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.
1,582
datasets available to search
ShareScore release 0.9.0
Dataset results
1,582 results for “manuscript”
Additional data for manuscript "Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data"
<p>Additional data for manuscript "Alevin-fry unlocks rapid, accurate, and memory-frugal quantification of single-cell RNA-seq data".</p> <p>Additional mitochondrial gene sequences for Danio rerio, Homo sapiens, and Mus musculus.</p>
Dataset for a manuscript entitled Variation in CO2 and CH4 Fluxes Among Land Cover Types in Heterogeneous Arctic Tundra in Northeastern Siberia
<p>Dataset for the manuscript Variation in CO<sub>2</sub> and CH<sub>4</sub> Fluxes Among Land Cover Types in Heterogeneous Arctic Tundra in Northeastern Siberia authored by Sari Juutinen, Mika Aurela, Juha-Pekka Tuovinen, Viktor Ivakhov, Maiju Linkosalmi, Aleksi Räsänen, Tarmo Virtanen, Juha Mikola, Johanna Nyman, Emmi Vähä, Marina Loskutova, Alexander Makshtas, and Tuomas Laurila</p> <p>Dataset consists of CO2 and CH4 flux data measured mainly using chamber method in arctic tundra</p>
Data for manuscript: "Themes in Academic Literature: Prejudice and Social Justice"
<p>This data set contains frequency counts of target words in 175 million academic abstracts published in all fields of knowledge. We quantify the prevalence of words denoting prejudice against ethnicity, gender, sexual orientation, gender identity, minority religious sentiment, age, body weight and disability in SSORC abstracts over the period 1970-2020. We then examine the relationship between the prevalence of such terms in the academic literature and their concomitant prevalence in news media content. We also analyze the temporal dynamics of an additional set of terms associated with social justice discourse in both the scholarly literature and in news media content. A few additional words not denoting prejudice are also available since they are used in the manuscript for illustration purposes.</p> <p>The list of academic abstracts analyzed in this work was taken from the Semantic Scholar Open Research Corpus (SSORC). The corpus contains, as of 2020, over 175 million academic abstracts, and associated metadata, published in all fields of knowledge. The raw data is provided by Semantic Scholar in accessible JSON format.</p> <p>Textual content included in our analysis is circumscribed to the scholarly articles’ titles and abstracts and does not include other article elements such as main body of text or references section. Thus, we use frequency counts derived from academic articles’ titles and abstracts as a proxy for word prevalence in those articles. This proxy was used because the SSORC corpus does not provide the entire text body of the indexed articles. Targeted textual content was located in JSON data and sorted by year to facilitate chronological analysis. Tokens were lowercased prior to estimating frequency counts.</p> <p>Yearly relative frequencies of a target word or n-gram in the SSORC corpus were estimated by dividing the number of occurrences of the target word/n-gram in all scholarly articles within a given year by the total number of all words in all articles of that year. This method of estimating word frequencies accounts for variable volume of total scientific output over time. This approach has been shown before to accurately capture the temporal dynamics of historical events and social trends in news media corpora.</p> <p>It is possible that a small percentage of scholarly articles in the SSORC corpus contain incorrect or missing data. For earlier years in the SSORC corpus, abstract information is sometimes missing and only article’s title information is available. As a result, the total and target word count metrics for a small subset of academic abstracts might not be precise. In a data analysis of 175 million scientific abstracts, manually checking the accuracy of frequency counts for every single academic abstract is unfeasible and hundred percent accuracy at capturing abstracts’ content might be elusive due to a small number of erroneous outlier cases in the raw data. Overall, however, we are confident that our frequency metrics are representative of word prevalence in academic content as illustrated by Figure 2 in the main manuscript, which shows the chronological prevalence in the SSORC corpus of several terms associated with different disciplines of scientific/academic knowledge.</p> <p>Factor analysis of frequency counts time series was carried out only after Bartlett’s test of sphericity and Kaiser-Meyer-Olkin (KMO) test confirmed the suitability of the data for factor analysis. A single factor derived from the frequency counts time series of prejudice-denoting terms was extracted from each corpus (academic abstracts and news media content). The same procedure was applied for the terms denoting social justice discourse. A factor loading cutoff of 0.5 was used to ascribe terms to a factor. Chronbach alphas to determine if the resulting factors appeared coherent were extremely high (>0.95).