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.
316
datasets available to search
ShareScore release 0.9.0
Dataset results
316 results for “lessons”
Dataset used for the paper: "How not to describe a species: lessons from a tangle of anacondas"
<p>Dataset used for the paper: "How not to describe a species: lessons from a tangle of anacondas"</p> <p>by Wolfgang Wüster, Hinrich Kaiser, Marinus S. Hoogmoed, Luis M. P. Ceríaco, Christophe Dufresnes, Frank Glaw, Axel Hille, Lutz Dirksen, Jörn Köhler, Thore Koppetsch, Konstantin D. Milto, Glenn M. Shea, David Tarkhnishvili, Scott A. Thomson, Miguel Vences, Wolfgang Böhme</p> <p><a href="https://doi.org/10.1093/zoolinnean/zlae099">https://doi.org/10.1093/zoolinnean/zlae099</a></p> <p> </p> <p> </p> <p>This dataset contains:</p> <p>- Spreadsheet with morphometric data used for analysis (measuremments/counts taken from previous publications as specified in the paper).</p> <p>- Treefiles and alignments used for timetree analyses</p> <p>- Photos of specimen ZISP 1441 (considered to be the lectotype of Eunectes murinus)</p> <p> </p> <p> </p> <p> </p>
Dataset for Medical Image Processing in Python Carpentries lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, originally developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="https://zenodo.org/records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a> <div> </div> </li> <li>Additional data: to be added</li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Changing birth places to care for healthcare workers, lessons from the pandemic: a scoping review.
<p><strong>Abstract</strong></p> <p><strong>Background:</strong> Together with women giving birth, healthcare professionals involved in perinatal care faced significant challenges due to Covid-19 restrictions. The birth environment has been affected, influencing the quality and nature of interactions between care providers, women, and their families as well as the quality of care provided to women.</p> <p><strong>Aim:</strong> To identify, categorize and reflect on the most relevant aspects of the experience of healthcare workers, especially obstetricians and gynecologists, involved in the peripartum period, after the first wave of the Covid-19 pandemic up till now.</p> <p><strong>Methods:</strong> A scoping review was conducted. After conducting a literature search using a variety of online data- bases, 22 articles were included, and the main/recurrent themes were identified.</p> <p><strong>Results: </strong>The following main themes were described: fear of the virus, changes in the way care is provided, increase in the workload, mental health, psychological distress, support strategies, university education, and perspectives of health managers.</p> <p><strong>Discussion:</strong> Standard maternity departments appeared not appropriate to face the pandemic. Psychological distress on perinatal healthcare workers’, the majority of whom were women, led to the impairment of their mental health, affecting the quality of care provided to women. A key role was played by healthcare managers support in shaping how doctors can cope and recover, and in reducing the mental health impact.</p> <p><strong>Conclusion:</strong> It is very important to address some of the most relevant issues to acknowledge the role of healthcare workers and improve the health systems organization to face future pandemics.</p>
Dataset of paper Privacy Orientation during Online Teaching-Learning Activities: Practices Adopted and Lessons Learned
<p>Dataset of the paper accepted for publication in the Journal on Interactive Systems (JIS).</p> <p>DA SILVA, M.; VITERBO, J.; SALGADO, L. C. C.; MOURÃO, E. Privacy Orientation during Online Teaching-Learning Activities:<br> Practices Adopted and Lessons Learned. Journal on Interactive Systems, Porto Alegre, RS, 2023.</p>
Supporting data and tool, for the paper "A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe"
<p>This 'Zenodo' contains supporting data and tools, for the paper 'A standardized methodology for the validation of air quality forecast applications (F-MQO): Lessons learnt from its application across Europe'.</p> <p>The repository includes the source code (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/DELTA_7.2.zip">DELTA_7.2.zip</a>) and the dataset (<a href="https://zenodo.org/api/files/fdcaf2a5-5289-44f5-96db-6dda29c5cb6c/GMD_Vitali_et_all_data_20230516.zip">GMD_Vitali_et_all_data_20230516.zip</a>).</p>
