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126 results for “downloads”
FAOSTAT AgLU data Archive for gcamfaostat v1.0.1 (Download Oct 16 2024)
<p>This repository contains data files needed to run the <em>gcamfaostat (v1.0.1)</em> package. All the data are publicly available from FAOSTAT and they are downloaded from <a href="https://www.fao.org/faostat/en/#data">FAOSTAT</a> in October 2024. This repo serves as an archive of the source data as FAOSTAT continues updating the data. Note that the zip files provide a snapshot of FAOSTAT data since the historical data may also be revised by FAO. </p> <p>These data should be placed in<em> inst/extdata/FAOSTAT</em> in the R package <em>gcamfaostat v1.0.1</em>. They are used in the package to generate data used in the <em>aglu/FAO</em> folder in <em>gcamdata</em> for GCAM. The package structure ensures the processing is transparent, traceable, and reproducible. <em>gcamfaostat v1.0.1 generates data for GCAM v7.3+.</em></p> <p> </p> <p><strong><em>We have now included more data from FAOSTAT (beyond gcamfaostat needs) and changed the archive version by date. E.g., version 2024.10.16 is downloaded around that date.</em></strong></p> <p><em>Note that gcamfaostat v1.0.0 used a version of FAOSTAT data downloaded in Fall 2022, which produced data in GCAM v7.0. </em></p> <p> </p>
User Feedback Dataset from the Top 15 Downloaded Mobile Applications
<p>This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. <i><strong>The dataset is particularly suitable for Natural Language Processing (NLP) applications</strong></i>, such as text classification and topic modeling.</p><p><strong>List of Included Applications:</strong></p><ul><li>TikTok</li><li>Instagram</li><li>Facebook</li><li>WhatsApp</li><li>Telegram</li><li>Zoom</li><li>Snapchat</li><li>Facebook Messenger</li><li>Capcut</li><li>Spotify</li><li>YouTube</li><li>HBO Max</li><li>Cash App</li><li>Subway Surfers</li><li>Roblox</li><li>Data Columns and Descriptions:</li></ul><p><strong>Data Columns and Descriptions:</strong></p><ul><li>review_id: Unique identifiers for each user feedback/application review.</li><li>content: User-generated feedback/review in text format.</li><li>score: Rating or star given by the user.</li><li>TU_count: Number of likes/thumbs up (TU) received for the review.</li><li>app_id: Unique identifier for each application.</li><li>app_name: Name of the application.</li><li>RC_ver: Version of the app when the review was created (RC).</li></ul><p><strong>Terms of Use:</strong></p><p>This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference:</p><p>M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: <a href="https://doi.org/10.1109/ACCESS.2023.3332644">10.1109/ACCESS.2023.3332644</a>.</p><blockquote><p>Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.</p></blockquote>
Plantago patagonica occurrences from the Colorado Plateau, GBIF download 02/18/2022
<p><em>Plantago patagonica</em> occurrences from the Colorado Plateau, GBIF download 02/18/2022. Downloaded with <em>gbif</em> function from <em>dismo</em> package in R.</p>
ENTICE download speed measurements
<p>A sample of a measurements dataset. Monitoring of a general purpose Cloud storages (e.g. such as AWS S3) is a part of ENTICE Pareto-SLA component. This dataset has been obtained by performing downloads of objects with random data of various sizes, ranging from 1 kB to 1 GB. Each file object has been generated by dd tool: dd if=/dev/urandom of=rand-1M.bin bs=1M count=1. On the server side a Minio S3 server has been setup with a Nginx gateway. The client who performed downloads was residing in the same local 1 Gbps ethernet network as the server. On the server side, network QoS has been controlled through a Linux tc netem tool, emulating various QoS conditions for the packet delay, jitter and packet loss.</p> <p>The dataset contains the following headers:</p> <p>Timestamp - the timestamp of a measurement performed.</p> <p>Client IP - the IP address of a client - anonymised.</p> <p>Server IP - the IP address of a server - anonymised.</p> <p>QoS - min|avg|max|stdev|ploss denoting minimum, average and maximum packet round-trip-time (RTT) between the server and the client, respectively, followed by the standard deviation of the RTT and emulated packet loss with the tc tool, expressed as a percentage.</p> <p>Object size [B] - the size of the file object in bytes.</p> <p>Download time [s] - the download time of the file object from the client perspective.</p> <p>Download speed [B/s] - the ratio (Object size[B]) / (Download time[s]).</p>