</p> <p>The textual content of news and opinion articles from the outlets listed in Figure 5 of the main manuscript is available in the outlet's online domains and/or public cache repositories such as Google cache (https://webcache.googleusercontent.com), The Internet Wayback Machine (https://archive.org/web/web.php), and Common Crawl (https://commoncrawl.org). We used derived word frequency counts from original sources. Textual content included in our analysis is circumscribed to articles headlines and main body of text of the articles and does not include other article elements such as figure captions.</p> <p>Targeted textual content was located in HTML raw data using outlet specific xpath expressions. Tokens were lowercased prior to estimating frequency counts. To prevent outlets with sparse text content for a year from distorting aggregate frequency counts, we only include outlet frequency counts from years for which there is at least 1 million words of article content from an outlet. </p> <p>Yearly frequency usage of a target word in an outlet in any given year was estimated by dividing the total number of occurrences of the target word in all articles of a given year by the number of all words in all articles of that year. This method of estimating frequency accounts for variable volume of total article output over time.</p> <p>The list of compressed files in this data set is listed next:</p> <p>-analysisScripts.rar contains the analysis scripts used in the main manuscript and raw data metrics</p> <p>-scholarlyArticlesContainingTargetWords.rar contains the IDs of each analyzed abstract in the SSORC corpus and the counts of target words and total words for each scholarly article</p> <p>-targetWordsInMediaArticlesCounts.rar contains counts of target words in news outlets articles as well as total counts of words in articles</p> <p>In a small percentage of news articles, outlet specific XPath expressions can fail to properly capture the content of the article due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. As a result, the total and target word counts metrics for a small subset of articles might not be precise. </p> <p>In a data analysis of millions of news articles, we cannot manually check the correctness of frequency counts for every single article and hundred percent accuracy at capturing articles’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our frequency metrics are representative of word prevalence in print news media content (see Rozado, Al-Gharbi, and Halberstadt, “Prevalence of Prejudice-Denoting Words in News Media Discourse" for supporting evidence).</p> <p>31/08/2022 Update: There is a new way to download the Semantic Scholar Open Research Corpus (see https://github.com/allenai/s2orc). This updated version states that the corpus contains 136M+ paper nodes. However, when I downloaded a previous version of the corpus in 2021 from http://s2-public-api-prod.us-west-2.elasticbeanstalk.com/corpus/download/ I counted 175M unique identifiers. The URL of the previous version of the corpus is no longer active, but it has been cached by the Internet Archive at https://web.archive.org/web/20201030131959/http://s2-public-api-prod.us-west-2.elasticbeanstalk.com/corpus/download/ I haven't had the time to look at the specific reason for the mismatch but perhaps the newer version of the corpus has cleaned a lot of noisy entries in the previous version which often contained entries with missing abstracts. Filtering out entries in low prevalence languages other than English might be another reason. In any case, Figure 2 of the main manuscript of this work (at https://www.nas.org/academic-questions/35/2/themes-in-academic-literature-prejudice-and-social-justice) should provide support for the validity of the frequency counts.</p> <p> </p>
Project files provided as supporting information to the manuscript "Membrane binding of pore-forming gamma-hemolysin components studied at different lipid compositions"
<p><strong>Project files provided as supporting information to the manuscript "Membrane binding of pore-forming gamma-hemolysin components studied at different lipid compositions"</strong></p> <p>The dataset contains the following folders:</p> <p>- number_of_contacts: files with the number of contacts between the rim domains of LukF and Hlg2 and the membrane, for different bilayer compositions (Fig. 2).</p> <p>- binding_events: files with the duration of the time interavals in which LukF and Hlg2 are bound to the membrane, and the scripts used to compute for each system the number of binding/unbinding events and the average membrane residence time (Fig. 3).</p> <p>- electrostatic_potential: files of the surface electrostatic potential produced with the adaptive Poisson-Boltzmann solver and used for visualization with Chimera (Fig. 4).