One Health EJP 4th Dissemination Workshop: Communication, education and training, and science to policy translation. Lessons learnt and legacy of the One Health EJP
<p>The Dissemination Workshops inform on One Health EJP solutions created in response to stakeholder needs, fostering the link between stakeholders and the consortium. Previous Dissemination Workshops targeted policy and decision makers at the national, European, and international level (reports available <a href="https://onehealthejp.eu/outcomes/science-to-policy-translation/reports">here</a>). This workshop instead is dedicated to all those who would like to set up One Health initiatives, encouraging them to learn from the experience of the One Health EJP.</p> <p>The 4th virtual One Health EJP Dissemination Workshop focuses on the interaction with stakeholders to translate science into policy, the impact of the education and training activities, and strategies used for effective dissemination of One Health solutions. Online presentations are given by the members of the One Health EJP <a href="https://onehealthejp.eu/outcomes/science-to-policy-translation/reports">Science to Policy Translation</a> (WP5), <a href="https://onehealthejp.eu/community/education-and-training">Education and Training</a> (WP6), and Communications Teams.</p> <p>Through this workshop, One Health EJP consortium members provide examples of successful strategies and lessons learnt, with the objective of inspiring all those working in this field or setting up new One Health initiatives and increasing the impact of One Health activities in Europe. This is an important legacy of the One Health EJP.</p> <p>Sessions include:</p> <ol> <li> Welcome and introduction.</li> <li> Education and training: Training a new generation of One Health scientists.</li> <li> Communication and dissemination: How to reach One Health audiences.</li> <li> Interactions with stakeholders: targeted dissemination, support, and advocacy. The example of the One Health EJP.</li> <li> Final round table discussion.</li> </ol> <p>Full details on the briefing agenda.</p>
Benchmark Data Repositories: Lessons and Recommendations
<p>Our dataset "repository_survey" summarizes a comprehensive survey of over 150 data repositories, characterizing their metadata documentation and standardization, data curation and validation, and tracking of dataset use in the literature. In addition, "survey_model_evaluation" includes our findings on model evaluation for five benchmark repositories. Column descriptions and further details can be found in "README.pdf." The data are associated with our paper "Benchmark Data Repositories: Lessons and Recommendations." </p>
Data from: Integrating deep learning derived morphological traits and molecular data for total-evidence phylogenetics: lessons from digitized collections
Open the record for dataset details and reuse information.
Microbiota data analysis lesson datasets
<p>Datasets to be used in the Carpentry-style lesson on [microbiota data analysis](https://scienceparkstudygroup.github.io/microbiome-lesson/).</p> <p>Datasets from the paper: Zancarini, A., Echenique-Subiabre, I., Debroas, D. <em>et al.</em> Deciphering biodiversity and interactions between bacteria and microeukaryotes within epilithic biofilms from the Loue River, France. <em>Sci Rep</em> <strong>7, </strong>4344 (2017). https://doi.org/10.1038/s41598-017-04016-w. <a href="https://rdcu.be/b3CZh">https://rdcu.be/b3CZh</a></p>
U-Th dating of lake sediments: Lessons from the 700 ka sediment record of Lake Junín, Peru — Datasets