Discovering dataset download link, or access via service, from DOI metadata
<p>Diagram showing how it can be possible to access a digital resource that a DOI identifies, either by direct download or via a web service, from the DOI's DataCite metadata. </p>
The DOAJ dataset downloaded 28-05-2023
<p>The DOAJ dataset (journalcsv__doaj_20230528_0035_utf8.csv) was downloaded in its publicly available dump in CSV format from 'https://doaj.org/docs/public-data-dump/' on 28/05/2023 (22.9MB).</p> <p>This dataset is related to the team Playarists of the Open Science course a.a. 2022/2023 :</p> <ul> <li>https://github.com/open-sci/2022-2023-playarists-code</li> <li>https://github.com/open-sci/2022-2023/blob/main/docs/Playarists/material.md</li> </ul> <p> </p>
Data and code for EDI overview paper, data collection characteristics, FAIR evaluation, downloads, and citations
The Environmental Data Initiative (EDI) is a trustworthy, stable data repository and data management support organization for the environmental scientist. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, these data and code were used to provide a general description of EDI’s collection of data and its data management philosophy and placement in the repository landscape. They show how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, they provide measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.
Baltimore Ecosystem Study: November 21, 2019 Download of TreeBaltimore data in support of Anderson et al 2022, Ecosphere
Tree Baltimore (treebaltimore.org) hired Davey Tree to conduct a census of all publicly owned trees and tree pits in the city of Baltimore. This census was completed by arborists in 2017-2018, documenting over 192,000 trees and potential tree sites that reflect the public component of Baltimore’s urban forest. Entries in this dataset include trees in parkways (street trees), mown areas of public parks (forest patches excluded), meridian trees, and vacant spaces for tree planting. Data is continuously updated and the current vintage can be found at https://baltimore.maps.arcgis.com/apps/webappviewer/index.html?id=d2cfbbe9a24b4d988de127852e6c26c8.
A set of generated Instagram Data Download Packages (DDPs) to investigate their structure and content
<p><strong>Instagram data-download example dataset</strong></p> <p>In this repository you can find a data-set consisting of 11 personal Instagram archives, or Data-Download Packages (DDPs).</p> <p> </p> <p><strong>How the data was generated</strong></p> <p>These Instagram accounts were all new and generated by a group of researchers who were interested to figure out in detail<br> the structure and variety in structure of these Instagram DDPs. The participants user the Instagram account extensively for approximately a week. The participants also intensively communicated with each other so that the data can be used as an example of a network. </p> <p>The data was primarily generated to evaluate the performance of de-identification software. Therefore, the text in the DDPs particularly contain many randomly chosen (Dutch) first names, phone numbers, e-mail addresses and URLS. In addition, the images in the DDPs contain many faces and text as well. The DDPs contain faces and text (usernames) of third parties. However, only content of so-called `professional accounts' are shared, such as accounts of famous individuals or institutions who self-consciously and actively seek publicity, and these sources are easily publicly available. Furthermore, the DDPs do not contain sensitive personal data of these individuals. </p> <p><br> <strong>Obtaining your Instagram DDP</strong></p> <p>After using the Instagram accounts intensively for approximately a week, the participants requested their personal Instagram DDPs by using the following steps. You can follow these steps yourself if you are interested in your personal Instagram DDP. </p> <p>1. Go to www.instagram.com and log in<br> 2. Click on your profile picture, go to *Settings* and *Privacy and Security*<br> 3. Scroll to *Data download* and click *Request download*<br> 4. Enter your email adress and click *Next*<br> 5. Enter your password and click *Request download*</p> <p>Instagram then delivered the data in a compressed zip folder with the format **username_YYYYMMDD.zip** (i.e., Instagram handle and date of download) to the participant, and the participants shared these DDPs with us.