</p> <p>- angles: files with the angle values computed between the protein axis and the z-axis of the simulation box (Fig. 5).</p> <p>- contacts_per_residue: files with the number of frames in which each protein residue is in contact with the membrane, with respect to the total number of frames in which the rim domain interacts with the bilayer (Fig. 5).</p> <p>- distance_protein_membrane: files with the minimum distance between the protein and the membrane (Fig. 6).</p> <p>- binding_sites: file produced by PyLipid with relevant information on the main DOPC binding sites identified in LukF.</p> <p>- min_distance_per_residue: files with the minimum distance between each protein residue and the membrane, computed at the binding steps (Fig. S5).</p>
Data sets used to demonstrate the software MadHitter in the manuscript "The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via a Single-Cell Perspective"
<p>This is a zip archive of nine single-cell RNASeq data sets used in the manuscript entitled:</p> <p>"The Landscape of Receptor-Mediated Precision Cancer Combination Therapy Via A Single-Cell Perspective" by Saba Ahmadi, Pattara Sukprasert, Rahulsimham Vegesna, Sanju Sinha, Fiorella Schischlik, Natalie Artzi, Samir Khuller, Alejandro A. Schaffer, Eytan Ruppin,</p> <p>The README.txt describes the data sets in detail.</p> <p>The associated software can be found at https://github.com/ruppinlab/madhitter</p>
Dataset and processing scripts for manuscript titled "Broadband polarization insensitivity and high detection efficiency in high-fill-factor superconducting microwire single-photon detectors"
<p>The manuscript "Broadband polarization insensitivity and high detection efficiency in high-fill-factor superconducting microwire single-photon detectors" contains results for high-efficiency, low-polarization-sensitivity superconducting microwire single-photon detectors. All of the data gathered and the python scripts used for processing is being provided as a zip archive, along with a digital signature file. The dataset contains crucial procedures regarding the equipment calibrations used for accurate device efficiency measurements.</p>
Dataset: digital photographs of specimens in manuscript entitled Taxonomic revision of the mydas-fly genera Eremohaplomydas Bequaert, 1959, Haplomydas Bezzi, 1924, and Lachnocorynus Hesse, 1969 (Insecta: Diptera: Mydidae)
<p>This is the original files containing the information published in digital specimens and photo records here in Zenodo.</p>
Project files provided as supporting information to the manuscript "Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework"
<p><strong>Project files provided as supporting information to the manuscript "Coarse-grained Mori-Zwanzig dynamics in a time-non-local stationary-action framework"</strong></p> <p><br> GLE Optimization: Optimizator of GLE parameters. Uses Matlab</p> <p>MD_GLE: CG GLE and LE simulator. Uses Matlab</p> <p>Water_simulation: Folders for atomistic water system simulation with GROMACS. It requires to be run on Linux with GROMACS <br> and VOTCA packages installed.</p>
Raw datasets and media accompanying the manuscript: Homogenous high enhancement surface-enhanced Raman scattering (SERS) substrates by simple hierarchical tuning of gold nanofoams
<p>Raw datasets and media accompanying the manuscript: Homogenous high enhancement surface-enhanced Raman scattering (SERS) substrates by simple hierarchical tuning of gold nanofoams</p>
Data of manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" submitted to Solar Energy
<p>This is the data corresponding to manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" by Kreuwel et al., 2022, submitted to Solar Energy.</p> <p> </p> <p>The file `basic_stats.tar.gz` contains a broad set of standard statistics of surface meteorology and vertical profiles. The file `sw_flux_dn_xy.tar.gz` contains spatial cross sections of downwelling shortwave radiation.</p>
CiberATAC software and manuscript datasets
<p>CiberATAC manuscript datasets.</p> <p>pbmc.zip contains datasets for running the CiberATAC tutorial</p> <p>mave_data.zip contains datasets for runnning the CiberATAC variational auto-encoder tutorial</p> <p><>_scRNA-seq.RDS files contain Seurat objects of CDX or PDX samples.</p> <p>SW480_scATAC-seq.RDS contains a SingleCellExperiment object of the peak matrix for SW480 CDX samples.</p>
datacleanr manuscript data sets
<p>The data sets in the archive are used to generate figures for a research article introducing the datacleanr R application.</p>
figures_and_data_for_manuscript_contrail_formation_within_cirrus_Verma_and_Burkhardt_07032022
<p>This data set includes figures and data used in the revised manuscript 'Contrail formation within cirrus'.</p>
Mechanical data of rotary shear experiments for the manuscript: "Determination of parameters characteristic of dynamic weakening mechanisms during seismic faulting in cohesive rocks".
