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6,704 results for “reports”
Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - the United Kingdom (Northern Ireland)
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Croatia
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Ireland
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Sweden
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Norway
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Luxembourg
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Sample based prevalence data complementing the European Union One Health 2022 Zoonoses Report - Finland
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Prevalence data complementing the European Union One Health 2022 Zoonoses Report
<p>This dataset contains monitoring data on zoonoses and zoonotic agents under the Directive 2003/99/EC. This Directive requires Member Sates (MSs) to collect, evaluate and report data on zoonoses and zoonotic agents. MSs can also report monitoring data and information on some other pathogenic microbiological agents in foodstuffs. Relevant EU legislation is: Commission Regulation (EC) No 2073/2005,Commission Regulation (EC) No 1441/2007, Commission Regulation (EU) No 1086/2011, Commission Regulation (EU) No 209/2013, Commission Regulation(EU) No 217/2014.</p>
Food and waterborne outbreaks data complementing the European Union One Health 2022 Zoonoses Report
<p>Food and waterborne outbreaks data reported under the framework of Directive 2003/99/EC and in accordance with the update of the technical specifications for harmonised reporting of FBOs through the EU reporting system in accordance with Directive 2003/99/EC. This dataset includes the number of outbreaks, as well as the number of human cases, hospitalisations and deaths, per causative agent. In addition, other information can include data on causative agents, food vehicles, and the factors in food preparation and handling that contributed to the food-borne outbreaks. Reporting countries can also provide information on the nature of the evidence supporting the suspicion of the food vehicle. This evidence can be epidemiological, microbiological, descriptive environmental, or based on product tracing investigations. </p>
Greenland 2022 GHOST Project: Sampling Greenland Geothermal Springs - Expedition Report Data
Data from the GHOST GRL22 Leg 1 Expedition report
Seismic and Hydrostratigraphic Characterization of the Onshore-Offshore Freshwater Systems of Martha's Vineyard and Nantucket, Massachusetts, USA: Field Survey Report
<p>This data archive includes three files: field project report, seisimic data (shot gathers) from Martha's Vineyard, and seismic data (shot gathers) from Nantucket. This work was supported by the National Science Foundation (NSF Award 2052794). Technical support was provided by Geophysical Technology, Inc. (<a href="https://geophysicaltechnology.com/">https://geophysicaltechnology.com/</a>), Exploration Instruments (<a href="https://www.exiusa.com/">https://www.exiusa.com/</a>) , and Seismic Source (<a href="https://seismicsource.com/">https://seismicsource.com/</a>). Field work in Manuel F. Correllus State Forest was conducted with the approval of the Massachusetts Department of Conservation and Recreation under Research Access Permit #R-209. Daniel Wright and Conor Laffey of the Massachusetts Department of Conservation and Recreation provided local logistical support on Martha’s Vineyard. Field work on Nantucket was conducted with the approval of Wannacommet Water Company. Mark Willett of Wannacommet water company provided local logistical support on Nantucket.</p>
Tornado Reports in Southeast South America
<p>This dataset corresponds to <strong>reports of tornadoes</strong> that happened in <strong>Southeast South America (SESA)</strong> between 1991 and 2020. It was constructed and used for studying tornadic environments in SESA, work that was recently published in the American Meteorological Society (AMS) journal <em>Monthly Weather Review</em> under the title: <strong>"Tornadoes in Southeast South America: Mesoscale to Planetary-scale Environments". </strong>A PDF containing this article was included with the last update of this publication (January 2024). Additionally, a datasheet explaining everything you need to know about the database of tornadoes in Southeast South America was included in this new version (January 2024).</p>
Code and Dataset used for assessment of key variables in manuscript titled "Equity Assessment of Global Mitigation Pathways in the IPCC Sixth Assessment Report".