</p> <p> </p> <p><strong>Data cleaning</strong></p> <p>To comply with the Instagram user agreement, participants shared their full name, phone number and e-mail address. In addition, Instagram logged the i.p. addresses the participant used during their active period on Instagram. After colleting the DDPs, we manually replaced such information with random replacements such that the DDps shared here do not contain any personal data of the participants.</p> <p> </p> <p><strong>How this data-set can be used</strong></p> <p>This data-set was generated with the intention to evaluate the performance of the de-identification software. We invite other researchers to use this data-set for example to investigate what type of data can be found in Instagram DDPs or to investigate the structure of Instagram DDPs. The packages can also be used for example data-analyses, although no substantive research questions can be answered using this data as the data does not reflect how research subjects behave `in the wild'. </p> <p><br> <strong>Authors</strong></p> <p>The data collection is executed by Laura Boeschoten, Ruben van den Goorbergh and Daniel Oberski of Utrecht University. For questions, please contact l.boeschoten@uu.nl. </p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The researchers would like to thank everyone who participated in this data-generation project.</p>
SETH predictions for Swiss-Prot (downloaded 13/06/22) and the human proteome (downloaded 08/07/21)
<p>Per residue disorder predictions for proteins of Swiss-Prot (downloaded 13/06/22) and the human proteome (downloaded 08/07/21; (The UniProt et al., 2021)) generated with SETH (<a href="https://github.com/DagmarIlz/SETH">https://github.com/DagmarIlz/SETH</a>). </p> <p>For details on SETH see: Ilzhöfer D, Heinzinger M and Rost B (2022) SETH predicts nuances of residue disorder from protein embeddings. Front. Bioinform. 2:1019597. <a href="https://doi.org/10.3389/fbinf.2022.1019597">https://doi.org/10.3389/fbinf.2022.1019597</a>.</p> <p>For some proteins no ProtT5 (Elnaggar et al., 2021) embeddings could be generated due to their length. Therefore, these proteins are missing from the dataset. All in all, predictions for 567,458 out of 567,483 proteins are available for Swiss-Prot and 20,352 proteins are available for the human proteome.</p>
URL list for downloading training data for 'Maximum Likelihood Phylogeny Reconstruction'' (Galaxy Training Material)
<p>This data is used for Galaxy Training Network (GTN) training 'Maximum Likelihood Phylogeny Reconstruction'. It is a list of Zenodo URL pointers to a dataset of 173 amino acid alignments of orthologs found in chromosome 5 of four strains of S. cerevisiae. Original sequence data (https://zenodo.org/record/6610704) was processed in Galaxy following GTN 'Preparing genomic data for phylogeny reconstruction' training (10.48546/workflowhub.workflow.359.1) to generate alignments of orthologs.</p>
Reverse Beacon Network Download for Eclipse Data at Corvallis, OR (massaged)
<p>The upload contains an Eclipse Excel spreadsheet that is enhanced from a Reverse Beacon Network (RBN) export, and sorted by receiving callsign and Zulu PM time during the eclipse and 15 minutes before and for a period from 8:45 to 12:30, local Corvallis time. The experiment looked at the RBN sites who received CW signals from the callsign WG0R, which was transmitting test messages at 100 Watts using the below station resources.</p> <p>The transmission resources are: Elecraft KX3, KXPA100, and PX3 transceiver, 100 Watt amplifier, and Panadapter, driving a Carolina Windom Antenna at 10 meterrs above ground level, strung at 150 to 330 degrees in two Oak trees.</p> <p> </p>
Figure 1 in Occurrence Download
Figure 1. – Distribution map showing georeferenced location points of Chromogobius britoi (black circle) and the present records of the Galicia specimens (red triangle).
Fig. 5 in Occurrence Download
Fig. 5. Occurrence records of Taiwan whistling thrushes in the Taipei city. The occurrence data are downloaded from the Global Biodiversity Information Facility database (GBIF.org, 15th December 2018, https://doi.org/10.15468/dl.svzckk). The records for the central and non-central areas of the Taipei city from 1996 to 2016 are shown.
