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Dataset results
657 results for “July”
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: root, weighting: TTLBOUNDED, days: 1, dates: 27 June 2023-04 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: web, weighting: IDENTITY, days: 1, dates: 27 June 2023-04 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: web, weighting: IDENTITY, days: 1, dates: 13 July 2023-28 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: web, weighting: TTLBOUNDED, days: 1, dates: 27 June 2023-04 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: root, weighting: TTLBOUNDED, days: 7, dates: 27 June 2023-04 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: web, weighting: IDENTITY, days: 7, dates: 13 July 2023-28 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: web, weighting: TTLBOUNDED, days: 7, dates: 27 June 2023-04 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: web, weighting: TTLBOUNDED, days: 7, dates: 13 July 2023-28 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: web, weighting: TTLBOUNDED, days: 1, dates: 13 July 2023-28 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: web, weighting: IDENTITY, days: 7, dates: 27 June 2023-04 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Evaluating the impact of design decisions on passive DNS-based domain rankings: ranking files (type: root, weighting: TTLBOUNDED, days: 7, dates: 13 July 2023-28 July 2023)
See <a href="https://domain-ranking-design-decisions.distrinet-research.be/">https://domain-ranking-design-decisions.distrinet-research.be/</a> for a full project description.
Nutrients, alkalinity (AT), dissolved inorganic carbon (CT) and trace elements in Kongsfjorden, Svalbard July 2020
<p>Original data set for <strong>‘‘The Influence of Glaciers on Biogeochemical Cycles of Carbon, Nutrients and Trace Elements in Arctic Fjord Systems‘‘ </strong>contributing to <span>European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 86938. </span> </p> <p>Data includes basic information (sample name, date, coordinates, depth), primary water column parameters (salinity, temperature, turbidity), dissolved (<22 µm) trace elements (dAl, dV, dFe, dMn, dCo, dNi, dCu, dZn, dCd, dPb), dissolved (<22 µm) nutrients (nitrate, nitrite, silicate, phosphate) and carbonate system parameters (alkalinity and dissolved inorganic carbon) for each sample.</p> <p> </p>
Observing scenarios simulations for HLVK-Configuration for O4 Runs, using 20 million injections. This simulation led to 17,009 BNS useful for training Parameter Estimations of EM counterparts of GW. (July 2024 edition).
<p>We have conducted a simulation of the HLVK-configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>Due to the large file sizes, we have split them into three parts and uploaded them to Zenodo with the following DOIs:<br><br></p> <ol> <li><strong>The first files is located :</strong> <code>runs_part_aa</code> and <code>runs_part_ab</code> : <a title="https://zenodo.org/doi/10.5281/zenodo.12693652" href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a></li> <li><strong>The second files is located : </strong><code>runs_part_ac</code> and <code>runs_part_ad</code> : <a href="../doi/10.5281/zenodo.12694779">https://zenodo.org/doi/10.5281/zenodo.12694779</a></li> <li><strong>The third files is located : </strong><code>runs_part_ae</code> and <code>runs_part_af</code> : <a href="../doi/10.5281/zenodo.12696695">https://zenodo.org/doi/10.5281/zenodo.12696695</a></li> </ol> <blockquote> <p>Download them or use this Python script from GitHub to download all of them by running the script: </p> <p> <a href="https://github.com/weizmannk/ObservingScenariosInsights/blob/main/chunk-xml/zenodo-process/download_split_chunk_data.py">hchunk-files-downloader</a>.