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.
4,694
datasets available to search
ShareScore release 0.7.1
Dataset results
4,694 results for “data analysis”
Data for manuscript: "Longitudinal Analysis of Sentiment and Emotion in News Media Headlines Using Automated Labelling with Transformer Language Models"
<p>This data set contains automated sentiment and emotionality annotations of 23 million headlines from 47 popular news media outlets popular in the United States. </p> <p>The set of 47 news media outlets analysed (listed in Figure 1 of the main manuscript) was derived from the AllSides organization <a href="https://www.allsides.com/blog/updated-allsides-media-bias-chart-version-11">2019 Media Bias Chart v1.1</a>. The human ratings of outlets’ ideological leanings were also taken from this chart and are listed in Figure 2 of the main manuscript. </p> <p>News articles headlines from the set of outlets analyzed in the manuscript are available in the outlets’ online domains and/or public cache repositories such as The Internet Wayback Machine, Google cache and Common Crawl. Articles headlines were located in articles’ HTML raw data using outlet-specific XPath expressions. </p> <p>The temporal coverage of headlines across news outlets is not uniform. For some media organizations, news articles availability in online domains or Internet cache repositories becomes sparse for earlier years. Furthermore, some news outlets popular in 2019, such as <em>The Huffington Post</em> or <em>Breitbart</em>, did not exist in the early 2000’s. Hence, our data set is sparser in headlines sample size and representativeness for earlier years in the 2000-2019 timeline. Nevertheless, 18 outlets in our data set have chronologically continuous partial or full headline data availability fulfilling our inclusive criteria (see manuscript Methods) since the year 2000. Figure S 1 in the SI reports the number of headlines per outlet and per year in our analysis.</p> <p>In a small percentage of articles, outlet specific XPath expressions might fail to properly capture the content of the headline due to the heterogeneity of HTML elements and CSS styling combinations with which articles text content is arranged in outlets online domains. After manual testing, we determined that the percentage of headlines following in this category is very small. Additionally, our method might miss detecting some articles in the online domains of news outlets. To conclude, in a data analysis of over 23 million headlines, we cannot manually check the correctness of every single data instance and hundred percent accuracy at capturing headlines’ 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 headlines set is representative of headlines in print news media content for the studied time period and outlets analyzed.</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 as well as aggregated data of sentiment and emotionality automated annotations of the headlines and human annotations of a subset of headlines sentiment and emotionality used as ground truth. </p> <p>-models.rar contains the Transformer sentiment and emotion annotation models used in the analysis. Namely: </p> <p>Siebert/sentiment-roberta-large-english from https://huggingface.co/siebert/sentiment-roberta-large-english. This model is a fine-tuned checkpoint of <a href="https://huggingface.co/roberta-large">RoBERTa-large</a> (<a href="https://arxiv.org/pdf/1907.11692.pdf">Liu et al. 2019</a>). It enables reliable binary sentiment analysis for various types of English-language text. For each instance, it predicts either positive (1) or negative (0) sentiment. The model was fine-tuned and evaluated on 15 data sets from diverse text sources to enhance generalization across different types of texts (reviews, tweets, etc.). See more information from the original authors at https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>DistilbertSST2.rar is the default sentiment classification model of the HuggingFace Transformer library https://huggingface.co/ This model is only used to replicate the results of the sentiment analysis with sentiment-roberta-large-english </p> <p>DistilRoberta j-hartmann/emotion-english-distilroberta-base from https://huggingface.co/j-hartmann/emotion-english-distilroberta-base. The model is a fine-tuned checkpoint of <a href="https://huggingface.co/distilroberta-base">DistilRoBERTa-base</a>. The model allows annotation of English text with Ekman's 6 basic emotions, plus a neutral class. The model was trained on 6 diverse datasets. Please refer to the original author at https://huggingface.co/j-hartmann/emotion-english-distilroberta-base for an overview of the data sets used for fine tuning. