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.
6,059
datasets available to search
ShareScore release 0.9.0
Dataset results
6,059 results for “Journale”
Supplementary material 1 from: Tanaka S, Kotaki T, Nishide Y, Ben-Hamouda A, Abdellaoui K, Ebbe MAB, Ely SO (2019) Effects of water extracts of feces from three locust species and various plants on oviposition and embryonic development in the desert locust Schistocerca gregaria. Journal of Orthoptera Research 28(2): 195-204. https://doi.org/10.3897/jor.28.34665
: Explanation note: Fig. S1. Effects of water extracts of desert locust frass after feeding on various plants on the numbers of egg pods laid when adult female desert locusts were presented with extracts mixed with sand.
Supplementary material 3 from: Tanaka S, Kotaki T, Nishide Y, Ben-Hamouda A, Abdellaoui K, Ebbe MAB, Ely SO (2019) Effects of water extracts of feces from three locust species and various plants on oviposition and embryonic development in the desert locust Schistocerca gregaria. Journal of Orthoptera Research 28(2): 195-204. https://doi.org/10.3897/jor.28.34665
: Explanation note: Fig. S3. Effects of water extracts of rescue grass-fed desert locust frass on antennal lengths of embryos (A, mean ± SD; n = 6–10).
Supplementary material 2 from: Heller K-G, Hemp C, Massa B, Kociński M, Warchałowska-Śliwa E (2018) Paraplangia sinespeculo, a new genus and species of bush-cricket, with notes on its biology and a key to the genera of Phaneropterinae (Orthoptera: Tettigonioidea) from Madagascar. Journal of Orthoptera Research 27(2): 143-153. https://doi.org/10.3897/jor.27.24243
Supplementary material 2 from: Heller K-G, Hemp C, Massa B, Kociński M, Warchałowska-Śliwa E (2018) Paraplangia sinespeculo, a new genus and species of bush-cricket, with notes on its biology and a key to the genera of Phaneropterinae (Orthoptera: Tettigonioidea) from Madagascar. Journal of Orthoptera Research 27(2): 143-153. https://doi.org/10.3897/jor.27.24243
Supplementary material 1 from: Heller K-G, Hemp C, Massa B, Kociński M, Warchałowska-Śliwa E (2018) Paraplangia sinespeculo, a new genus and species of bush-cricket, with notes on its biology and a key to the genera of Phaneropterinae (Orthoptera: Tettigonioidea) from Madagascar. Journal of Orthoptera Research 27(2): 143-153. https://doi.org/10.3897/jor.27.24243
Supplementary material 1 from: Heller K-G, Hemp C, Massa B, Kociński M, Warchałowska-Śliwa E (2018) Paraplangia sinespeculo, a new genus and species of bush-cricket, with notes on its biology and a key to the genera of Phaneropterinae (Orthoptera: Tettigonioidea) from Madagascar. Journal of Orthoptera Research 27(2): 143-153. https://doi.org/10.3897/jor.27.24243
Dataset of the journal article "Spatial Reuse in IEEE 802.11ax WLANs"
<p>This dataset contains both the inputs and the outputs from the conference article "Spatial Reuse in IEEE 802.11ax WLANs", authored by Francesc Wilhelmi, Sergio Barrachina, Cristina Cano, Ioannis Selinis and Boris Bellalta. The article has been sent to IEEE Surveys & Tutorials.</p> <p>Regarding the Komondor's input, we provide the files used in the Komondor simulator, as well as the execution scripts, for generating the results presented in the paper. We find "toy" and "random" scenarios, which cover different parts of the paper. In the random case, we have 39,800 different scenarios, which correspond to 4 network densities, 3 strategies on applying SR, 3 traffic loads, 50 deployments, and 21 different OBSS/PD values. More details are provided in the article. </p> <p>Apart from the Komondor's input, we also provide other Matlab files used in the context of the SFCTMN analytical model (refer to <a href="https://github.com/sergiobarra/SFCTMN/releases/tag/v1.0_11ax_SR">https://github.com/sergiobarra/SFCTMN/releases/tag/v1.0_11ax_SR</a>). </p> <p>Contact information: francisco.wilhelmi@upf.edu</p>
Datasets for Journal of Geophysical Research-Oceans submission
<p>These Datasets are used in a manuscript submitted to JGR-Oceans. Datasets A1 and A2 include the data from the Acoustic Doppler Current Profiler, CTD cast (with a built-in OBS 3+ sensor), and echo sounder.</p> <p>Dataset A1. This dataset includes profiles of velocity, salinity, temperature, and suspended sediment concentration (SSC).</p> <p>Dataset A2. This dataset includes echosounder signal used for image plots in the manuscript.</p>
Dataset used in "Urban Seismic Site Characterization by Fiber-Optic Seismology" by Spica et al. in Journal of Geophysical Research: Solid Earth
<p>Spica et al. (2019, Journal of Geophysical Research: Solid Earth). This repository contains continuous waveforms from DAS for one day and for the stations mentioned in the article (Fig. 8).</p> <p>Files: all traces in miniseed format. Traces stats in the headers and in the article.</p>
Data for: Palma, N. and Reis, J. (2019). From convergence to divergence: Portuguese economic growth, 1527-1850. Journal of Economic History 79 (2): 477-506