</p> </blockquote> <p>After downloading them (6 files ), you will need to combine them in a single file using the following process:</p> <blockquote> <p>1.Combine the parts:<br><code>cat runs_part_* > runs.zip</code></p> </blockquote> <blockquote> <p>2.Verify the combined file:<br><code>ls -lh runs.zip</code><br><code>file runs.zip</code></p> </blockquote> <blockquote> <p>3.Unzip the combined file:<br><code>unzip runs.zip</code></p> </blockquote> <p> </p> <p>In the <code>runs</code> folder, we have three subfolders:</p> <ul> <li><code>O4</code>: This contains the <code>.fits</code> files for skymap localization and all GW parameters of the CBCs that passed the threshold cutoff of 8.</li> <li><code>statistics_results</code>: This contains the summary results of the statistical predictions of GW detections.</li> <li><code>subpopulations</code>: This folder is the split of BNS, NSBH, and BBH events. It is useful for those who need quick parameters of BNS and NSBH for their lightcurve simulations or EM counterpart statistical estimation.</li> </ul> <p> </p> <p>For more information, visit: <a href="https://github.com/lpsinger/observing-scenarios-simulations" target="_new" rel="noreferrer">Observing Scenarios Simulations</a></p> <p>Contact: <a rel="noreferrer">weizmann.kiendrebeogo@oca.eu</a> or <a rel="noreferrer">kiend.weizman7@gmail.com</a></p>
Observing Runs for Binary Neutron Star and Neutron Star-Black Hole mergers in the HLVK-Configuration during O4, using 20 million CBC injections (July 2024 edition)
<p>We have conducted a simulation of the HLVK configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>However, due to size constraints, this repository contains only the simulation data and sky maps of BNS and NSBH detections that have passed the cutoff threshold. For the complete dataset, including BBH, please refer to <a href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a> .</p>
FIGURE 1 in Preface-A carcinological tribute to Ng Ngan Kee (14 April 1966 to 5 July 2022)
FIGURE 1. Ng Ngan Kee in her prime. (Left) Conducting a workshop at the open house event of Raffles Museum of Biodiversity Research (now Lee Kong Chian Natural History Museum) in 2012; (Right) Study trip to India in September 2011. Photographs: (Left) Joelle C.Y. Lai; (Right) Bee Yan Lee.
Names, locations, sampling dates, physical characteristics, water chemistry, and plankton data in samples collected in Canadian Prairie lakes in July and August, 2023
<p>PPR Sample Lake Data.xlsx contains names, locations, sampling dates, physical characteristics, water chemistry, and plankton data in samples collected in Canadian Prairie lakes in July and August, 2023. PPR Sample Lake Fact Sheets.pdf contains fact sheets for every sampled lake including maps and essential lake, catchment, and water chemistry characteristics. </p>
The raw data recorded by the WIOBSS on 22 and 23 July 2009
<p>The data is saved in a TXT file, whose time resolution is one minute. The key wave parameters were 41.66 μs pulse width, 20% duty cycle and 128 pulse train repetition numbers.</p>
epicPCR sequencing data, July 7 2021
<p>Samples: 1. Biolstd, Nomock, Nomag, 16S<br> 2. Biolstd, Nomock, Mag, 16S<br> 3. Biolstd, Nomock, Nomag, 18S<br> 4. Biolstd, Nomock, Mag, 18S<br> 5. Biolstd, Mock, Nomag, 16S<br> 6. Biolstd, Mock, Mag, 16S<br> 7. Biolstd, Mock, Nomag, 18S<br> 8. Biolstd, Mock, Mag, 18S<br> 9. Biolstd+WW, Nomock, Nomag, 16S<br> 10. Biolstd+WW, Nomock, Mag, 16S<br> 11. Biolstd+WW, Nomock, Nomag, 18S<br> 12. Biolstd+WW, Nomock, Mag, 18S<br> 13. Biolstd+WW, Mock, Nomag, 16S<br> 14. Biolstd+WW, Mock, Mag, 16S<br> 15. Biolstd+WW, Mock, Nomag, 18S<br> 16. Biolstd+WW, Mock, Mag, 18S<br> 17. WW, Nomock, Nomag, 16S<br> 18. WW, Nomock, Mag, 16S<br> 19. WW, Nomock, Nomag, 18S<br> 20. WW, Nomock, Mag, 18S<br> 21. WW, Mock, Nomag, 16S<br> 22. WW, Mock, Mag, 16S<br> 23. WW, Mock, Nomag, 18S<br> 24. WW, Mock, Mag, 18S</p>
OLAC Nightly Data Dump (XML) from 18 July 2021
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 18 July 2021. Downloaded on July 18th, therefore likely the nightly compiled on the 17th. OLAC <em>does</em> uniquely name the nightly downloads in their uncompressed form, but it does not persist them.</p>
Neural Network Radiation Emulator (KMA/NIMS), July
<p>The dataset is a part of https://doi.org/10.5281/zenodo.5220712 (July)</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.