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromSentimentRobertaLargeModel.rar URLs of headlines analyzed and the sentiment annotations of the siebert/sentiment-roberta-large-english Transformer model. https://huggingface.co/siebert/sentiment-roberta-large-english</p> <p>-headlinesDataWithSentimentLabelsAnnotationsFromDistilbertSST2.rar URLs of headlines analyzed and the sentiment annotations of the default HuggingFace sentiment analysis model fine-tuned on the SST-2 dataset. https://huggingface.co/</p> <p>-headlinesDataWithEmotionLabelsAnnotationsFromDistilRoberta.rar URLs of headlines analyzed and the emotion categories annotations of the j-hartmann/emotion-english-distilroberta-base Transformer model. https://huggingface.co/j-hartmann/emotion-english-distilroberta-base</p>
Dataset for: Guidelines for standardising the application of discriminant analysis of principal components to genotype data
<p><span>Data and scripts required to replicate the analyses in Thia (2022) "<span class="fontstyle0">Guidelines for standardising the application of discriminant analysis of principal components to genotype data" in <em>Molecular Ecology</em>.</span></span></p> <p><span>This study aimed to address methodological misunderstandings and misuse of the DAPC method in population genetics. The analyses are used to illustrate that for genotype data comprising <em>k</em> effective populations, there are only <em>k</em><span>−</span>1 PC axes that describe populations structure, and that are biologically informative. These PC axes are the only suitable axes for modelling the among-population differences with a DA. Use of many more than <em>k</em><span>−1 PC axes leads to decreasing biological relevancy of the final DA solution, with implications for misinterpretations of population structure.</span></span></p>
Data for: Two is better than one: Coupling DNA metabarcoding and stable isotope analysis improves dietary characterizations for a riparian-obligate, migratory songbird
<p>While an increasing number of studies are adopting molecular and chemical methods for dietary characterization, these studies often employ only one of these laboratory-based techniques; an approach which may yield an incomplete, or even biased, understanding of diet due to each method's inherent limitations. To explore the utility of coupling molecular and chemical techniques for dietary characterizations, we applied DNA metabarcoding alongside stable isotope analysis to characterize the dietary niche of breeding Louisiana waterthrush (<em>Parkesia motacilla</em>), a migratory songbird hypothesized to preferentially provision their offspring with pollution-intolerant, aquatic arthropod prey. While DNA metabarcoding was unable to determine if waterthrush provision aquatic and terrestrial prey in different abundances, we found that specific aquatic taxa were more likely to be detected in successive seasons than their terrestrial counterparts, thus supporting the aquatic specialization hypothesis. Our isotopic analysis added greater context to this hypothesis by concluding that breeding waterthrush provisioned Ephemeroptera and Plecoptera, two pollution-intolerant, aquatic orders, in higher quantities than other prey groups, and expanded their functional trophic niche when such prey were not abundantly provisioned. Finally, we found that the dietary characterizations from each approach were often uncorrelated, indicating that the results gleaned from a diet study can be particularly sensitive to the applied methodologies. Our findings contribute to a growing body of work indicating the importance of high-quality, aquatic habitats for both consumers and their pollution-intolerant prey, while also demonstrating how the application of multiple, laboratory-based techniques can provide insights not offered by either technique alone.</p>
Analysis of shared research data in Spanish scientific papers about COVID-19: a first approach
<p><strong>Introduction:</strong> During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. <strong>Objective: </strong>To study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. <strong>Methodology:</strong> Qualitative and descriptive study applying nine attributes: (1) availability, (2) accessibility, (3) format, (4) licensing, (5) linkage, (6) funding, (7) editorial policy, (8) content and (9) statistics. <strong>Results:</strong> We analyzed 1340 papers, 1173 (87.5%) did not have research data. 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data and 5.2% share their data as supplementary material. <strong>Conclusions:</strong> There is a small percentage that shares their research data, however it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.</p>
