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39 results for “referencing”
Matching results between landmark in different sources and landmark in a referenced dataset (BDTOPO)
<p>The four datasets represent the results of a two sequentials processus. The first processus consists on a automatic matching between landmark in different sources and landmark in a referenced dataset (french national topographic data: BDTOPO). Then the links 1:1 are manually validated by experts in the second processus.</p> <p>The four different datasets and the BDTOPO dataset are archived <a href="https://doi.org/10.5281/zenodo.6480986">here</a>.</p> <p>The data matching algorithm is described in this <a href="http://dx.doi.org/10.5311/JOSIS.2015.10.194">paper</a>.</p> <p>Each file represents the result matching for features belonging to a data source with:</p> <p>- the name of file depending on the data source</p> <p>- the column "id_source" corresponds to the identifier of the landmark in data source</p> <p>- the column "types_of_matching_results" describes the type of matching result:</p> <ul> <li>« 1:0 »: means that a landmark from a data source (e.g. Camptocamp) has no homologue landmark in BDTOPO</li> <li>« 1:1 validated »: means that a homologous feature exist in BDTOPO and the link was validated</li> <li>« 1:1 non validated »: means that the matching link was not validated</li> <li>« without candidates »: represents the non-matched landmarks because there are no candidates in BDTOPO or because the landmark in data source is far away from its homologous in BDTOPO</li> <li>« uncertain »: uncertainty cases are complex cases where any decision is taken by the data matching algorithm</li> </ul> <p>- the column "id_candidat" corresponds to the identifier of the landmark in BDTOPO if and only if there is a validated matching link</p> <p>- the column "samal" corresponds to the <a href="https://doi.org/10.1080/13658810410001658076">Samal distance</a></p> <p>The matching results are obtained using an ontology application named <a href="http://choucas.ign.fr/doc/ontologies/index-fr.html">OOR</a>. These specific results are obtained using the version of OOR V1.0.1 which is an improved version and contains new concepts compared to the first release 1.0.0. The new version of OOR (i.e. 1.0.1) will be released by the end of May 31 2022. The new link will be added here.</p> <p>This archive is released for transparency and reproducibility purposes.</p>
FRUC multiple sensor forest dataset including absolute, map-referenced localization
<p><strong>FRUC Datasets (Forest environment dataset)</strong></p> <p>This dataset was collected as part of the work conducted by the Forestry Robotics @ University of Coimbra team (<a href="https://www.youtube.com/@forestryroboticsuc">https://www.youtube.com/@forestryroboticsuc</a>; part of the Institute of Systems and Robotics, <a href="https://www.isr.uc.pt/">https://www.isr.uc.pt/</a>) within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-03269; <a href="http://semfire.ingeniarius.pt/">http://semfire.ingeniarius.pt/</a>) and the Semi-Autonomous Robotic System for Forest Cleaning and Fire Prevention (SafeForest, CENTRO-01-0247-FEDER-045931) research projects. Its purpose is to allow researchers in forestry robotics to have an in-depth analysis of a florests environment; obtain an a priori map for robot operations (e.g. path plannning, landscaping, etc…) and to train segmentation algorithms;</p> <p> </p> <p>The dataset in question includes data from multiple sensors and absolute, map-referenced localization which can be used to register the sensor data to a fixed coordinate system. It was collected at the <a href="https://www.google.com/maps/place/Mata+Nacional+do+Choupal/@40.2208522,-8.4429989,842m/data=!3m1!1e3!4m6!3m5!1s0xd22f91d7cec3b95:0xb02aedc4d8380d48!8m2!3d40.2222536!4d-8.4438944!16s%2Fm%2F026jw89?hl=pt-PT">Choupal National Woods, Coimbra, Portugal</a> (40<sup>◦</sup>13′13.3′′N;8<sup>◦</sup>26′38.1′′W). The dataset was collected during a partly clouded day in a forest environment by performing <strong>two circular loop</strong> laps amounting to a total distance of approximately <strong>800m,</strong> with a total duration of <strong>14 minutes and 22 seconds</strong>. The scenario is rich in features relevant to forestry robotics applications, including trees, bushes, tree trunks, etc. To better handle the multimodal nature of the acquired data, the dataset is bundled into <a href="http://wiki.ros.org/rosbag">rosbags</a>, a file format used by the <a href="http://wiki.ros.org/">ROS (Robot Operating System)</a> to record and play back data.