<p>This repository contains the code used for extraction of data from the IPCC scenarios database for key variables that are assessed in the manuscript titled "Equity Assessment of Global Mitigation Pathways in the IPCC Sixth Assessment Report". It also contains data for key variables for scenario categories C1, C2, C3, and C4</p>
IPBES Invasive Alien Species Assessment: Chapter 2. Figures, tables, captions and data management reports
<p>This folder contains the figures and tables included in Chapter 2 of the IPBES Invasive Alien Species and their Control Assessment Report. Each figure is provided in pdf and svg format. In addition, the R scripts and the data sets required to generate the figures are provided as well.</p><p>The figures and tables showing information about alien species numbers or distributions are all based on two data sets, which are stored on Zenodo folders. One data set contains the records of alien species per region worldwide (https://doi.org/10.5281/zenodo.7554428) and the workflow including R scripts have been published (https://doi.org/10.3897/neobiota.59.53578). This data set is called the 'chapter database'. Version 2.4.1 of this data set was used to extract the numbers shown in figures and tables of this chapter. The second data set (https://doi.org/10.5281/zenodo.6458083) contains coordinates of alien species occurrences worldwide and the workflow including R scripts have also been published elsewhere (https://doi.org/10.3897/neobiota.74.81082). Version 1.0.1 of this data set was used here.</p><p>The folder also contains the data management reports for the generation of the chapter database and figures.</p>
Supplementary materials of the article "First report of plastic biomedia contamination in Brazilian beaches – evidence from the Paraná coast"
<p>Supplementary materials of the article "First report of plastic biomedia contamination in Brazilian beaches – evidence from the Paraná coast", published in Ocean and Coastal Research.</p>
Data Package for "A Platform-Agnostic Approach for Automatically Identifying Real-Life Performance Issue Reports with Heuristic Linguistic Patterns"
<p>This Zenodo repository contains the data supporting the findings of the journal paper, titled "A Platform-Agnostic Approach for Automatically Identifying Real-Life Performance Issue Reports with Heuristic Linguistic Patterns", published on IEEE Transactions on Software Engineering, including:</p> <ol> <li><strong>Heuristic Linguistic Pattern Set</strong>: <span>we listed the 80 HLP we derived from </span><span>Apache's JIRA issue tracking system</span><span>. Column "</span><span>Category" </span><span>lists the type of each pattern. Namely, LEX represents lexical pattern, STR represents structural pattern, SEM represents semantic pattern, and PRF represents profiling pattern. Column "Name" is a descriptive name we give to each pattern. Column "Definition" defines the detailed content in each pattern.</span></li> <li><strong>Manual Tagging Results</strong>: manual_tagging.xlsx spreadsheet <span>comprises both sentence-level and issue-level manually tagging results for three datasets: 'Dataset-1: Apache Jira's Homologous Evaluation', '</span><span>Dataset-</span><span>2: Apache Jira's Heterologous Evaluation', and '</span><span>Dataset-</span><span>3: Other Platform's Evaluation'. The tagging results are segmented into sentence-level tabs ("Dataset-1 Sen", "Dataset-2 Sen", "Dataset-3 Sen") and issue-level tabs ("Dataset-1 Issue", "Dataset-2 Issue", "Dataset-3 Issue").</span></li> <li><span><strong>RQ Findings</strong>: </span> <p><span>This section contains detailed data findings from six research questions (RQ1 to RQ6).</span></p> <ul> <li> <p><span>The RQ1 tab provides an evaluation of our HLP-based approach, showing the precision, recall, and F1-Score of eight classifiers. These results are juxtaposed with the corresponding values from baseline methods, at both sentence and issue levels for automatic tagging.</span></p> </li> <li> <p><span>The RQ2 tab illustrates the precision, recall, and F1-Score of eight classifiers under two training conditions: a balanced training dataset (BT+HLP) and an imbalanced training dataset (UBT+HLP). These outcomes are contrasted with the equivalent values from baseline methods, also trained under balanced (BT+BLM) and imbalanced (UBT+BLM) conditions. The results are shown at both sentence and issue levels for automatic tagging.</span></p> </li> <li> <p><span>The RQ3 tab evaluates the dataset transferability of our HLP-based approach in comparison to baseline methods. It achieves this by analyzing the precision, recall, and F1-Score metrics for eight classifiers under two different "training/testing" dataset conditions, i.e., 'D1/D1' and 'D1/D3'. These conditions allow for a direct comparison of performance when applied to the same dataset ('D1/D1') versus when transferred to a different dataset ('D1/D3'). Additionally, the tab includes an 'Avg Change' and 'p-value' section, summarizing the statistical change in performance metrics between the two dataset conditions. </span></p> </li> <li> <p><span>The RQ4 tab presents a direct comparison between strict and fuzzy HLP matching approaches, assessed through precision, recall, and F1-Score metrics across eight issue classifiers.