Analysis code and data for "End-to-end study of the host galaxy and genealogy of GW170817 with BPASS"
<p>This folder contains the code and data required to reproduce all figures and values presented in the study titled "End-to-end study of the host galaxy and genealogy of GW170817 with BPASS". </p> <p>The running of the data analysis jupyter notebooks will require the installation of the python package "hoki" v1.7 and the download of the BPASS models that are already publically available. The README.md file contains all the information regarding the dependencies of this directory. </p> <p>Should you need assistance please email hfstevance@gmail.com</p>
Doctoral School 2022: Data for 3D analysis lesson
<p>Data for 3D analysis lesson, see the notebook here: https://github.com/alert-geomaterials/2022-doctoral-school</p>
Supporting data for article comparison and Uncertainty Analysis of Species Distribution Models
<p>Downloaded from Web of Science for the supporting data of article comparison and Uncertainty Analysis of Species Distribution Models.</p>
Nanopore sequencing data analysis using Microsoft Azure cloud computing service
<p>Genetic information provides insights into the exome, genome, epigenetics and structural organisation of the organism. Given the enormous amount of genetic information, scientists are able to perform mammoth tasks to improve the standard of health care such as determining genetic influences on outcome of allogeneic transplantation. Cloud-based computing has increasingly become a key choice for many scientists, engineers and institutions as it offers on-demand network access and users can conveniently rent rather than buy all required computing resources. With the positive advancements of cloud computing and nanopore sequencing data output, we were motivated to develop an automated and scalable analysis pipeline utilizing cloud infrastructure in Microsoft Azure to accelerate HLA genotyping service and improve the efficiency of the workflow at lower cost. In this study, we describe (i) the selection process for suitable virtual machine sizes for computing resources to balance between the best performance versus cost-effectiveness; (ii) the building of Docker containers to include all tools in the cloud computational environment; (iii) the comparison of HLA genotype concordance between the in-house manual method and the automated cloud-based pipeline to assess data accuracy. In conclusion, the Microsoft Azure cloud-based data analysis pipeline was shown to meet all the key imperatives for performance, cost, usability, simplicity and accuracy. Importantly, the pipeline allows for the ongoing maintenance and testing of version changes before implementation. This pipeline is suitable for data analysis from MinION sequencing platforms and could be adopted for other data analysis application processes.</p>
An Analysis of the Current Bibliographical Data Landscape in the Humanities. A Case for the Joint Bibliodata Agendas of Public Stakeholders - video presentation
<p>A video presenting the DARIAH's Bibliographical Data Working Group entitled <em>An Analysis of the Current Bibliographical Data Landscape in the Humanities. A Case for the Joint Bibliodata Agendas of Public Stakeholders. </em>The report is freely available on Zenodo: <a href="https://zenodo.org/record/6559857#.Y0XDo3ZBy5f">https://zenodo.org/record/6559857#.Y0XDo3ZBy5f</a>. </p> <p>This presentation aims to present the original work - co-authored by 18 WG's members - in a condensed manner.</p>
Data and GenX analysis for "Liquid Helium as a reference may provide clarity for some neutron reflectometry experiments"
<p>Data and GenX analysis for "Liquid Helium as a reference may provide clarity for some neutron reflectometry experiments"</p>
Supporting data and analysis for, "An atlas of healthy and injured cell states and niches in the human kidney"
<p>Online repository for contents associated with kidney cell state atlas: </p> <p>This combines the image datasets used for the manuscript describing an approach for the integrated tissue cytometry analysis of mesoscale confocal imaging datasets.</p> <p>Linked deposits:</p> <table> <tbody> <tr> <td>Zenodo Extended Data figures</td> <td>10.5281/zenodo.7120908</td> </tr> <tr> <td>3D Cytometry and neighborhood analysis</td> <td>10.5281/zenodo.7120941</td> </tr> <tr> <td>Github repository, "Cell-State-Atlas_2022"</td> <td>https://github.com/KPMP/Cell-State-Atlas-2022</td> </tr> </tbody> </table> <p> </p> <p> </p>