</p> <p><strong>More specifically, the datasets include:</strong></p> <ul> <li><strong>RGB Images</strong> from an Intel Realsense D435i</li> <li>Aligned <strong>Depth Images</strong> from an Intel Realsense D435i</li> <li>Left and Right Mono Images from a Mynt Eye s1030</li> <li><strong>Point Clouds</strong> from a Livox Mid-70 LiDAR</li> <li>Unfiltered <strong>acceleration, gyroscopic and magnetic</strong> data from a Xsens MTi IMU</li> <li>Unfiltered <strong>acceleration, gyroscopic </strong>data from an Intel Realsense D435i</li> <li><strong>GNSS Fix data</strong> from a Xiaomi Mi Mix 3 device</li> </ul> <p><strong>Description of files:</strong></p> <ol> <li>The dataset is contain in <strong>choupal.bag</strong>.</li> <li>The <strong>rosbag_info.txt </strong>contains the information of each rosbag;</li> <li>The <strong>sensor_box.urdf </strong>contains all the required transforms;</li> <li>The <strong>sensor_box.stl</strong> contains the 3D model of the apparatus;</li> <li>The <strong>choupal.launch </strong>publishes the sensor transforms and plays the dataset;</li> <li>The <strong>localization.bag</strong> contains the final graph of poses extracted with Cartographer republished as nav_msgs/odom at 4.98Hz.</li> <li>The <strong>localization_15Hz.bag</strong> contains a map-referenced localization extracted with Cartographer at a higher frequency, but the poses are interpolated. If you don't require a high frame rate, please use the <strong>localization.bag</strong> instead.</li> </ol> <p><strong>Usage:</strong></p> <ol> <li>Extract the <em>fruc_dataset_choupal_launch.zip </em>into a catkin workspace</li> <li>Install the necessary dependencies of the package: <ol> <li> <pre><code class="language-bash">cd [/path/to/catkin_ws]</code></pre> <p> </p> </li> <li> <pre><code class="language-bash">rosdep install --from-paths src --ignore-src -y -r</code></pre> </li> </ol> </li> <li>Copy the <strong>rosbags </strong>into the <em>fruc_dataset_choupal_launch/rosbag/</em></li> <li>Edit the <em>fruc_dataset_choupal_launch/launch/choupal.launch </em>file to your use case: <ol> <li>Change the <em>file_path </em>argument if the rosbags are not in the default location;</li> <li>Set <em>localization_file</em> to <em> </em>the path of the desired localization bag, leave it empty to run the dataset without localization.</li> </ol> </li> <li>Compile the package and source the environment: <ol> <li> <pre><code class="language-bash">catkin_make [/your_catkin_workspace/]</code></pre> <p> </p> </li> <li> <pre><code class="language-bash">source [/your_catkin_workspace/devel/setup.bash]</code></pre> <p> </p> </li> </ol> </li> <li>Launch the files: <pre><code class="language-bash">roslaunch fruc_dataset_choupal_launch choupal.launch</code></pre> </li> </ol>
Geo-referencing of journal articles and platform design for spatial query capabilities
<p>We analyzed the corpus of three geoscientific journals to investigate if there are enough locational references in research articles to apply a geographical search method, on the example of New Zealand. We counted place name occurrences that match records from the official Land Information New Zealand (LINZ) gazetteer in the titles, abstracts and full texts of freely available papers of the New Zealand Journal of Geology and Geophysics, the New Zealand Journal of Marine and Freshwater Research, and the Journal of Hydrology, New Zealand, for the years 1958 to 2015. We generated ISO standard compliant metadata records for each article including the spatial references and make them available in a public catalogue service.</p> <ol> <li><em>articles_georef_count_data.xlsx</em>: The counts and evaluation tracking of the place name occurrences in the journal articles.</li> <li><em>summary_final.xlsx</em>: Summary statistics for evaluation based on the counts data.</li> <li><em>article_template.xml</em>: XML template for ISO 19139 compliant metadata record filled for each article.</li> <li><em>full_article.xm</em>l: Exemplary fully filled ISO 19139 compliant metadata record.<br> </li> </ol>
Geo-referenced Harmonized Financial Data on Soil Defense Public Works in Italy
<p>The dataset collects financial data about public works in Italy, specifically, it focuses on soil defense investments. The data is sourced from three distinct platforms: the OpenCoesione website, the OpenBDAP database, the Ministry of Economy and Finance's open data platform, and the ReNDiS database, provided by ISPRA, that exclusively gathers information about interventions in soil defense. The data obtained is interconnected using unique project codes (CUP) to prevent duplication.</p> <p>Georeferencing involves integrating geographic references into the three datasets. It enhances the accuracy of spatial analyses of spatial defense investments and provides valuable context for understanding the geographical distribution of available financial data. By incorporating geographic references such as regions, provinces, and municipalities analysts can gain insights into the spatial patterns and relationships within the datasets. This step is crucial for effective decision-making and policy formulation in the field of soil defense investments.