<p><strong>Title</strong>: U-Th dating of lake sediments: Lessons from the 700 ka sediment record of Lake Junín, Peru — Datasets</p> <p><strong>Version</strong>: 2.0</p> <p><strong>Date of Release</strong>: August 18, 2020</p> <p><strong>Last Update</strong>: September 16, 2020</p> <p><strong>Identifier</strong>: 10.5281/zenodo.4031644</p> <p><strong>Permalink</strong>: <a href="https://doi.org/10.5281/zenodo.4031644">https://doi.org/10.5281/zenodo.4031644</a></p> <p><strong>Associated publication</strong>: Chen, C.Y., McGee, D., Woods, A., Pérez, L., Hatfield, R.G., Edwards, R.L., Cheng, H., Valero-Garcés, B.L., Lehmann, S.B., Stoner, J.S., Schwalb, A., Tal, I., Seltzer, G.O., Tapia, P.M., Abbott, M.B., and Rodbell, R.T. (2020) U-Th dating of lake sediments: Lessons from the 700 ka sediment record of Lake Junín, Peru. Quaternary Science Reviews. 244: 106422 doi: <a href="https://doi.org/10.1016/j.quascirev.2020.106422">10.1016/j.quascirev.2020.106422</a></p> <p><strong>Link to publication</strong>: <a href="http://doi.org/10.1016/j.quascirev.2020.106422">https://doi.org/10.1016/j.quascirev.2020.106422</a></p> <p><strong>Suggested citation</strong>: Please reference the associated publication above when using any datasets or materials in this repository.</p> <p><strong>Contact information</strong>: Christine Y. Chen, cychen@caltech.edu OR cychen.earth@gmail.com</p> <p><strong>Dates of data collection and generation</strong>: February 2016 to July 2019</p> <p>---------------</p> <p><strong>VERSION NOTES </strong>(v2.0)</p> <p>This online data repository was updated in order to correct errors in the U-Th data tables (Tables A1, A2, and A3) and subsequently update the age-depth model. The corrections impacted the files named '<em>CYChen et al. (2020) QSR - U-Th Data Tables for PLJ-1 and 1996 cores - v2.xlsx</em>' and '<em>CYChen et al. (2020) QSR - Input Your Own Depths for Age-Depth Model - v2.xlsx</em>'. No changes have been made to the original versions of other datasets. However, all components of this data repository have been given the "v2" identifier to be consistent with the versioning scheme applied to this data repository as a whole by Zenodo.</p> <p>---------------</p> <p><strong>DESCRIPTION OF DATA</strong></p> <p>This data repository contains the following datasets and supplementary materials. Specific documentation and, where applicable, instructions of use are included in the readme section of each file. We refer the user to the original manuscript (see above) and the text of the Supplemental Materials published alongside this manuscript for additional general information regarding the collection and generation of these data.</p> <p>SUPPLEMENTARY TEXT AND FIGURES</p> <ul> <li><strong>CYChen et al. (2020) QSR - Supplementary Materials.pdf</strong></li> </ul> <p>DATA TABLES FOR PLJ-1 and 1996 CORE</p> <ul> <li><strong>CYChen et al. (2020) QSR - U-Th Data Tables for PLJ-1 and 1996 cores - v2.xlsx</strong>: U-Th data tables for the PLJ-1 and 1996 core (with all digits preserved where possible, for reproducibility and data longevity)</li> <li><strong>CYChen et al. (2020) QSR - Other Data for PLJ-1 core - v2.xlsx</strong>: sample locations and corresponding elemental concentrations, carbon coulometry, and color reflectance data</li> <li><strong>CYChen et al. (2020) QSR - Ostracode Analysis for PLJ-1 core - v2.xlsx</strong>: ostracode paleoecological data from the PLJ-1 core</li> </ul> <p>MODELING URANIUM-THORIUM ISOTOPIC EVOLUTION OF LAKE SEDIMENTS</p> <ul> <li><strong>CYChen et al. (2020) QSR - Modeling Spreadsheet for Uranium-Thorium Isotopic Evolution of Lake Sediments - v2.xlsx</strong>: spreadsheet for simulating the uranium-thorium isotopic evolution of a sample that has undergone open system behavior with respect to uranium and/or a sample of mixed composition (an impure carbonate)</li> </ul> <p>AGE-DEPTH MODEL</p> <ul> <li><strong>CYChen et al. (2020) QSR - Input Your Own Depths for Age-Depth Model - v2.xlsx</strong>: spreadsheet for extracting the radiometric-based age-depth model generated by Bacon by splice depth</li> </ul>
Hare's affairs: lessons learnt from a noninvasive genetic monitoring for tracking mountain hare individuals