Analysis tools and data for study "Better insurance could effectively mitigate the increase in economic growth losses from US hurricanes under global warming"
<p>This scripts are used for post-analysis and for creating the main figures for our study "Better insurance could effectively mitigate the increase in economic growth losses from US hurricanes under global warming". Our raw results are calculated by InGroClIm (DOI:10.5281/zenodo.5017904).</p>
Analyses, data and figures related to: "Connecting ships: Using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and visualise wood use patterns"
<p>Analyses, data and figures related to: "Connecting ships: using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and to visualise patterns of wood use" by Ronald M. Visser (Saxion University of Applied Sciences, Deventer, the Netherlands) and Yardeni Vorst (Vorst wood research, Zaandam, the Netherlands) submitted to the International Journal of Wood Culture</p>
Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools - C. elegans unc-16 and N2 motion behavior dataset
<p>Dataset contains <em>Caenorhabditis</em> <em>elegans </em>motor behaviors originally used in this publication <a href="https://www.nature.com/articles/nmeth.2560">10.1038/nmeth.2560</a> and downloaded from the available <a href="http://wormbehavior.mrc-lmb.cam.ac.uk/">DB</a> which points to Zenodo. The dataset consists in two worm strains, 20 individuals from a mutant unc-16 strain with reduced mobility and 40 individuals from a control N2 strain. The behavioral measures derived from each individual worm trajectory were available in a HDF5-formatted file (Hierarchical Data Format Version 5) that has been included in this dataset.</p> <p>The data set consist in:</p> <p>- a "mappings" folder containing all the mappings used by the pergola in the pipeline to convert data.</p> <p>- a "N2" folder containing the 40 HDF5 files with the measures derived from the N2 worms.</p> <p>- a "N2" folder containing the 20 HDF5 files with the measures derived from the unc-16 worms.</p>
Somatosensory data for group analyses in the Frontiers Reseach Topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.
<p><strong>If you use the data or the analysis pipeline, please refer to:</strong></p> <p>Andersen, L.M., 2018. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00006">https://doi.org/10.3389/fnins.2018.00006</a></p> <p><strong>and/or</strong></p> <p>Andersen, L.M., 2018. Group Analysis in FieldTrip of Time-Frequency Responses: A Pipeline for Reproducibility at Every Step of Processing, Going From Individual Sensor Space Representations to an Across-Group Source Space Representation. Front. Neurosci. 12. <a href="https://doi.org/10.3389/fnins.2018.00261">https://doi.org/10.3389/fnins.2018.00261</a></p> <p><strong>IMPORTANT</strong><br> Version 2 only contains subjects 1, 18, 20 and a new version of the FreeSurfer folder. This is due to a (very) wrong co-registration for subject 1 and due to 18 and 20 having had their anatomy files mixed up. This has now been fixed. For all other subjects, please see version 1. Also, get the updated scripts from github instead at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p> <p><br> </p> <p>Dataset with tactile expectations to be analysed with pipelines for either <a href="https://mne.tools/stable/index.html">MNE-Python</a> or <a href="http://www.fieldtriptoolbox.org/">FieldTrip</a>, aiming to follow the MEG-BIDS structure</p> <p><br> <strong>Unzipping the data</strong></p> <p>Data is compressed into twenty-two different zip-files, one for each of the twenty subjects, one for the FreeSurfer data, one for the scripts files . The easiest way to uncompress and prepare the analysis directories is to create a directory in your home folder called "analyses", which has a sub-directory called "omission_frontiers_BIDS-FieldTrip", which has a sub-directory called "data".<br> Thus, as an example, in my case, I should have the path: /home/lau/analyses/omission_frontiers_BIDS-FieldTrip/data</p> <p><strong>Path:</strong><br> on a Linux system the path would be /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a macOS system the path would be /Users/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data<br> on a Windows system the path would be C:\Users\your_name\analyses\omission_frontiers_BIDS-FieldTrip\data</p> <p><strong>Steps for unzipping:</strong></p> <p>1. Set up the folder above according to your operating system, following the examples above and substitute "your_name" for your user name.