</p> <p>Geographical references for each project were integrated using codes and names of regions, provinces, and municipalities from the ISPRA database. This database retrieves information directly from ISTAT websites, ensuring constant updates to names and codes, thus enhancing the accuracy of spatial analyses.</p> <p>Furthermore, geographical codes facilitated the association of centroids coordinates and polygon shapes for each financial observation, enhancing spatial visualization and analysis of soil defense investments, empowering decision-makers with a deeper understanding of the geographic distribution and impact of these initiatives. This comprehensive approach allows for a deeper exploration of the geographical factors influencing soil defense investments, including identifying hotspots of activity, assessing spatial trends, and understanding the localized impact of interventions on environmental sustainability and community resilience.</p> <p>The zip folder comprises four subfolders and two files. Among the files, one is a text file containing metadata, while the other is a CSV file consolidating merged data at the national level from three repositories. The subfolders contain data categorized by region and data categorized by region sourced from the three distinct repositories.</p> <p> Datasets present 28 variables: </p> <ul> <li>Columns 1-2: descriptive variables;</li> <li>Column 3: total amount financed for each intervention;</li> <li>Columns 4-9: geo-reference variables;</li> <li>Columnn 10:25: key dates of the public works process;</li> <li>Column 26: source of the data;</li> <li>Columns 27-28: geo-referencing (centroids and areal shape).</li> </ul> <p>An additional dataset has been added comprising all Italian municipalities, including thos that lack information on soil defense investments. In such a way, there are geographical information regarding all the peninsula. </p>
Raw and aggregated data for the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors"
<p>This dataset contains all the raw data and aggregated data subject of the study introduced in the article "An analysis of citing and referencing habits across all scholarly disciplines: approaches and trends in bibliographic metadata errors". The study is based on the bibliographic and citation data contained in 729 articles published in 147 journals in 27 subject areas. The articles contained a total amount of 34,140 bibliographic references and 55,100 mentions and quotations overall.</p> <p>The dataset is composed of a series of files:</p> <ul> <li>the files "subject_area_<discipline-name>.csv" contain the raw data of the articles published in the journals of all the disciplines considered in the study;</li> <li>the file "article_data_summary.csv" contains the aggregated data created considering the raw data in the previous files, which have been used to creating all the tables and figures in the article;</li> <li>the file "starred_metadata_set.csv" contains information about the most used subset of bibliographic metadata;</li> <li>the file "journals_selection.csv" contains information about all the journals selected for the study.</li> </ul>
Geo-referenced crop-nutrient response function dataset for Tropical Africa
The profit potential for a given investment in fertilizer use can be estimated using representative crop nutrient response functions. Where response data is scarce, determination of representative response functions can be strengthened by using results from homologous crop growing conditions. Maize (Zea mays L.) nutrient response functions were selected from the Optimization of Fertilizer Recommendations in Africa (OFRA) database of 5500 georeferenced response functions determined from field research conducted in Sub-Saharan Africa. Three methods for defining inference domains for selection of response functions were compared. Use of the OFRA Inference Tool (OFRA-IT; http://agronomy.unl.edu/OFRA) resulted in greater specificity of maize N, P, and K response functions with higher R2 values indicating superiority compared with using the Harvest Choice Agroecological Zones (HC-AEZ) and the recommendation domains of the Global Yield Gap Atlas project (GYGA-RD). The OFRA-IT queries three soil properties in addition to climate-related properties while the latter two options use climate properties only. The OFRA-IT was generally insensitive to changes in criteria ranges of 20–25% used in queries suggesting value in using wider criteria ranges compared with the default for information scarce crop nutrient response functions.