<p>Data for: Palma, N. and Reis, J. (2019). From convergence to divergence: Portuguese economic growth, 1527-1850. Journal of Economic History 79 (2): 477-506 </p>
Journal of Hymenoptera Research: Journal of Hymenoptera Research (archived)
The Journal of Hymenoptera Research is a peer-reviewed, open-access, rapid online journal launched to accelerate research on all aspects of Hymenoptera, including biology, behavior, ecology, systematics, taxonomy, genetics, and morphology. All published papers can be freely copied, downloaded, printed and distributed at no charge for the reader. Authors are thus encouraged to post the pdf files of published papers on their homepages or elsewhere to expedite distribution. There is no charge for color. ISSN 1314-2607 (online) | ISSN 1070-9428 (print)<p></p>The Journal of Hymenoptera Research is a peer-reviewed, open-access, rapid online journal launched to accelerate research on all aspects of Hymenoptera, including biology, behavior, ecology, systematics, taxonomy, genetics, and morphology. All published papers can be freely copied, downloaded, printed and distributed at no charge for the reader. Authors are thus encouraged to post the pdf files of published papers on their homepages or elsewhere to expedite distribution. There is no charge for color. ISSN 1314-2607 (online) | ISSN 1070-9428 (print)
SEAHI PUBLICATIONS AND ACADEMIC JOURNALS
Open the record for dataset details and reuse information.
Epistemic communities in knowledge organization: an analysis of research trends in the Knowledge Organization journal
<p><span>Considering knowledge organization as a domain in a continuous process of theoretical-methodological consolidation, it becomes important to identify its epistemological configuration and “epistemic communities” in order to measure its impact on society and scholarship. As a reflection of the ISKO and knowledge organization activities, the Knowledge Organization (KO) journal was analysed for the period 1994-2014 in order to identify and characterise the core of researchers that the knowledge organization community consider as central and more impactful. In addition, we analysed the impact of this literature on the LIS scientific domain as a whole, by means of the presence of such literature also in scholarly journals other than KO. The results reveal that the consolidation process of the KO domain and the epistemic communities’ progress occur in a dynamic and well-balanced way with a strong and widespread impact on the LIS scientific context as a whole. The dynamic and evolving nature of these groups, with citations growing in number and new authors slowly emerging for each period, makes it possible to conclude that the ISKO epistemic communities and their impact are not only changing but also expanding.</span></p>
Replication package for Journal paper title "Supporting the identification of prevalent quality issues in code changes by analyzing reviewers' feedback"
<p>Replication package for the study "Supporting the identification of prevalent quality issues in code changes by analyzing reviewers' feedback"</p> <p> </p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Journal of Geophysical Research: Oceans.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF) and Convolutional Neural Network (CNN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <div>[1] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</div> <div>[2] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
Huawei or The American Way? Summary of Journal Article
Open the record for dataset details and reuse information.
Nature Gratitude Journal Intervention Study
<p>The analysis of the data is presented in an article entitled "How to increase nature connectedness? Effectiveness and mechanisms of a gratitude journal intervention" by Andreas Samus, Katharine Dickinson, Claire Freeman and Yolanda van Heezik published in People and Nature.</p>
Datasets supplementing journal article "Scalable Bottom-up Synthesis of Co-Ni-Doped Graphene"
<p>Datasets supporting the journal article "Scalable Bottom-up Synthesis of Co-Ni-Doped Graphene".</p>
Supplementary tables for "Inconsistencies and opacity in retraction notices: Snapshot using 50 cases from Elsevier journals"
Open the record for dataset details and reuse information.
Dataset for the plots in paper: 'A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation' in 'Journal of Computational Physics'
<p>The .xlsx file contains the data used for the plots Fig. 3, 4, 7, 8 and 9 in the paper 'Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation'.</p>
Core competencies of a scholarly journal editor. Database and citation search report and PRISMA_flow_diagram
<p><strong>Report on Database and Citation search and PRISMA_flow_diagram</strong></p>
Endorsement of reporting guidelines and clinical trial registration across urological medical journals: a cross-sectional study
Open the record for dataset details and reuse information.
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.