<br> 2. Unzip each of the subject folders into the data folder (sub-01 - sub-20) (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 3. Also unzip the FreeSurfer folder into the data folder (/home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/data)<br> 4. Finally, unzip the scripts folder into /home/your_name/analyses/omission_frontiers_BIDS-FieldTrip/</p> <p>Now you are ready to run the analyses.</p> <p><br> <strong>The MEG data</strong></p> <p>Raw fif files are contained in the data folder, ordered by subject (n=20)<br> There is one recording for each subject, MaxFiltered, called oddball_absence-tsss-mc_meg.fif. These are split into three files with -1 and -2 being the remainder of the recording</p> <p><strong>Processed MRI data </strong></p> <p>For the MRI, only the segmented data are provided. This is to sufficient to make the volume conduction model and the source model, while protecting the subjects' identity</p> <p>For Fieldtrip, there is an mri_segmented.mat for each subject, which is found in the meg (sic!) folder for each subject. This has been co-registered to the MEG data<br> For MNE-Python, the FreeSurfer directory should also be used, which contains a folder for each subject that contains surfaces (surf) and boundary element methods models (bem) that are used for source reconstruction in MNE-python. There is also a trans-file for each subject (oddball_absence_dense-trans.fif) in the meg folder specifying the co-registration between MEG and MRI coordinate systems for the MNE-Python analysis. Finally, the FreeSurfer folder also contains the labels for the cortical surface. This is not used in any of the analyses, but are supplied for interested users.</p> <p><br> <strong>Metadata</strong></p> <p>Each subject has a number of tsv-files:<br> *channel.tsv contain information about the channels in that recording<br> *events.tsv contain information about the events in that recording<br> removed_trial_indices.tsv contains information about which events were removed manually (NB! this is only used for the FieldTrip analysis)<br> ica_components.tsv contains information which independent component were removed manually (NB! this is only used for the FieldTrip analysis)<br> *scans_tsv contain information about the scans conducted</p> <p><br> <strong>Scripts </strong></p> <p>Please see Github for the updated scripts at: <a href="https://github.com/ualsbombe/omission_frontiers.git">https://github.com/ualsbombe/omission_frontiers.git</a></p>
Raw data for the article "Combining STPA and BDD for Safety Analysis and Verification in Agile Development: A Controlled Experiment"
<p>These files include the raw data of pre-questionnaire, operation report, post-questionnaire and hypothesis testing in the experiment. </p>
Extended-field synchrotron microtomography for non-destructive analysis of incremental lines in human teeth cementum: example data
<h3>Abstract</h3> <p>Tooth cementum annulation (TCA) is used for determining age-at-death and stress periods based on yearly<br>deposited layers in the root cementum of human teeth. Traditionally, TCA analysis employs optical microscopy,<br>which requires cutting sections of the root and provides only sparse sampling in the third dimension. Ancient<br>teeth are often unique specimens that should not be damaged. In this imaging study, we show that extended-field<br>synchrotron radiation-based microtomography can be used to provide true micrometer resolution and full<br>coverage of the tooth for non-destructively surveying ancient teeth for incremental layers. To rapidly review the<br>root cementum layer of four teeth from an early 19th century cemetery with historical records of life events, we<br>developed a method for automatically enhancing incremental lines on virtual slices and for detecting regions with<br>strong incremental line appearances. Surveying large regions of the root cementum avoids missing high-contrast<br>incremental lines and hence improves TCA analysis as an alternative to irreversible slicing of the unique teeth.