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11,177 results for “identify”
Figure 3 in Aculus taihangensis (Acari: Prostigmata: Eriophyidae), a potential biological control agent identified from the highly invasive pest plant, tree of heaven, in Türkiye
Figure 3. Aculus taihangensis – Deutogyne: A. Coxigenital region, B. Internal genitalia.
Blood DNA Methylation Profiling Identifies Cathepsin Z Dysregulation in Pulmonary Arterial Hypertension
<p>Ulrich, A., Wu, Y., Draisma, H., Wharton, J., Swietlik, E. M., Cebola, I., Vasilaki, E., Balkhiyarova, Z., Jarvelin, M. R., Auvinen, J., Herzig, K. H., Coghlan, J. G., Lordan, J., Church, C., Howard, L. S., Pepke-Zaba, J., Toshner, M., Wort, S. J., Kiely, D. G., Condliffe, R., … Rhodes, C. J. (2024). <a href="https://pubmed.ncbi.nlm.nih.gov/38184627/"><strong>Blood DNA methylation profiling identifies cathepsin Z dysregulation in pulmonary arterial hypertension</strong></a>. <em>Nature communications</em>, <em>15</em>(1), 330. https://doi.org/10.1038/s41467-023-44683-0</p> <p>Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B12 concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.</p>
A matter of scale: Identifying the best spatial and temporal scale of environmental variables to model the distribution of a small cetacean
<p>The importance of scale when investigating ecological patterns and processes is recognised across many species. In marine ecosystems, the processes that drive species distribution have a hierarchical structure over multiple nested spatial and temporal scales. Hence, multi-scale approaches should be considered when developing accurate distribution models to identify key habitats, particularly for populations of conservation concern. Here, we propose a modelling procedure to identify the best spatial and temporal scale for each modelled and remotely sensed oceanographic variable to model harbour porpoise (<em>Phocoena phocoena</em>) distribution. Harbour porpoise sightings were recorded during dedicated line-transect aerial surveys conducted in the summer of 2016, 2021 and 2022 in the Northeast Atlantic. Binary generalised additive models were used to assess the relationships between porpoise presence and oceanographic variables at different spatial (5, 20 and 40 km) and temporal (daily, monthly and across survey period) scales. Selected variables included sea surface temperature, thermal fronts, chlorophyll-a, sea surface height, mixed layer depth and salinity. A total of 30,514 km was covered on-effort with 216 harbour porpoise sightings recorded. Overall, the best spatial scale corresponded to the coarsest resolution considered in this study (40 km), while porpoise presence showed stronger association with oceanographic variables summarised at a longer temporal scale (monthly and averaged over survey period). Habitat models including covariates at coarse spatial and temporal scales may better reflect the processes driving availability and abundance of prey resources at the large scales covered during the surveys. These findings support the hypothesis that a multi-scale approach should be applied when investigating species distribution. Identifying suitable spatial and temporal scale would improve the functional interpretation of the underlying relationships, particularly when studying how a small marine predator interacts with its environment and responds to climate and ecosystem changes. </p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Jessica M. Budke, <a href="https://orcid.org/0000-0003-1149-1522">https://orcid.org/0000-0003-1149-1522</a>. Claims or attributions were made on Bionomia, <a href="http://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
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>
Results from simulated drift trajectories of driting Fish Aggregating Devices used in Figures for paper Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles
<p>Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles<br><span><a href="../doi/10.5281/zenodo.10815559">https://zenodo.org/doi/10.5281/zenodo.10815559</a></span></p> <p>Results data description<br>The above doi contains simulation output data generated from passive drift simulations of drifting FADs in the Pacific ocean. We refer the reader to the main text of the paper for details and definitions of the different zones and simulation experiments.</p> <p>All data files are matrices stored in comma-delimited files (.csv), where the first provides the column names of the matrix, and the first column provides the row names. The first cell in row 1, column 1 is simply a placement value that describes the nature of the matrix. </p> <p>When this value is a hash symbol (#), it denotes that the matrix represents a spatial density of virtual particles under a particular drift scenario and over a particular period of time. Column names provide the latitude indices of each cell, and row names are the longitude indices. Values are the proportion of all particules in the domain that passed through this cell, during the drift-time window of this results file (see below).</p> <p>When the first cell value is a hash followed by a code (e.g. # EqZ), it denotes that the file represents a connectivity matrix between the zones given by the code (e.g. Equatorial Zones EqZ or Fishing Zones FZ) and defined in the row names of the matrix, and the turtle zones defined by the column names. Values are the proportion of particules beginning the zone defined by the row name at the start of the simulation, which are now present in the zone defined by the column name.</p> <p>Files name follow a convention describing the deployment and drift-time scenario they represent.</p> <p>For spatial density matrices:<br>[Origin Zones]_[Subset]_Density_[Drift Time]_....csv</p> <p>For connectivity/transition matrices:<br>[Origin Zones]_[Drift Time]_[Number of Simulations Run]_TransitionMatrix_....csv</p>
