence of the other species. Finally, our results show that the tiger leech has a narrower distribution, being restricted to lower elevation forest types with higher water retention, suggesting that the tiger leech could be more sensitive to lower soil moisture levels.</p>
Deliverable 2.4: Biodiversity impact database including characterization factors and documentation ready for use in Module C
<p><span>We quantify the impacts of agriculture and livestock, on biodiversity (including intensity of use), and translate the results of these modeling efforts which indicate current patterns of biodiversity indicators in both South America and Africa (as well as potential biodiversity loss) to data inputs to be further used in CLEVER. </span></p>
Fig. 3 in The correlations between certain features of the journal Neotropical Ichthyology and its impact factor: a comparative analysis at the thematic and national levels
Fig. 3. Correlation between average IF and uncitedness rate of journals on zoology in Sample 1 between 2006 and 2010. The highlighted represents the data for Neotropical Ichthyology.
Companion for "OpenMP and StarPU Abreast: the Impact of Runtime in Task-Based Block QR Factorization Performance"
<p>This is the companion website for the paper entitled *OpenMP and StarPU Abreast: the Impact of Runtime in Task-Based Block QR Factorization Performance* by Marcelo Cogo Miletto and Lucas Mello Schnorr that has been submited to Simpósio de Sistemas<br> Computacionais de Alto Desempenho (WSCAD) - 2019. This repository provides all the data and the code snippets used to generate the figures that are discussed inside the article, in a way that you can use them to reproduce our steps.</p>
Figure 2 in Impacts of environmental factors on zooplankton taxonomic diversity in coastal lagoons in Turkey
Figure 2. Venn diagram showing identified zooplankton taxa distribution and number of taxa (in parentheses) in the lagoons.
Supplementary material for: RSAT variation-tools: An accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding
<p>Supplementary Material for the Article</p> <p>Santana-Garcia, W., Rocha-Acevedo, M., Ramirez-Navarro, L., Mbouamboua, Y., Thieffry, D., Thomas-Chollier, M., Contreras-Moreira, B., van Helden, J., Medina-Rivera, A., 2019. RSAT variation-tools: An accessible and flexible framework to predict the impact of regulatory variants on transcription factor binding. Comput. Struct. Biotechnol. J. 17, 1415–1428.</p> <p> </p>
Data for "Measuring Back: Bibliodiversity and the Journal Impact Factor brand. A Case study of IF-journals included in the 2021 Journal Citations Report."
<p>This is the open data for the preprint "Measuring Back: Bibliodiversity and the Journal Impact Factor brand. A Case study of IF-journals included in the 2021 Journal Citations Report."</p>
Figure 1 in Impact of climatic factors on sexual size dimorphism in ground beetle Pterostichus melanarius (Illiger, 1798) (Coleoptera, Carabidae)
Figure 1. Elytra length variation in P. melanarius from different habitats (a – females, b – males). Habitats are designated as follows: 1 – meadow, 2 – birch-forest, 3 – elm, 4 – oak-wood, 6 – pine forest, 7 – willow, 8 – shrubs, 9 – lawn, 10 – fir-forest, 11 – garden, 12 – rape field.
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.