Fig. 4 in Occurrence Download
Fig. 4. Simulated distributions of weekly offspring survival rates of barn swallows through Typhoon Maria in Nangang based on 1,000 times of bootstrap resampling. The average offspring survival rates were estimated between July 4th (before Typhoon Maria hit Taiwan) and July 11th (after Typhoon Maria hit Taiwan). (a) The first distribution was generated from nests that contained eggs and/ or chicks of all ages on July 4th. (b) The second distribution was generated from the same nests as above but excluding ones containing chicks> two weeks on July 4th that were potential fledglings leaving nests before July 11th. The black line indicates the average offspring survival rate of five nests predated by a Taiwan whistling thrush between May 30th and June 7th.
The Natural Products Atlas - data download
<p>Download files from the Natural Products Atlas (<a href="https://www.npatlas.org/joomla/">npatlas.org</a>).</p> <p>van Santen, J. A.; Poynton, E. F.; Iskakova, D.; McMann, E.; Alsup, T. A.; Clark, T. N.; Fergusson, C. H.; Fewer, D. P.; Hughes, A. H.; McCadden, C. A.; Parra Villalobos, J.; Soldatou, S.; Rudolf, J. D.; Janssen, E. M.-L.; Duncan, K. R.; Linington, R. G.* "The Natural Products Atlas 2.0: A Database of Microbially-Derived Natural Products", <em>Nucleic Acids Research</em>, <strong>2022</strong>, 50, D1, 11, D1317-D1323. <a href="https://doi.org/10.1093/nar/gkab941">10.1093/nar/gkab941</a></p> <p>van Santen, J. A.; Jacob, G.; Leen Singh, A.; Aniebok, V.; Balunas, M. J.; Bunsko, D.; Carnevale Neto, F.; Castaño-Espriu, L.; Chang, C.; Clark, T. N.; Cleary Little, J. L.; Delgadillo, D. A.; Dorrestein, P. C.; Duncan, K. R.; Egan, J. M.; Galey, M. M.; Haeckl, F. P. J.; Hua, A.; Hughes, A. H.; Iskakova, D.; Khadilkar, A.; Lee, J.-H.; Lee, S.; LeGrow, N.; Liu, D. Y.; Macho, J. M.; McCaughey, C. S.; Medema, M. H.; Neupane, R. P.; O’Donnell, T. J.; Paula, J. S.; Sanchez, L. M.; Shaikh, A. F.; Soldatou, S.; Terlouw, B. R.; Tran, T. A.; Valentine, M.; van der Hooft, J. J. J.; Vo, D. A.; Wang, M.; Wilson, D.; Zink, K. E.; Linington, R. G.* "The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery”, <em>ACS Central Science</em>, <strong>2019</strong>, 5, 11, 1824-1833. <a href="https://doi.org/10.1021/acscentsci.9b00806">10.1021/acscentsci.9b00806</a></p> <p> </p> <p>Now includes ontological data from:</p> <p>NP Classifier - <a href="https://npclassifier.ucsd.edu/">https://npclassifier.ucsd.edu/</a></p> <p>ClassyFire - <a href="http://classyfire.wishartlab.com/">http://classyfire.wishartlab.com/</a></p> <p>Including archived versions, extra data download types, and new MIBiG and GNPS IDs</p> <p>Includes dump of compounds deemed out of scope and removed from DB on May 19, 2021.</p> <p>The latest versions (v2021_08 onward) include the ontological data in the full JSON download.</p> <p>Starting in v2024_09 - compounds with exclusions are included as a separate file but kept in the database for reference.</p>
Signed Citation of Provenance of GBIF Occurrence Downloads referenced in Chesshire et al. 2023 doi:10.1111/ecog.06584 hash://sha256/9e3ca96d94229e20f47c14efaa59f793845aa37d9f6c698d2dd35876705e9feb hash://md5/43652e3d26989008026e092e3f04b04d
<p>Chesshire et al. 2023. scientific publication [1] used and referenced three GBIF mediated occurrence download queries [2,3,4] and associated data. However, in their GBIF records indicate that the data associated with the three download queries are slated for removal at any point after 2021-08-03 . This publication explicitly references the DOIs associated with [2,3,4] and documents the provenance of their associated meta-data records. The provenance was captured using Preston [5,6], a biodiversity dataset tracker. </p> <p>The signed citation of this provenance publication can be derived from:</p> <pre><code class="language-bash">preston history\ --anchor hash://sha256/9e3ca96d94229e20f47c14efaa59f793845aa37d9f6c698d2dd35876705e9feb\ --remote https://zenodo.org/record/7849559/files</code></pre> <pre><code><hash://sha256/f2d8bdaec7a416a0039e9398cf07c6fa69083f64a6f22de3f252ebb5dd4fd412> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/c457565ea0cec7b0392f1271fcda08440919f03bbf29bb8df1eb926c78a972cc> . <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/c457565ea0cec7b0392f1271fcda08440919f03bbf29bb8df1eb926c78a972cc> .