Additional plots referenced in Ghyselen (2016)
<p>The plots here included provide information on the linguistic repertoire structure in Ypres (Belgium) and accompany the chapter of Ghyselen (2016) in <em>The Future of Dialects</em> (eds.: Marie-Hélène Côté, Remco Knooihuizen & John Nerbonne).</p>
Raw data for: Self-Referencing for Quasi Shot-Noise-Limited Widefield Transient Microscopy
<p>The repository shares the data used to produce the figures in the paper "Self-Referencing for Quasi Shot-Noise-Limited Widefield Transient Microscopy", submitted 2024 in Optics Express. </p> <p>The upload includes for each Figure the raw and processed self-referenced transient normalized transmission data, (T_on-T_off) / T_off, from which all results and statistics are deduced.</p>
"@alex, this fixes #9": Analysis of Referencing Patterns in Pull Request Discussions
<p>This publication consists of a dataset of 7k references manually identified in 450 Pull request (PR) discussion threads sampled from GitHub in CSV format. In addition to the dataset, it also contains R code files which were written to analyze this dataset statistically. This dataset is released under the research, which is accepted for publication at CSCW 2021 conference, titled "@alex, this fixes #9": Analysis of Referencing Patterns in Pull Request Discussions".</p> <p><strong>Paper Abstract</strong></p> <p>Pull Requests (PRs) are a frequently used method for proposing changes to source code repositories. When discussing proposed changes in a PR discussion, stakeholders often reference a wide variety of information objects for establishing shared awareness and common ground. Previous work has not considered how referential behavior impacts collaborative software development via PRs. This knowledge gap is the major barrier in evaluating the current support for referencing in PRs and improving them. We conducted an explorative analysis of ~7K references, collected from 450 public PRs on GitHub, and constructed taxonomies of referent types and expressions. Using our annotated dataset, we identified several patterns in the use of references. Referencing source code elements was prevalent but the authoring interface lacks support for it. Three classes of contextual factors influence referencing behaviors: referent type, discussion thread, and project attributes. Referencing patterns may indicate PR outcomes (e.g., merged PRs frequently reference issues, users, and tests). We conclude with design implications to support more effective referencing in PR discussion interfaces.</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>
A self-referenced optical phase noise analyzer for quantum technologies
<p>Raw data used to create plots accompanying the publication. Includes time traces from mixed-domain oscilloscope for COSH analysis as well as pre-processed data directly from commercial phase noise analyzer. Includes README.txt for notes on format and processing.</p>
Geo-referenced crop-nutrient response function dataset for Tropical Africa
Open the record for dataset details and reuse information.
Contextual Documentation Referencing on Stack Overflow — Supplementary Material
<p>Supplementary material for our paper "Contextual Documentation Referencing on Stack Overflow".</p>
"Toy Data Set" referenced in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (https://doi.org/10.1101/2024.08.22.609110)
<p>This data set, referenced as "toy data set" in the article "A deep-learning based analysis framework for ultra-high throughput screening time-series data" (<a href="Lint-to-article">https://doi.org/10.1101/2024.08.22.609110</a>), mimics a high-throughput screening data set. To demonstrate the application of our analysis framework described in the main article this toy data set was generated. It contains in total 1536000 individual transient signals, splitted in 5 batches of each 200 plates in 1536-well plate format. Five distinct signal classes were used to resemble typical shapes encountered in biological experiments. Fequency of occurrences for each class is reported in the main article.</p>
Data from: Kin recognition in guppies uses self-referencing on olfactory cues
<p>Kin recognition plays an important role in social behavior and evolution, but the proximate mechanisms by which individuals recognize kin remain poorly understood. In many species, individuals form a "kin template" that they compare against conspecifics' phenotypes to assess phenotypic similarity–and by association, relatedness. Individuals may form a kin template through self-inspection (i.e. self-referencing) and/or by observing their rearing associates (i.e. family-referencing). However, despite much interest, few empirical studies have successfully disentangled self- and family-referencing. Here, we employ a novel set of breeding crosses using the Trinidadian guppy (<i>Poecilia reticulata</i>)<i> </i>to disentangle referencing systems by manipulating exposure to kin from conception onwards. We show that guppies discriminate among their full- and maternal half-siblings, which can only be explained by self-referencing. Additional behavioral experiments revealed no evidence that guppies incorporate the phenotypes of their broodmates or mother into the kin template. Finally, by manipulating the format of our behavioral tests, we show that olfactory communication is both necessary and sufficient for kin discrimination. These results provide robust evidence that individuals recognize kin by comparing the olfactory phenotypes of conspecifics against their own. This study resolves key questions about the proximate mechanisms underpinning kin recognition, with implications for the ontogeny and evolution of social behavior.</p>
Dataset for documents referenced as part of my research on immigration in the German context
<p>A combination of legal documents, surveys and reports (published by organisations affiliated to the EU or the German state and non-governmental agencies/institutes. These are open access documents downloaded either directly from the owner's website or other online platforms.</p> <p><strong>Declaration: I am not the author or owner of any of the documents uploaded here.</strong></p> <p>They have been uploaded here as part of the Marie Curie grant stipulations to make the research data open access.</p> <p>Documents are in German and English.</p> <p>MSCA Project: RE-NUP: Spousal Reunification and Integration Laws in Europe</p> <p>Grant agreement No. 890826</p>
Supporting data and code for the published paper: Machine Learning Nonadiabatic Dynamics: Eliminating Phase Freedom of Nonadiabatic Couplings with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn–Sham Approach
Open the record for dataset details and reuse information.