<p>Mechanical data of rotary shear experiments and temperature measurements</p> <p>Each experiment is presented in a file with the experiment name (mechanical data of rotary shear experiment) and a file with the experiment name and _Temp (temperature measurement with the optical fiber).</p> <p>Mechanical data are presented in a tab-delimited file with calibrated measurements of:</p> <ul> <li>Time (milliseconds)</li> <li>Normal stress: Normal (MPa) </li> <li>Fault displacement: Slip (mm)</li> <li>Fault velocity: Velocity (mm/s)</li> <li>Shear stress: Shearstress (MPa)</li> <li>Axial shortening: Shortening (mm).</li> </ul>
Simulation files to accompany the manuscript "Two-dimensional basin-scale seismic site effects in the Kitimat Valley, British Columbia, Canada: A practical example of using a fast hybrid FE/BE method"
<p>Simulation files to accompany the two-dimensional basin-scale seismic site effects investigation in the Kitimat valley, located in the northern coastal region of British Columbia, Canada.</p>
Data for manuscript "The Prevalence of Terms Denoting Far-right and Far-left Political Extremism in U.S. and U.K. News Media"
<p>This data set belongs to an academic manuscript examining longitudinally (2000-2019) the prevalence of terms denoting far-right and far-left political extremism in a large corpus of more than 32 million written news and opinion articles from 54 news media outlets popular in the United States and the United Kingdom.</p> <p>The textual content of news and opinion articles from the 54 outlets listed in the main manuscript is available in the outlet's online domains and/or public cache repositories such as Google cache (https://webcache.googleusercontent.com), The Internet Wayback Machine (https://archive.org/web/web.php), and Common Crawl (https://commoncrawl.org). We used derived word frequency counts from these sources. Textual content included in our analysis is circumscribed to articles headlines and main body of text of the articles and does not include other article elements such as figure captions.</p> <p>Targeted textual content was located in HTML raw data using outlet specific xpath expressions. Tokens were lowercased prior to estimating frequency counts. To prevent outlets with sparse text content for a year from distorting aggregate frequency counts, we only include outlet frequency counts from years for which there is at least 1 million words of article content from an outlet. This threshold was chosen to maximize inclusion in our analysis of outlets with sparse amounts of articles text per year. </p> <p>Yearly frequency usage of a target word in an outlet in any given year was estimated by dividing the total number of occurrences of the target word in all articles of a given year by the number of all words in all articles of that year. This method of estimating frequency accounts for variable volume of total article output over time.</p> <p>The list of compressed files in this data set is listed next:</p> <p>-analysisScripts.rar contains the analysis scripts used in the main manuscript </p> <p>-articlesContainingTargetWords.rar contains counts of target words in outlets articles as well as total counts of words in articles</p> <p> </p> <p>Usage Notes</p> <p>In a small percentage of articles, outlet specific XPath expressions failed to properly capture the content of the article due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. As a result, the total and target word counts metrics for a small subset of articles are not precise. In a random sample of articles and outlets, manual estimation of target words counts overlapped with the automatically derived counts for over 90% of the articles.</p> <p>Most of the incorrect frequency counts were minor deviations from the actual counts such as for instance counting the word "Facebook" in an article footnote encouraging article readers to follow the journalist’s Facebook profile and that the XPath expression mistakenly included as the content of the article main text. Some additional outlet-specific inaccuracies that we could identify occurred in "The Hill" and "Newsmax" news outlets where XPath expressions had some shortfalls at precisely capturing articles’ content. For "The Hill", in years 2007-2009, XPath expressions failed to capture the complete text of the article in about 40% of the articles. This does not necessarily result in incorrect frequency counts for that outlet but in a sample of articles’ words that is about 40% smaller than the total population of articles words for those three years. In the case of "NewsMax", the issue was that for some articles, XPath expressions captured the entire text of the article twice. Notice that this does not result in incorrect frequency counts. If a word appears x times in an article with a total of y words, the same frequency count will still be derived when our scripts count the word 2x times in the version of the article with a total of 2y words.</p> <p>To conclude, in a data analysis of 32 million articles, we cannot manually check the correctness of frequency counts for every single article and hundred percent accuracy at capturing articles’ content is elusive due to the small number of difficult to detect boundary cases such as incorrect HTML markup syntax in online domains. Overall however, we are confident that our frequency metrics are representative of word prevalence in print news media content (see Figure 1 in the main manuscript for illustration of the accuracy of the frequency counts).</p>
Supporting data for King et al., 2022, manuscript.
