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FIGURE 28 in Systematic position and composition of Merodon nigritarsis and M. avidus groups (Diptera, Syrphidae) with a description of four new hoverfLies species
FIGURE 28 Head of male Merodon cohurnus sp. n. A) lateral view, B) dorsal view. Scale: 1 mm.
Dataset from 'Klever, L., Islam, J., Võ, M., & Billino, J. (2023). Aging attenuates the memory advantage for unexpected objects in real-world scenes. Heliyon, e20241, doi: 10.1016/j.heliyon.2023.e20241
<p>We provide two files; one data file contains data on which analyses are based, the other one gives the column labels for the data file.</p> <p>___________________________________________<br> For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de</p>
HIGGS_M_WP5_423_OST_Techno-economic-model_190620171000_230922
<p>Metadata for graphs of HIGGS' deliverable D5.3 "Intermediate report: key findings on potential and enablers"</p>
HIGGS_M_WP5_423_OST_Techno-economic-model_Techno-economic-model_111120171800_230922
<p>Metadata of graphs of HIGGS' deliverable D5.3 "Intermediate report: key findings on potential and enablers"</p>
Monthly aggregated GLASS FAPAR V6 (250 m): 50th percentile monthly time-series (2005)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
Data supporting 'The Response of Midlatitude Surface Temperature Persistence to Arctic Sea-Ice Loss' by Neil T Lewis, William J M Seviour, Hannah E Roberts-Straw, and James A Screen.
<p>Data supporting Lewis et al., 2023. The Response of Midlatitude Surface Temperature Persistence to Arctic Sea-Ice Loss. Submitted to Geophysical Research Letters.</p><p>All model output is contained within the folder data/. All data is in NetCDF format.</p><p>The folder data/PAMIP/ contains output from coupled AOGCMs that contributed piArcSIC and futArcSIC timeslice runs to PAMIP. The AOGCMS present are: HadGEM3-GC31-MM, IPSL-CM6A-LR, CESM2-WACCM6, and CESM-WACCM-SC. For each model + run, two data files are included. One contains the autocorrelation of surface temperature, at 5, 10, and 15 day lags. The second contains the frequency and duration of persistent extremes (as defined in Lewis et al., 2023).</p><p>Additional output is included in data/PAMIP/ from extended pdSIC-ext and futArcSIC-ext experiments run using CNRM-CM6-1. For each run, a file containing the autocorrelation of surface temperature (as above) is included.</p><p>The folder data/CMIP/ contains output from CMIP6 historical/SSP585 runs using three of the models listed above: HadGEM3-GC31-MM, IPSL-CM6A-LR, and CESM2-WACCM6. For each model, 'pre-industrial' and 'future' output is available. Output in these files was computed from 30-year time-periods, subsampled from the historical/SSP585 runs, selected so that the 30-year average sea-ice area matched that in the corresponding PAMIP runs above. For each model and time period, two data files are included. One contains the autocorrelation of surface temperature, at 5, 10, and 15 day lags. The second contains the frequency and duration of persistent extremes (as defined in Lewis et al., 2023).</p><p>Output is also included in data/CMIP/ from CNRM-CM6-1 'present day' and 'future' time periods, selected to match the sea-ice area in the CNRM -ext PAMIP runs. For this model, output files contain the autocorrelation of surface temperaure. </p>
AltGen: 1.3M Plausible Alternatives From Neural Text Generators
<h2>AltGen: 1.3M Plausible Alternatives From Neural Text Generators</h2><p>The AltGen dataset contains 1.3 million English texts generated by neural language generators conditioned on contexts from three corpora of acceptability judgements and two corpora of reading times. </p><p>For each corpus, each text generator, and each sampling algorithm,100 generations are sampled—for a total of 1,257,300 generations. Details about the language generators and the corpora are presented in a paper published at EMNLP 2023 (in particular, Section 4). Please cite this paper if you use any version of the dataset in your work:</p><blockquote><p>Mario Giulianelli, Sarenne Wallbridge, and Raquel Fernández. 2023. <strong>Information Value: Measuring Utterance Predictability as Distance from Plausible Alternatives</strong>. In <i>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</i>. Association for Computational Linguistics.</p></blockquote><p>The files are in jsonl format and include a <i>context_id</i> field, which allows retrieving the relevant entry from the original corpus, and the <i>alternatives</i> field, which contains the language model generations. Please note that the alternatives are not post-processed (see code and footnote 2 in the paper for further details). Filenames are built as follows: <i>DecodingAlgorithm</i>_<i>DecodingParameter</i>-n<i>NumAlternatives</i>-maxlen_<i>MaxGenerationLength</i>-sep_<i>Separator.</i>jsonl.</p>
Remero 5 HQ 0,55 M
El Remero perteneciente al grupo escultórico de 'Los atletas' por Fructuoso Orduna Captura en sesión 5 tras fijar el brazo en el último paso del proceso de restauración Source: Objaverse 1.0 / Sketchfab
Posterior Tibial Nerve Stimulation (PTNS) Plus Mirabegron to Treat Refractory OAB Symptoms (PTNS-M Study)
ClinicalTrials.gov study NCT04907032. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Evaluation of Early Bactericidal Activity in Pulmonary Tuberculosis With(J-M-Pa-Z) (NC-001)
ClinicalTrials.gov study NCT01215851. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Preventive Treatment of Oxaliplatin Induced Peripheral Neuropathy in Metastatic Colorectal Cancer (POLAR-M)
ClinicalTrials.gov study NCT03654729. IPD Sharing: NO. Countries: 13. Publications: 1.
Study of Posoleucel (ALVR105,Viralym-M) for Multi-Virus Prevention in Patients Post-Allogeneic Hematopoietic Cell Transplant
ClinicalTrials.gov study NCT05305040. IPD Sharing: NO. Countries: 10. Publications: 2.
Immune Response & Safety of a Hepatitis A Vaccine Given Together With a Pneumococcal Vaccine in Healthy Children 15 m of Age
ClinicalTrials.gov study NCT00197002. IPD Sharing: YES. Countries: 1. Publications: 1.
Medical Decisions in the Hospital Setting- M-Safety
ClinicalTrials.gov study NCT03757884. IPD Sharing: YES. Countries: 1. Publications: 1.
Neoadjuvant Chemotherapy With Methotrexate, Vinblastine, Adriamycin and Cisplatin (M-VAC) Plus Avastin in Patients With Urothelial Cancer
ClinicalTrials.gov study NCT00506155. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Comparison of Quality and Quantity of M-PRP Cellular Content Filgrastim vs. Pegfilgrastim
ClinicalTrials.gov study NCT05573386. IPD Sharing: NO. Countries: 1. Publications: 50.
Study to Evaluate Viralym-M (ALVR105) for the Treatment of Virus-Associated Hemorrhagic Cystitis (HC)
ClinicalTrials.gov study NCT04390113. IPD Sharing: NO. Countries: 7. Publications: 2.
Posoleucel (ALVR105, Formerly Viralym-M) for Multi-Virus Prevention in Patients Post-Allogeneic Hematopoietic Cell Transplant
ClinicalTrials.gov study NCT04693637. IPD Sharing: NO. Countries: 1. Publications: 3.
Immunogenicity of GSKs' MMR Vaccine (209762) vs. M-M-R® II, When Given With Routine Vaccines at 12-15 Months of Age
ClinicalTrials.gov study NCT00861744. IPD Sharing: YES. Countries: 2. Publications: 2.
V+PSF-M for Tobacco Cessation in HIV Care in India
ClinicalTrials.gov study NCT05786547. IPD Sharing: YES. Countries: 2. Publications: 1.
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.