An Open Dataset of Scholarly Publications Referenced in Selected Policy Documents (POLIDOC_SCHOLAR)
<p>POLIDOC_SCHOLAR: An Open Dataset of Scholarly Publications Referenced in Selected Policy Documents</p> <p>This repository contains an open dataset of scholarly publications cited by selected policy documents.</p> <p><strong>1. Background:</strong></p> <ul> <li>We do not aim to create a dataset of references for all policy documents or millions of policy documents but rather from a carefully selected set of policy documents.</li> <li>The long-term plan is to facilitate the inclusion of citations of scholarly publications in open bibliometric databases (or at least to create inter-operable datasets).</li> <li>In the short-term, we plan to increase the number of policy documents included in the dataset and continue to monitor and increase the data quality (completeness of records, provided external identifiers).</li> <li>We will also document - in the next release - the reference extraction process (including code used)</li> </ul> <p> </p> <p><strong>2. Structure of the dataset:</strong></p> <p>The dataset is structured into two primary categories: "<strong>Collections</strong>" and "<strong>Collection References</strong>."</p> <p><strong>Collections:</strong></p> <p>The metadata for selected policy documents is included the "<em>collections.jsonl</em>" file.</p> <p>The <strong>collection</strong> is a central feature of the POLIDOC_SCHOLAR dataset. The selected policy documents are listed in the “<em>collections.jsonl</em>”.</p> <p>For instance, a collection might include reports like the IPCC reports of the 6th Cycle (the "IPCC_AR_6 collection") or the reports from IPBES (the "IPBES collection").</p> <p>Within each collection, there are "documents." These can be twofold:</p> <ul> <li>They represent individual reports within a collection (e.g., the IPCC_AR_6 collection contains 6 reports: 3 assessment reports and 3 special reports from the 6th Cycle of the IPCC assessment).</li> <li>They also denote specific sections of these reports that contain bibliographic references. These sections can be chapters or other segments like supplementary materials or annexes (any section which has a reference list). Each document has a unique code, and the relationships between a main document and its subdivisions are indicated in the "is_part_of" field.</li> </ul> <p><strong>Collection References:</strong></p> <p>To allow users to access only the collections they are interested in, we've separated references by collection in files named "<em>collection_reference_{…name of collection…}jsonl</em>."</p> <ul> <li>Each of these files includes bibliographic references for every document in a specific collection.</li> <li>Besides presenting these as "reference strings" (in their original format within the document), we also offer unique identifiers like DOI and OpenAlex ID to facilitate linkage to external databases.</li> </ul> <p>The documentation of the dataset is provided in the file “<em>data_dictionary</em>”</p> <p><strong>3, Content release v1:</strong></p> <p>This release (POLIDOC_SCHOLAR version 1) includes 2 collections:</p> <ol> <li>IPCC Assessment Cycle 6 </li> <li>IPBES Assessment reports</li> </ol> <p> </p> <table> <tbody> <tr> <td> <p> </p> </td> <td> <p>collection</p> </td> <td> <p>Number of reports</p> </td> <td> <p>Number of documents (“sections” with reference)</p> </td> <td> <p>Number of references (strings, not unique)</p> </td> <td> <p>Number of references with DOI (unique)</p> </td> <td> <p>Number of references with DOI (unique)</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>IPCC Assessment Cycle 6</p> </td> <td> <p>6</p> </td> <td> <p>103</p> </td> <td> <p>94,958 </p> </td> <td> <p>51,713</p> </td> <td> <p>48,695</p> <p> </p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>IPBES Assessment reports</p> </td> <td> <p>3</p> </td> <td> <p>27</p> </td> <td> <p>21,750 </p> </td> <td> <p>12,100 </p> </td> <td> <p>11,896</p> </td> </tr> </tbody> </table>
Patient Specific Instrumentation (PSI) Referencing Osteotomy Technological Transfer - a Randomized Control Trial (RCT)
ClinicalTrials.gov study NCT04000672. IPD Sharing: YES. Countries: 1. Publications: 1.
Prospective, Randomized Comparison of the Use of FloShield Air System ® Versus the Referenced Technic (Water + Povidone-Iodine Solution) During Gynecologic Endoscopic Surgery to Evaluate the Operative
ClinicalTrials.gov study NCT02702531. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ScienceDex guides
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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.