</p> <h3>Data</h3> <p>The repository contains the microtomography data from four teeth to enable reproducing Figures 6a,c,e,f in the associated publication, Tanner et al., "Extended-field synchrotron microtomography for non-destructive analysis of incremental lines in archeological human teeth cementum". Proceedings of SPIE 11840 (2021) 1184019. DOI: <a href="https://doi.org/10.1117/12.2595180" target="_blank" rel="noopener">10.1117/12.2595180</a>. Each dataset contains 57 tif slices around the selected slice selZ, i.e. selZ-28:selZ+28, as stated below.</p> <table> <tbody> <tr> <td><strong>Fig.</strong></td> <td><strong>Dataset</strong></td> <td><strong>Tooth</strong></td> <td><strong>selZ</strong></td> <td><strong>Slices</strong></td> <td><strong>sampleIdx</strong></td> <td><strong>hs</strong></td> <td><strong>Directory</strong></td> </tr> <tr> <td>6a</td> <td>tooth4_hs04_reco_selZ715.tar.gz</td> <td>T1</td> <td>715</td> <td>687:743</td> <td>2</td> <td>4</td> <td>dataDir1</td> </tr> <tr> <td>6c</td> <td>zahn_OKreC_probe1_hs02_reco_selZ1730.tar.gz</td> <td>T2</td> <td>1730</td> <td>1702:1758</td> <td>5</td> <td>2</td> <td>dataDir2</td> </tr> <tr> <td>6e</td> <td>zahn_probe1_hs04_reco_selZ275.tar.gz</td> <td>T3</td> <td>275</td> <td>247:303</td> <td>4</td> <td>4</td> <td>DataDir2</td> </tr> <tr> <td>6f</td> <td>zahn35_probe2_hs02_reco_selZ275.tar.gz</td> <td>T4</td> <td>275</td> <td>247:303</td> <td>3</td> <td>2</td> <td>dataDir1</td> </tr> </tbody> </table> <h3>Processing</h3> <p>Incremental teeth lines can be enhanced via the MATLAB programs available in the github repository <a href="https://github.com/unibas-bmc/enhanceIncrementalTeethLines">unibas-bmc/enhanceIncrementalTeethLines</a>.</p> <p>Please extract the data from <em>name</em>.tar.gz via unix command "tar -xzvf <em>name</em>.tar.gz" . Place the .tif files called reco_????.tif in directory "dataDir1" or "dataDir2" (see last column in table) using subdirectories based on the tooth name and heightstep, e.g. "tooth4_hs04/reco/" for dataset "tooth4_hs04_reco_selZ715.tar.gz". Then set the "sampleIdx" and "hs" parameter in "dataParameterDefinitionTeeth.m" as stated in the table above. Finally run "extractCementumPatchesEnhanceIL.m" to process the data.</p> <h3>Note</h3> <p>We are grateful for beamtime access at the Synchrotron SOLEIL, ANATOMIX beamline (experiment no. 20200712). ANATOMIX is an Equipment of Excellence (EQUIPEX) funded by the Investments for the Future program of the French National Research Agency (ANR), project NanoimagesX, grant no. ANR-11-EQPX-0031. </p> <p>We also thank the Citizen Science Project BBS “Bürgerforschungsprojekt Basel-Spitalfriedhof” for their time-consuming voluntary research regarding the historical sources of the samples.</p>
Data from 'How Did Trilobites Conquer the Oceans? A Probabilistic Palaeobiogeographical Analysis of Cambrian Trilobites'
Open the record for dataset details and reuse information.
Data for "Structure, short-range order, and phase stability of the Al$_x$CrFeCoNi high-entropy alloy: Insights from a perturbative, DFT-based analysis"
<p>Data associated with "Structure, short-range order, and phase stability of the AlxCrFeCoNi high-entropy alloy: Insights from a perturbative, DFT-based analysis", published in npj Comput. Mater. <strong>10</strong>, 271 (2024).</p>
Data and code for "Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"
<p>Data and code for <br>"Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"</p> <p>All R code used for the analysis is provided in the folder <em>code</em>. <br>Data and intermediate results are provided or stored in the folders <em>data </em>and <em>tmp_data</em>.<br>All results including figures will be stored in the folder <em>results</em>. </p> <p>R version: 4.3.1</p> <p>To carry out the entire analysis the code should be run in the provided order:</p> <p>1) Code to run non-metric multidimensional scaling (NMDS) for habitat groups and <br>produce Fig. 1b (habitat map and legend for Fig 1a: data/eunis_gl_habitat_ger_990m.tif,eunis_gl_habitat_ger_990m_legend.clr)<br> <br>2) Code to generate grassland vitality maps and time series from 1985 to 2021 (Fig. 3). <br>Grassland vitality maps on 30m for all grasslands in Germany provided in data/glv_1985-2021.zip.</p> <p>3) Code to model relation of grassland vitality to five drought indices (VPD, temperature, CWB, soil moisture, precipitation),<br>output are Fig. 4, Fig. S1, Tab. 1.</p> <p>4) Code for trend analysis of drought sensitivity based on 5-, 10-, and 15-year moving windows, output are Fig. 5, Fig. S2. </p> <p>5) Code to model drought sensitivity of grassland habitat groups and habitat types, output are Fig. 6 and table with sensitivity per habitat type. </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.