<p>Supporting data and scripts for the "King et al., 2022, ENSO teleconnections on the North Atlantic atmosphere in non-NAO and NAO variabilities" manuscript. Materials in this archive can be used for the purpose of reproducing the analyses and figures in the manuscript, and may not be suitable for general research use.</p>
Data supporting the manuscript: "Influence of modern environmental gradients on foraminiferal faunas in the inner Kongsfjorden (Svalbard)" published in Marine Micropaleontology
<p>Here we provide data about living benthic foraminifera related to the manuscript "Influence of modern environmental gradients on foraminiferal faunas in the inner Kongsfjorden (Svalbard)" published in Marine Micropaleontology.</p> <p>This excel file contains four sheets:</p> <ol> <li><strong>readme</strong></li> <li><strong>KING18_station_infos:</strong> includes sampling coordinates and related information.</li> <li><strong>KING18_abundances10cm3_0-1cm:</strong> includes living benthic foraminiferal absolute abundances (individuals 10cm<sup>-3</sup>) in the 0-0.5 and 0.5-1 cm sediment layers and four different size fractions (i.e., 63-100, 100-125, 125-150 and >150 µm).</li> <li><strong>KING18_abundances10cm3_0-5cm: </strong>includes living benthic foraminiferal absolute abundances (individuals 10cm<sup>-3</sup>) in seven sediment layers (i.e., 0-0.5, 0.5-1, 1-1.5, 1.5-2, 2-3, 3-4 and 4-5 cm) and the >150 µm size fraction.</li> </ol>
The Rasendramaṅgala: Manuscript Transcriptions
<p>For details of this project, see</p> <ul> <li><a href="https://wujastyk.github.io/Rasendramangala/">https://wujastyk.github.io/Rasendramangala/</a></li> </ul> <p>change: Added the ṭippaṇa files and word breaks</p>
Dataset for manuscript entitled: Switchgrass cropping systems affect soil carbon and nitrogen and microbial diversity and activity on marginal lands
<p class="MsoListParagraph">Switchgrass (<em>Panicum virgatum</em> L.),<span> </span>as a dedicated bioenergy crop, can provide cellulosic feedstock for biofuel production while improving or maintaining soil quality. However, comprehensive evaluations of how switchgrass cultivation and nitrogen (N) management impact soil and plant parameters remain incomplete. We conducted<span> </span>field trials in three years (2016–2018) at six locations in the North Central Great Lakes Region to evaluate the effects of cropping systems (switchgrass, restored prairie, undisturbed control) and N rates (0, 56 kg N ha<sup>-1</sup> yr<sup>-1</sup>) on biomass yield and soil physicochemical, microbial, and enzymatic parameters. Switchgrass cropping system yielded an aboveground biomass 2.9–3.3 times higher than the other two systems (Jayawardena et al., In submission) but our study found that this biomass accumulation didn't reduce soil dissolved organic C (DOC), total dissolved N (TDN), or bacterial diversity. The annual aboveground biomass removal for bioenergy feedstock, however, reduced soil microbial biomass C (MBC) and N (MBN) and bacterial richness in the 2<sup>nd</sup> and 3<sup>rd</sup> years; despite this, continuous monocropping of switchgrass improved soil TDN, inorganic N, bacterial diversity, and shoot biomass in the 2<sup>nd</sup> and/or 3<sup>rd</sup> years when compared to the 1<sup>st</sup> year. N fertilization increased aboveground biomass yield by 1.2 times and significantly increased soil TDN, MBN, and the shoot biomass of switchgrass when compared to the unfertilized control. Locations with higher C and N contents and lower C:N ratio had higher aboveground biomass, MBC, MBN, and the activity of BG, CBH, and UREA enzymes; by contrast, locations with higher pH had higher soil TDN and activity of NAG and LAP enzymes. Our research demonstrates that switchgrass cultivation could improve or maintain soil N content and N fertilization can increase plant biomass yield. The comprehensive data also can inform future biogeochemical models to successfully implement switchgrass for bioenergy production.</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.