</code></pre> <p>And their tracked content include, as obtained via </p> <pre><code class="language-bash">preston alias\ --anchor hash://sha256/9e3ca96d94229e20f47c14efaa59f793845aa37d9f6c698d2dd35876705e9feb\ --remote https://zenodo.org/record/7849559/files</code></pre> <table> <caption>Tracked content associated with hash://sha256/9e3ca96d94229e20f47c14efaa59f793845aa37d9f6c698d2dd35876705e9feb</caption> <tbody> <tr> <td>content location</td> <td>content relation</td> <td>content id</td> </tr> <tr> <td>https://doi.org/10.15468/dl.6cxfsw</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/6b8b5f79af53dee98c3654b945389628194c9e9f0ad610852327574b3f99ff7a</td> </tr> <tr> <td>https://doi.org/10.15468/dl.b9rfa7</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/a74cfe8a6c6b7d2361f41cc04979c262b5fbba60c0992253ae78fe6d31f414bb</td> </tr> <tr> <td>https://doi.org/10.15468/dl.w2nndm</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/6a587a219e78ff2674fbb54d99fed48c21b77bc46608d8f991e79ee06a547fac</td> </tr> <tr> <td>https://api.gbif.org/v1/occurrence/download/0182006-200613084148143</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/1c5d8a7399793a634a0dde32f3a94ccf64199f010d7f93baa422c2e1dbb98b2f</td> </tr> <tr> <td>https://api.gbif.org/v1/occurrence/download/0182032-200613084148143</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/23d7c875420bea71d24c1ec3ba127f91eff5b368744de14824de0fc4fc090bb2</td> </tr> <tr> <td>https://api.gbif.org/v1/occurrence/download/0182076-200613084148143</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/6555d581e0ce75c77740811e547da726297d02369b149893faf531f132a2aff0</td> </tr> <tr> <td>https://api.gbif.org/v1/occurrence/download/0182006-200613084148143</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/2c4c4f4cd1151bc65394466416b066c19422fe22b8eb64c5c144fb7889ea2f16</td> </tr> <tr> <td>https://api.gbif.org/v1/occurrence/download/0182032-200613084148143</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/e4e9742259e9232c773ab157e34af1cfebfd09050effb49c15db032057fc5750</td> </tr> <tr> <td>https://api.gbif.org/v1/occurrence/download/0182076-200613084148143</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/20915d475c63fa6f96ab127ff5efb5554df40208596244349d110432b478168b</td> </tr> <tr> <td>https://api.gbif.org/v1/occurrence/download/request/0182006-200613084148143.zip</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/d14a14e549e3caa8965daecad6fcb0cfddd4be12fb78a495b248c380df41db9b</td> </tr> <tr> <td>https://api.gbif.org/v1/occurrence/download/request/0182032-200613084148143.zip</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/3fc1b6491813f5d7e2d32b7c6cadb1ae60558f31a4489e23735c43bd74ed4db6</td> </tr> <tr> <td>https://api.gbif.org/v1/occurrence/download/request/0182076-200613084148143.zip</td> <td>http://purl.org/pav/hasVersion</td> <td>hash://sha256/7ddea84a67329ec8eea389d09798e5b6d60d86c39b975590f117679cdbbe8e20</td> </tr> </tbody> </table> <p>This data publication, and associated tracked content, can be cloned using:</p> <pre><code class="language-bash">preston clone https://zenodo.org/record/7849559/files</code></pre> <p><br> Note that the original publication dated 2023-04-03 did *not* include the