<p>Systematic monitoring of individuals and their abundance over time has become an important tool to provide information for conservation. For genetic monitoring studies, noninvasive sampling has emerged as a valuable approach, particularly so for elusive or rare animals. Here, we present the five-year results of an ongoing noninvasive genetic monitoring of mountain hares (<i>Lepus timidus</i>) in a protected area in the Swiss Alps. We used nuclear microsatellites and a sex marker to identify individuals and assign species to noninvasively collected feces samples. Through including a marker for sex identification, we were able to assess sex-ratio changes and sex-specific demographic parameters over time. Male abundance in the area showed high fluctuations and apparent survival for males was lower than for females. Generally, males and females showed only little temporary migration into and out of the study area. Additionally, using genotyped tissue samples from mountain hares, European hares (<i>Lepus europaeus</i>) and their hybrids, we were able to provide evidence for the first occurrence of a European hare in the study area at an elevation of 2300 m a.s.l. in spring 2016. For future monitoring studies, we suggest to include complementary analysis methods to reliably infer species identities of the individuals analyzed and thus, not only monitor mountain hare individual abundance, but also assess the potential threats given through competitive exclusion by and hybridization with the European hare.</p>
Including intraspecific trait variability to avoid distortion of functional diversity and ecological inference: lessons from natural assemblages
<p>1. Functional diversity assessments are crucial and increasingly used for understanding ecological processes and managing ecosystems. The functional diversity of a community is assessed by sampling traits at one or more scales (individuals, populations, species) and calculating a summary index of the variation in trait values. However, it remains unclear how the scale at which traits are sampled and the indices used to estimate functional diversity may alter the patterns observed and inferences about ecological processes.</p> <p>2. For 40 plant and 61 ant communities, we assess functional diversity using six methods – encompassing various mean-based and probabilistic methods – chosen to reflect common scenarios where different levels of detail are available in trait data. We test whether including trait variability at different scales (from individuals to species) alter functional diversity values calculated using volume-based and dissimilarity-based indices, Functional Richness (FRic) and Rao, respectively. We further test whether such effects alter the functional diversity patterns observed across communities and their relationships with environmental drivers such as abiotic gradients and occurrences of invasive species.</p> <p>3. Intraspecific trait variability strongly determined FRic and Rao. Methods using only species' mean trait values to calculate FRic (convex hulls) and Rao (Gower-based dissimilarity) distorted the patterns observed when intraspecific trait variability was considered. These distortions generated Type I and Type II errors for the effects of environmental factors structuring the plant and ant communities.</p> <p>4. The high sensitivity of FRic to individuals with extreme trait values was revealed in comparisons of different probabilistic methods including among-individual and among-population trait variability in functional diversity. By contrast, values and ecological patterns in Rao were consistent among methods including different scales of intraspecific trait variability.</p> <p>5. Decisions about where traits are sampled and how trait variability is included in functional diversity can drastically change the patterns observed and conclusions about ecological processes. We recommend sampling the traits of multiple individuals per species and capturing their intraspecific trait variability using probabilistic methods. We discuss how intraspecific trait variability can be reasonably estimated and included in functional diversity in the common circumstance where only limited trait data are available.</p>
Figure 6. - ATruncatoflabellummortenseni, USNM 97522, paratype, Philippines B Truncatoflabellumaustraliensis, paratype (including anthocaulus), USNM 96652, Western Australia C Truncatoflabellumcandeanum, neotype, including anthocaulus, USNM 81963, Albatross 5369, Philippines D Truncatoflabellumcompressum, upper figure, illustration of type from Lesson (1827); other views from Challenger 190, BM 1880.11.25.78. Scale bars: all 10 mm.
