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Figure 4 in Dynamics of meiobenthic fauna in a tropical sandy coast (Chennai, Bay of Bengal) with reference to the physicochemical parameters
Figure 4. CCA analysis between meiobenthic groups and physicochemical parameters.
Figure 2 in Dynamics of meiobenthic fauna in a tropical sandy coast (Chennai, Bay of Bengal) with reference to the physicochemical parameters
Figure 2. Physicochemical parameters of the Chennai coast (a- Phosphate; b- Nitrate).
Figure 1 in Dynamics of meiobenthic fauna in a tropical sandy coast (Chennai, Bay of Bengal) with reference to the physicochemical parameters
Figure 1. Sampling locations.
Combining reference-star and angular differential imaging for high-contrast imaging of extended sources - Appendix
<p>This appendix presents the disk estimations obtained with RDI, ADI, and ARDI, using IPCA, for all 60 test data sets considered in Juillard et. al. (2024). Page-sized figures shows IPCA disk estimations obtained leveraging RDI, ADI and ARDI strategies for cube 1 to 4 and disks A to E.</p> <p>Details on the data sets where disks were injected can be found in Juillard et. al. (2023) and Juillard et. al. (2024), and can be found on the Zeneodo folder : <a href="../records/11442267">https://zenodo.org/records/11442267</a></p> <p>Each page-sized figure presents the results for a specific synthetic disk in a given data set. The injected disks (A→E) are shown in the left-most column of each figure. Each figure contains three pairs of rows, each corresponding to a different injected contrast level: $10^{-3}$ (top), $10^{-4}$ (middle), and $10^{-5}$ (bottom). The top row of each pair represents the estimation, while the bottom row represents the residuals. Each column displays the following in order: ground truth (left), RDI (middle left), ADI (middle right), and ARDI (right). The best method, as determined by each metric, is indicated in the bottom-left corner of each pair of rows. The color bar bounds in the residual plots is set to $\pm$ maximum of intensity of the GT, centered at 0. For the disk image estimation plots, the color bar is adjusted for each data set to have a maximum value equal to the 99th percentile of the image. The minimal value is set to 0. Additionally, the optimal IPCA parameters (rank and number of iterations) are written at the top left of the corresponding images.</p>
Reference genome data used for benchmarking SingleM
<p>For use as part of https://github.com/wwood/singlem-benchmarking/</p>
Plastic additive qualitative analysis - Plastic reference materials
<p><span> Clusters of tabular data (with metadata including variable labels, code labels and definition of missing value) reporting plastic additive profile in the reference materials with ID P3, P4, P5, P6, P8 and P9. </span><span>Quality assurance measures were in place during analysis including calibration curves, blank measurements and use of validated methods.</span></p>
Figs 124–125. Asiodixa spp., wings.124 in DIPTERA OF THAILAND A summary of the families and genera with references to the species representations
Figs 124–125. Asiodixa spp., wings.124 = A. maculosa sp. n.; 125 = A. pura sp. n.
Fig. 54 in DIPTERA OF THAILAND A summary of the families and genera with references to the species representations
Fig. 54. Stypocladius appendiculatus (HENDEL) (del. A. SZAPPANOS)
Fig. 115 in DIPTERA OF THAILAND A summary of the families and genera with references to the species representations
Fig. 115. Stenocyamops thaii sp. n., holotype male, habitus (del. A. SZAPPANOS)
genomecomb reference data for Homo sapiens (hg38) version 0.107.0
Open the record for dataset details and reuse information.
Dataset for FEM modelling of emission reference material (ERM) developed in the EMPIR project 20NRM04 MetrIAQ
<p>This dataset contains the numerical data used in the project 20NRM04 MetrIAQ for the FEM model simulations on the emission reference material (ERM) with the software COMSOL 6.2. The data published here are freely accessible with a text editor.<br>The accompanying document '<em>FEM data and file name explanations.pdf'</em> contains additional information for each data file.<br>A detailed description of the development of the FEM model can be found in the '<em>Guideline with recommendations on the customised design and dimensioning of the ERM, including the uncertainty of the numerical model developed for the prediction of the emission rate. The numerical model will (i) support the customised generation of the ERM (ii) simulate the transport processes inside the ERM and the compound release into test chamber air and (iii) enable the prediction of the emissions for each of the selected target VOC</em>' (<a href="https://doi.org/10.5281/zenodo.12749007" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12749007</a>).</p>
Northern and central Walker Lane horizontal velocities in North America and Sierra Nevada reference frames.