associated tracked data retrieved from https://api.gbif.org/v1/occurrence/download/request/0182006-200613084148143.zip, https://api.gbif.org/v1/occurrence/download/request/0182032-200613084148143.zip, https://api.gbif.org/v1/occurrence/download/request/0182076-200613084148143.zip. However, on 2023-03-17, the data associated with [2], [3], [4] were still marked for deletion in the GBIF ecosystem, two weeks after the respective DOIs were first cited in the v0.1 of this data publication. This 2023-03-17 publication includes tracked content that shows the associated data is marked for deletion, and contains the associated data archives. </p> <p>The example below shows a tracked versions of the metadata associated with the download request/query doi:10.15468/dl.w2nndm [4] indicates that the associated data is scheduled to be "eraseAfter" "2021-08-03T19:18:46.611+00:00". </p> <pre><code class="language-bash">preston cat\ --remote https://zenodo.org/record/7837572/files\ hash://sha256/6555d581e0ce75c77740811e547da726297d02369b149893faf531f132a2aff0\ | jq .</code></pre> <p> </p> <pre><code class="language-json">{ "key": "0182076-200613084148143", "doi": "10.15468/dl.w2nndm", "license": "http://creativecommons.org/licenses/by-nc/4.0/legalcode", "request": { "predicate": { "type": "and", "predicates": [ { "type": "equals", "key": "DATASET_KEY", "value": "e05f6e7d-418e-4407-8e0f-7b8ccf21109e", "matchCase": false }, { "type": "or", "predicates": [ { "type": "equals", "key": "TAXON_KEY", "value": "4334", "matchCase": false }, { "type": "equals", "key": "TAXON_KEY", "value": "4345", "matchCase": false }, { "type": "equals", "key": "TAXON_KEY", "value": "7911", "matchCase": false }, { "type": "equals", "key": "TAXON_KEY", "value": "7908", "matchCase": false }, { "type": "equals", "key": "TAXON_KEY", "value": "7901", "matchCase": false }, { "type": "equals", "key": "TAXON_KEY", "value": "7905", "matchCase": false } ] } ] }, "sendNotification": true, "format": "DWCA", "type": "OCCURRENCE", "verbatimExtensions": [] }, "created": "2021-02-03T19:18:46.687+00:00", "modified": "2021-02-03T19:20:03.899+00:00", "eraseAfter": "2021-08-03T19:18:46.611+00:00", "status": "SUCCEEDED", "downloadLink": "https://api.gbif.org/v1/occurrence/download/request/0182076-200613084148143.zip", "size": 2624689, "totalRecords": 11654, "numberDatasets": 1 }</code></pre> <p>Also, on after (re-)running</p> <pre><code class="language-bash">preston track\ https://doi.org/10.15468/dl.6cxfsw\ https://doi.org/10.15468/dl.b9rfa7\ https://doi.org/10.15468/dl.w2nndm</code></pre> <p>on 2023-04-20, the download record metadata retrieved from https://api.gbif.org/v1/occurrence/download/0182006-200613084148143 and associated with https://doi.org/10.15468/dl.6cxfsw appeared to no longer be marked for deletion, as shown by the difference between a pre-2023-04-20 version (i.e. hash://sha256/1c5d8a7399793a634a0dde32f3a94ccf64199f010d7f93baa422c2e1dbb98b2f) with the newly retrieved response on 2023-04-20 (i.e., hash://sha256/2c4c4f4cd1151bc65394466416b066c19422fe22b8eb64c5c144fb7889ea2f16).</p> <p>The difference is highlighted below using the diff and preston tools via</p> <pre><code class="language-bash">diff\ <(preston cat hash://sha256/2c4c4f4cd1151bc65394466416b066c19422fe22b8eb64c5c144fb7889ea2f16 | jq .)\ <(preston cat hash://sha256/1c5d8a7399793a634a0dde32f3a94ccf64199f010d7f93baa422c2e1dbb98b2f | jq .)</code></pre> <p>yielding:</p> <pre><code class="language-diff">116c116,117 < "modified": "2023-04-18T08:09:09.757+00:00", --- > "modified": "2021-02-03T18:00:50.416+00:00", > "eraseAfter": "2021-08-03T17:50:18.453+00:00",</code></pre> <p> This observation is consistent with the 2023-04-18 claim by Daniel Noesgaard [7] that associated download records are no longer marked for deletion.