</span></p> </li> <li> <p><span>The RQ5 tab examines the influence of sentence order on the accuracy of eight classifiers within our approach. It shows the change in precision, recall, and F1-Score when the sentence order feature is taken into consideration versus when it is not.</span></p> </li> <li> <p><span>The RQ6 tab explores the impact of feature selection algorithms on both issue and sentence-level tagging accuracy. This tab presents the average precision, recall, and F1-Score for three experiments: Boruta, Recursive Feature Elimination (RFE), and the usage of all 80 features. </span></p> </li> </ul> </li> <li><strong>Qualitative Analysis</strong>: <p><span>This spreadsheet offers a comprehensive examination of the data supporting Section 6.1, which focuses on Qualitative Analysis. It is organized into several tabs, each dedicated to specific research questions (RQs) as outlined below:</span></p> <ul> <li> <p><span>Tab "RQ-1" showcases performance issue reports accurately detected by our High-Level Performance (HLP) approach's top model, XGBoost, which were not identified by the benchmark method's leading model, BERT. This highlights the comparative advantage of our approach in identifying nuanced performance issues.</span></p> </li> <li> <p><span>Tab "RQ-2" continues the exploration of performance issue reports, presenting cases with specific details (to be added).</span></p> </li> <li> <p><span>Tab "RQ-3" delves into the unique capabilities of XGBoost, the leading model in our HLP approach, showcasing its ability to detect performance issues missed by the baseline's top model, BERT. This comparison is drawn under distinct conditions: with pre-training (Dataset 1) and without pre-training (Dataset 3), illustrating the robustness and adaptability of our model.</span></p> </li> <li> <p><span>Tab "RQ-4" focuses on performance issue reports uniquely identified through the implementation of Fuzzy HLP Matching within our HLP approach. This method underscores the innovative matching techniques that enhance issue detection.</span></p> </li> <li> <p><span>Tab "RQ-5" presents performance issue reports pinpointed exclusively by applying the Issue HLP Matrix within our approach. This tab demonstrates the effectiveness of our matrix-based analysis in isolating and identifying specific performance concerns.</span></p> </li> <li> <p><span>Tab "RQ-6" is dedicated to performance issue reports uniquely detected by incorporating feature selection techniques into our HLP approach. This illustrates the value of advanced feature selection in improving the precision of performance issue identification.</span></p> </li> </ul> </li> <li><strong>LLM Experiment Data</strong>: presents the tagging outcomes of Large Language Models (LLMs), specifically ChatGPT-3.5 and ChatGPT-4, across three distinct datasets: 'Dataset-1: Apache Jira's Homologous Evaluation', 'Dataset-2: Apache Jira's Heterologous Evaluation', and 'Dataset-3: Evaluation on Other Platforms'. The results are organized into three separate tabs: 'Dataset-1 Issue', 'Dataset-2 Issue', and 'Dataset-3 Issue'.</li> <li><strong>ChatGPT Operation Python Script</strong>: crafted for automating the evaluation and tagging of issue reports in Excel using Large Language Models (LLMs) like ChatGPT-3.5 and ChatGPT-4. It underscores the importance of administrative rights for file modifications and outlines procedures for reading from and writing responses to Excel files. Key functions include querying LLMs with issue descriptions, processing their responses, and updating the spreadsheet with 'Yes' or 'No' labels and explanatory reasons, thereby facilitating an organized review of LLM performance across different datasets.</li> </ol>
Figure 2. Hentzia antillana Bryant, 1940 in A Caribbean in the South Atlantic: first records of Hentzia antillana Bryant 1940, with notes on other previously reported jumping spider species (Araneae: Salticidae), from Ascension Island
Figure 2. Hentzia antillana Bryant, 1940 adult male (A–B) and female (C–D) from Ascension Island. A, Right male palp, ventral view. B, Same, retrolateral view. C, Epigyne, ventral view. D, Epigyne, dorsal view.
Figure 1. Hentzia antillana Bryant, 1940 in A Caribbean in the South Atlantic: first records of Hentzia antillana Bryant 1940, with notes on other previously reported jumping spider species (Araneae: Salticidae), from Ascension Island
Figure 1. Hentzia antillana Bryant, 1940 adult male (A–C) and female (D–F) from Ascension Island. A, Male, habitus, dorsal. B, Same, ventral. C, Male, frontal view of eyes and chelicerae. D, Female habitus, dorsal. E, Same, ventral. F, Female, frontal view of eyes and chelicerae.
Artifact for "Inside Bug Report Templates: An Empirical Study on Bug Report Templates in Open-Source Software"
<p>This is the artifact for the paper "Inside Bug Report Templates: An Empirical Study on Bug Report Templates in Open-Source Software".</p> <p><strong>What the artifact does:</strong><br>1) a questionnaire that we used for our online survey (PDF);<br>2) the valid responses of our online survey (CSV).</p> <p>3) the code of preprocessing (.py).</p> <p>4) the dataset of preprocessing and labeling (CSV).</p>
Figure 1 in First report of two gobiid fishes from the coral reefs of Gulf of Mannar, Tamil Nadu, India
Figure 1. Map of the Mandapam group of Islands in GoM showing the collection sites.
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.