<p>The data in this file contains east and north velocities covering the northern and central sections of the Walker Lane, USA. The velocities were obtained from GPS time series, available at Nevada Geodetic Laboratory (NGL): http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html (24 hour final solutions). The position time series were manually screened and corrected for offsets, and a local common-mode filter was applied following the methodology of Kreemer & Blewitt (2021, https://doi.org/10.1007/s00190-020-01466-5). Velocities in the North America frame were obtained using MIDAS, a robust median trend estimator (Blewitt et al., 2016, https://doi.org/10.1002/2015JB012552). Two sets of stations were used to rotate the velocities into the Sierra Nevada frame: CAOV, CAPV, P140, P276, P310 for the northern section and CMBB, P245, P305, P308, P512 for the central section.</p> <p>The file contains the following columns:</p> <ul> <li>sta: station ID</li> <li>lon: longitude of station (decimal degree)</li> <li>lat: latitude of station (decimal degree)</li> <li>ve_NA: east velocity in North America frame (mm/yr)</li> <li>vn_NA: north velocity in North America frame (mm/yr) </li> <li>ve_SN: east velocity in Sierra Nevada frame (mm/yr) </li> <li>vn_SN: north velocity in Sierra Nevada frame (mm/yr) </li> <li>region: denotes whether the set of stations used to rotate the velocities into the Sierra Nevada frame are located in the northern (N) or central (C) Walker Lane</li> <li>sve: east velocity uncertainty (mm/yr)</li> <li> svn: north velocity uncertainty (mm/yr)</li> <li>network: who operates the station</li> </ul>
Improved estimation of the prevalence of bovine cysticercosis and the diagnostic test characteristics in the absence of a reference standard using Bayesian Latent Class models, the example of Jimma and Ambo Abattoirs, Ethiopia
<p>Bovine cysticercosis is an infection of cattle musculature with the cestode parasite of humans known as Taenia saginata. This bovine cysticercosis data was collected from two Ambattoirs in Ethiopia namely Ambo and Jimma. Dissection of the predilection site, Ag-ELISA, and meat inspection were the diagnostic methods employed. Cysticerci collected during dissection of the predilection site were also confirmed using multiplex PCR. </p>
ddhN biomarkers NMR reference datasets
<p>To visualize the data please click on the following link: <a title="https://zenodo.nmrium.org/zenodo/v1/record/12806559" href="https://zenodo.nmrium.org/zenodo/v1/record/12806559" target="_blank" rel="noopener">https://zenodo.nmrium.org/zenodo/v1/record/12806559</a></p> <p>Reference 1D and 2D spectra from publication: <a href="https://doi.org/10.1021/acs.jproteome.3c00654" target="_blank" rel="noopener">10.1021/acs.jproteome.3c00654</a></p> <p>For further data, please also see zenodo entry: <a href="../doi/10.5281/zenodo.7906157">10.5281/zenodo.7906157</a></p>
Thermodynamic (p, ρ, T) characterization of a reference high-calorific natural gas mixture when hydrogen is added up to 20 % (mol/mol)
<p>File: 1-s2.0-S0360319924017166-mmc1.docx</p> <p>This file (DOCX) is a supplementary file that contains the mixture preparation chart and auxiliary mixture validation data.</p> <p>File: 2024_IJHE_Results_Repository.xlsx</p> <p>This file (XLSX) contains tables with the density data of the systems investigated.</p> <p>File: 2024_IJHE_Manuscript_repository.docx</p> <p>This is an author-created, un-copyedited version of an article accepted for publication in the International Journal of Hydrogen Energy (2024, 70, 118-135). The editor of the Journal is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated, Open-Access version is available online at: https://doi.org/10.1016/j.ijhydene.2024.05.028</p>
YA Domain Dataset: Dataset of scholarly bibliographic references on YouTube videos