Figure 6. - ATruncatoflabellummortenseni, USNM 97522, paratype, Philippines B Truncatoflabellumaustraliensis, paratype (including anthocaulus), USNM 96652, Western Australia C Truncatoflabellumcandeanum, neotype, including anthocaulus, USNM 81963, Albatross 5369, Philippines D Truncatoflabellumcompressum, upper figure, illustration of type from Lesson (1827); other views from Challenger 190, BM 1880.11.25.78. Scale bars: all 10 mm.
Water ethics and substantive democracy. Lessons from the Latin American Water Tribunal.
<p>Presentation made at Panel 5, the Educational, Ethical, and Preservationist Water Question, The Youth and the Climatic Struggle, First International Forum of the Federal University of Pernambuco (UFPE), in Preparation for the 2025 UNCOP30, 21-24 November 2023 Recife, Pernambuco, Brazil.</p>
Data supporting "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables"
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</p>
Research data supporting ""Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables""
<p>This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).</p>
Population genetics as a tool to elucidate pathogen reservoirs: Lessons from Pseudogymnoascus destructans, the causative agent of White-Nose disease in bats
<p>Emerging infectious diseases pose a major threat to human, animal, and plant health. The risk of species-extinctions increases when pathogens can survive in the absence of the host. Environmental reservoirs can facilitate this. However, identifying such reservoirs and modes of infection is often highly challenging. In this study, we investigated the presence and nature of an environmental reservoir for the ascomycete fungus <i>Pseudogymnoascus destructans</i>, the causative agent of White-Nose disease. Using 18 microsatellite markers, we determined the genotypic differentiation between 1,497 <i>P. destructans</i> isolates collected from nine closely situated underground sites where bats hibernate (i.e., hibernacula) in Northeastern Germany. This approach was unique in that it ensured that every isolate and resulting multi-locus genotype was not only present, but also viable and therefore theoretically capable of infecting a bat. The distinct distribution of multi-locus genotypes across hibernacula demonstrates that each hibernaculum has an essentially unique fungal population. This would be expected if bats become infected in their hibernaculum (i.e., the site they spend winter in to hibernate) rather than in other sites visited before they start hibernating. In one hibernaculum where both the walls and the hibernating bats were sampled at regular intervals over five consecutive winter seasons (1,062 isolates), higher genotypic richness was found on walls compared to bats and multi-locus genotypes showed a stable frequency over multiple winters. This clearly implicates hibernacula walls as the main environmental reservoir of the pathogen, from which bats become re-infected annually during hibernation.</p>
Alignments used for the phylogenies in "Caryophylliids (Anthozoa, Scleractinia) have a mitochondrial gene rearrangement: lesson learned from mitochondrial and nuclear phylogenomics"
<p>"mitochondrial_caryotree.phy": concatenated alignment of mitochondrial data in phylip format; "mitochondrial_caryotree.partitions.txt": indication of start/stop positions of each partition in the mitochondrial data alignment; "nuclear_caryotree_55taxa-50p.phylip": alignment of nuclear exons and ultraconserved elements in phylip format.</p>
Diamond hits: Another lesson from unmapped reads – in depth analysis of RNA-Seq reads from various horse tissues
<p>Diamond hits - predicted peptides were compared against NCBI RefSeq protein (NR) databases using Diamond software</p> <p>Data: loin adipose - AD, hoof lamina - LM, liver - LI, longissimus muscle - LO, left lung - LU, heart left ventricle - LV, ovary- OV and parietal cortex – PC<br> 1 – horse ECA_UCD_AH1<br> 2 – horse ECA_UCD_AH2</p>
Diamond hits_1: Another lesson from unmapped reads – in depth analysis of RNA-Seq reads from various horse tissues
<p>Diamond hits - de novo assembled transcripts were compared against NCBI RefSeq Nucleotide (NT) using Diamond software</p> <p> </p> <p>Data: loin adipose - AD, hoof lamina - LM, liver - LI, longissimus muscle - LO, left lung - LU, heart left ventricle - LV, ovary- OV and parietal cortex – PC<br> 1 – horse ECA_UCD_AH1<br> 2 – horse ECA_UCD_AH2</p> <p> </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.