</p> <p><strong>References </strong></p> <p>[1] Chesshire, P.R., Fischer, E.E., Dowdy, N.J., Griswold, T.L., Hughes, A.C., Orr, M.C., Ascher, J.S., Guzman, L.M., Hung, K.-L.J., Cobb, N.S. and McCabe, L.M. (2023), Completeness analysis for over 3000 United States bee species identifies persistent data gap. Ecography e06584. <a href="https://doi.org/10.1111/ecog.06584">https://doi.org/10.1111/ecog.06584</a></p> <p>[2] GBIF.org (3 February 2021) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.6cxfsw">https://doi.org/10.15468/dl.6cxfsw</a></p> <p>[3] GBIF.org (3 February 2021) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.b9rfa7">https://doi.org/10.15468/dl.b9rfa7</a></p> <p>[4] GBIF.org (3 February 2021) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.w2nndm">https://doi.org/10.15468/dl.w2nndm</a></p> <p>[5] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2020.101132">https://doi.org/10.1016/j.ecoinf.2020.101132</a></p> <p>[6] Elliott, M. J., Poelen, J. H., & Fortes, J. (2022, August 29, accepted with minor revisions). Signed Citations: Making Persistent and Verifiable Citations of Digital Scientific Content. <a href="https://doi.org/10.31222/osf.io/wycjn">https://doi.org/10.31222/osf.io/wycjn</a></p> <p>[7] Noesgaard, D. 2023. https://discourse.gbif.org/t/data-queries-doi-10-15468-dl-6cxfsw-doi-10-15468-dl-b9rfa7-doi-10-15468-dl-w2nndm-used-in-chesshire-et-al-2023-were-cited-but-remain-marked-for-deletion/3915/2 accessed at 2023-04-20 .</p>
Plant FUnctional COnservation DB (Plant FUNCO) Download
<p>Zenodo repository hosting main resources generated in the database.</p>
Cluster expansions in icet format for direct download
<p>This record contains cluster expansions (CEs) in <a href="https://icet.materialsmodeling.org/">icet</a> format from the following three publications</p> <ul> <li><em>High-Throughput Characterization of Transition Metal Dichalcogenide Alloys: Thermodynamic Stability and Electronic Band Alignment</em>, <a href="10.1021/acs.chemmater.2c01176">DOI:10.1021/acs.chemmater.2c01176</a></li> <li><em>Hydrogen-Driven Surface Segregation in Pd Alloys from Atomic-Scale Simulations</em>, <a href="https://doi.org/10.1021/acs.jpcc.1c00575">DOI: 10.1021/acs.jpcc.1c00575</a></li> <li><em>To Every Rule There is an Exception: A Rational Extension of Loewenstein's Rule</em> ,<a href="https://doi.org/10.1002/anie.202013256">DOI: 10.1002/anie.202013256</a></li> </ul> <p>They are compiled here to enable easy access via, e.g., <code>curl</code> or <code>wget</code>.</p>
Estatísticas de downloads e reproduções de episódios de podcast na plataforma PodCloud
<p>O objeto de estudo trata-se de um podcast acadêmico lançado oficialmente no mês de agosto de 2019, como produto de uma dissertação de mestrado defendida em 2018 e posteriormente incorporado à rotina de atividades do setor de atendimento de uma biblioteca universitária. A etapa de testes da produção do podcast data do ano de 2018, na época hospedado na plataforma SoundCloud. No entanto, em 2019, a plataforma PodCloud foi a escolhida para a hospedagem do conteúdo e permitiu a administração do podcast até 2023, quando foi descontinuada no mês de julho deste ano.</p> <p><br> Com base na etnografia na podosfera, a primeira etapa do método deu-se quando planejamos a migração da plataforma de hospedagem de um podcast acadêmico, em virtude da descontinuidade da PodCloud no Brasil. Assim, houve a necessidade de explorar o podcast no intuito de salvaguardarmos as capturas de tela (print screen) do painel de controle (dashboard), o qual continha os temas abordados e as estatísticas de downloads e reproduções na plataforma PodCloud. O arquivo comprova o total de 11.852 downloads e reproduções alcançados pelo podcast em 2023.</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.