<p><strong>Abstract</strong></p> <p>Scholarly communication through YouTube videos has been increasing. Although Altmetric (<a href="https://altmetric.com/">https://altmetric.com/</a>) provides the dataset on such references, its coverage is unclear, and it does not contain the original external links in each video. Considering this background, we built and published a dataset of scholarly bibliographic references on YouTube videos by using YouTube Data API v3, targeting six types of domain names: "doi.org," "ncbi.nlm.nih.gov," ieeexplore.ieee.org," "link.springer.com," "onlinelibrary.wiley.com," and "sciencedirect.com." As a result, we identified approximately 480,000 references associated with Crossref DOIs among 230,000 videos published by December 31, 2023, posted on 55,000 channels. Notably, over half of these references were not covered by the Altmetric dataset, resulting in a 150% increase in the number of references when combining the dataset constructed by the proposed method with the Altmetric dataset, compared to the Altmetric dataset alone. Regarding external links, PubMed and DOI links were prominent; however, a substantial number of direct links to publisher platforms were observed. Most channels and videos contained external links to a single platform, scattered across each platform. This dataset is helpful for identifying and analyzing scholarly references on YouTube.<br>As for the original paper related to this dataset, please refer to the references section.</p> <p> </p> <p><strong>Data Records</strong></p> <p>The data format of the dataset is JSON lines, where each line is a single record. The data is split into files by DOI Registration Agencies. A sample of the record is as follows:</p> <table> <tbody> <tr> <td>{<br> "channel_id": "UCEfEi-IMiB87UsxY3765P6w",<br> "video_id": "e7YmyVd4uOE",<br> "is_covered_by_altmetric_com": false,<br> "youtube_data_api_search": [<br> {<br> "query": "doi.org",<br> "uri": "http://dx.doi.org/10.1145/2807442.2814654"<br> }<br> ],<br> "doi": "10.1145/2807442.2814654",<br> "doiRA": "Crossref"<br>}</td> </tr> </tbody> </table> <ul> <li>channel_id (String) -- Channel ID of the YouTube channel that uploaded the video.</li> <li>video_id (String) -- Video ID.</li> <li>is_covered_by_altmetric_com (Boolean) -- Whether this reference is covered by altmetric.com or not.</li> <li>youtube_data_api_search (Array) <ul> <li> query (String) -- The query used in the search:list of YouTube Data API v3. (<a href="https://developers.google.com/youtube/v3/docs/search/list?hl=en">https://developers.google.com/youtube/v3/docs/search/list?hl=en</a>)</li> <li> uri (String)-- The original external links written in the description text or video title in each video.</li> </ul> </li> <li>doi (String) -- DOI corresponding to the bibliographic reference in the video.</li> <li>doiRA (String) -- DOI registration agency for the DOI. We obtained this data using the WhichRA? API (<a href="https://www.doi.org/the-identifier/resources/factsheets/doi-resolution-documentation#4-which-ra">https://www.doi.org/the-identifier/resources/factsheets/doi-resolution-documentation#4-which-ra</a>).</li> </ul> <p>We note that the altmetric dataset obtained from Altmetric Explorer in this study is not included in this dataset.</p> <p><strong>References</strong></p> <ul> <li>Kikkawa, Jiro; Takaku, Masao; Yoshikane, Fuyuki: "Enhancing Identification of Scholarly Reference on YouTube: Method Development and Analysis of External Link Characteristics", <em>Proceedings of the 28th International Conference on Theory and Practice of Digital Libraries (<a href="https://tpdl2024.nuk.si/">TPDL 2024</a>)</em>, Ljubljana, Slovenia, Lecture Notes in Computer Science (LNCS), Vol.15178, 2024.09. (in press).</li> </ul> <p><strong>Fundings</strong></p> <p>JSPS KAKENHI Grant Numbers <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K18147/">JP22K18147</a>, <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-23K11761">JP23K11761</a>, and <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-24K15652">JP24K15652</a>.</p>
Supplemental files for the manuscript "Chromosome assembly of large and complex genomes using multiple references"
<p>This archive contains supplemental files for the manuscipt: "Chromosome assembly of large and complex genomes using multiple references".</p> <p>It contains assemblies generated by Ragout and RACA as well as evaluation scripts that were used in our analysis.</p> <p>Each subdirectory contains an additional README file with details.</p> <p>Please note that some intermediate files were deleted in the interest of saving space. If you need access to those files or having issues with reproducing our results, don't hesitate to contact Mihkail Kolmogorov: fenderglass@gmail.com</p>
Bibliographic references extracted from theses and dissertations in the Electronic Theses and Dissertations Library of the Federal University of Paraná, Brazil
<p>Bibliographic references obtained from a <a href="https://doi.org/10.5281/zenodo.1169586">subset of PDF files in the Electronic Theses and Dissertations Library of the Federal University of Paraná, Brazil</a>, extracted through ParsCit.</p>
rpsC reference database for xander
<p>Reference gene database (<em>rpsC</em>) for Xander (https://github.com/rdpstaff/Xander_assembler).</p>
Sentinel-2 reference cloud masks generated by an active learning method
<p> <strong>Reference classifications generated with Active Learning for Cloud Detection (ALCD)</strong></p> <p>This data set provides a reference cloud mask data set for 38 Sentinel-2 scenes. These reference masks have been created with the ALCD tool, developed by Louis Baetens, under the direction of Olivier Hagolle at CESBIO/CNES[1]. They were created to validate the cloud masks generated by the MAJA software [2].</p> <p>- The `Reference_dataset` directory contains 31 scenes selected in 2017 or 2018.<br> - The `Hollstein` directory contains 7 scenes that were used to validate the ALCD tool by comparison to manually generated reference images kindlyprovided by Hollstein et al[3]<br> One of these scenes is present in both directories. For the validation of MAJA, the "Hollstein" scenes were not used because of their acquisition at a time period when Sentinel-2 was not yet operational, with a degraded repetitivity of observations.</p> <p><strong># Description of the data structure</strong><br> The name of each scene directory is the name of the corresponding Sentinel-2 L1C product.<br> In the scene directory, three sub-directories can be found.<br> - `Classification`<br> - `Samples`<br> - `Statistics`</p> <p><strong># Description of the files</strong><br> - `Classification/classification_map.tif` --- the main product, which is the classified scene. 7 classes are available. Each one is represented with a different integer.<br> 0: no_data.<br> 1: not used.<br> 2: low clouds.<br> 3: high clouds.<br> 4: clouds shadows.<br> 5: land.<br> 6: water.<br> 7: snow.</p> <p>- `Classification/confidence_enhanced.tif` --- enhanced confidence map of the classification. The values are between 0 and 255 (coded on 1 bit).<br> The original confidence map is, for each pixel, the proportion of votes for the majority class as the classification map has been created via a Random Forest algorithm.<br> A median filter has been applied to this confidence map. Finally, the value was saved on 1 bit, leading to the value being between 0 and 255.</p> <p>- `Classification/contours.png` --- the contours of the classes from the classification map, overlayed on the scene. The color code depends on each class.<br> Green: low and high clouds. Yellow: cloud shadows. Blue: water. Purple: snow.</p> <p>- `Classification/used_parameters.json` --- the parameters that were used to classify the scene. It includes the tile code, the cloudy and clear dates, along with their product reference.</p> <p>- `Samples/` --- this directory contains all the shapefiles, one per class.</p> <p>- `Statistics/k_fold_summary.json` --- results of the 10-fold cross-validation on the scene.<br> 5 metrics are computed, in the order given in the "metrics_names". "all_metrics" is a list of the 10 folds, with the 5 metrics in the correct order for each fold.<br> "means" and "stds" are the means and standard deviations of the 10 folds.</p> <p><br> <strong># References</strong></p> <p>[1] Baetens, L.; Desjardins, C.; Hagolle, O. Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. <em>Remote Sens.</em> <strong>2019</strong>, <em>11</em>, 433.</p> <p>[2] A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images, O Hagolle, M Huc, D. Villa Pascual, G Dedieu, Remote Sensing of Environment 114 (8), 1747-1755, 2010</p> <p>[3] Hollstein, A.; Segl, K.; Guanter, L.; Brell, M.; Enesco, M. Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images. Remote Sens. 2016, 8, 666</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.