Defensins identified through molecular de-extinction
<p>Dataset used in the Article</p> <p>The programs used were Augustus, HMMER and Gromacs.</p> <p>The files are:</p> <p>-Augustusresults (results obtained from augutus)</p> <p>-BirdsTree (the data to create the bird tree)</p> <p>-Commands (the commands used for augustus and hmmer)</p> <p>-Hmmer.Finding (the findings from hmmer)</p> <p>-Hmmer Results (results from the searches)</p> <p>-HmmProfiles (the profiles used to serch in augustus)</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Anne Ronse, <a href="https://orcid.org/0000-0001-7446-9205">https://orcid.org/0000-0001-7446-9205</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Using Radiogenic Noble Gas Nuclides to Identify and Characterize Rock Fracturing - Data
<p>Data and data processing scripts associated with the paper:</p> <p>"<strong>Using Radiogenic Noble Gas Nuclides to Identify and Characterize Rock Fracturing</strong><strong>" <br></strong></p> <p>submitted to Earth and Space Sciences.</p> <p>Data and data processing scripts are given for each shot in a zipped archive. Metadata describing data and dataprocessing scripts for each shot can be found in a README file in each archive.</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by David G. San León, <a href="https://orcid.org/0000-0001-9434-9235">https://orcid.org/0000-0001-9434-9235</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Immune repertoires of de-identified TCGA tumor samples assembled by TRUST4
<p>We applied TRUST4 to assemble the immune repertoire data from TCGA tumor samples. Because TCGA has restricted access permission, the sample IDs are de-identified and the sequence is at the amino acid level. The data is used in the study of "Comprehensive characterizations of immune receptor repertoire in tumors and cancer immunotherapy studies". The format is:</p> <p>CancerType_RandomID Chain_Type CDR3_AminoAcid VGene JGene ConstantGene Abundance</p> <p>(Note that the deidentified RandomID is different from the previous version (version 1))</p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Lucas M. Carbone, <a href="https://orcid.org/0000-0002-2571-5905">https://orcid.org/0000-0002-2571-5905</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Jonathan A. Nations, <a href="https://orcid.org/0000-0002-1170-7656">https://orcid.org/0000-0002-1170-7656</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Dataset: Survey for Identifying User Groups of Active Transport Modes in Urban Centers
<p>This dataset was derived from an online questionnaire conducted between June and July 2022. The survey aimed to identify groups of individuals based on patterns revealed through clustering methods, considering the importance assigned to various indicators associated with the dimensions of comfort, safety and security, and time and distance. <span>CSV file structure details are available at 'csv_structure.xlsx'. </span></p> <p>The questionnaire also included a set of items designed to capture the respondents' sociodemographic context, specifically:</p> <ul> <li>Gender </li> <li>Parental Status</li> <li>Age Group</li> <li>Educational Level</li> <li>Country of Residence </li> <li>Employment Situation </li> </ul>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Katelin D. Pearson, <a href="https://orcid.org/0000-0003-4947-7662">https://orcid.org/0000-0003-4947-7662</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Thomas C. McElrath, <a href="https://orcid.org/0000-0003-0390-4227">https://orcid.org/0000-0003-0390-4227</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters
<p>In Tables 4 and 5, the <strong>Cluster</strong> column represents the name of the cluster. </p> <p><strong>Table 4</strong>: The columns <strong>ra</strong>, <strong>dec</strong>, <strong>pmra</strong>, <strong>pmdec</strong>, and <strong>parallax</strong> correspond to the median values for the cluster's position, parallax, and proper motions, respectively. The <strong>[Fe/H]</strong> and <strong>[Fe/H]_err</strong> columns indicate the cluster's [Fe/H] and its associated error. The <strong>logt</strong> and <strong>log_t_err</strong> columns represent the logarithmic age and its error, while the <strong>m-M</strong> and <strong>m-M_err</strong> columns denote the distance modulus and its error. Additionally, the <strong>E(BP-RP)</strong> and <strong>E(BP-RP)_err</strong> columns specify the cluster's reddening and its error, and the <strong>A_V</strong> and <strong>A_V_err</strong> columns represent the extinction and its error.</p> <p><strong>Table 5</strong>: The <strong>rc_pc</strong> and <strong>e_rc_pc</strong> columns indicate the core radius and its error, while the <strong>rt_pc</strong> and <strong>e_rt_pc</strong> columns represent the tidal radius and its error. The <strong>rh_pc</strong> column provides the radius containing half of the total number of stars in the cluster, and the <strong>rhm_pc</strong> column gives the half-mass radius. The <strong>R_J</strong> and <strong>R_J_err</strong> columns represent the Jacobi radius and its error. The <strong>mass</strong> and <strong>mass_err</strong> columns show the total mass of the cluster and its error, and the <strong>fb</strong> column denotes the binary fraction of the cluster. Finally, the <strong>trlx</strong> and <strong>trlx_err</strong> columns represent the relaxation time and its error. The units of<strong> trlx</strong> and <strong>trlx_err</strong> are Myr</p> <p>Note: NULL values for <strong>rc_pc, e_rc_pc, rt_pc, </strong>and <strong>e_rt_pc </strong>indicate the inapplicability of the RDP method. For <strong>Bootes I, NGC 104, NGC 3201, NGC 6121, NGC 6544, </strong>and <strong>NGC 6656</strong>, the parameters listed as “N/A”—including <strong>rhm, rJ, rJ_err, mass, mass_err, fb, trlx_Myr,</strong> and<strong> trlx_err</strong>—cannot be determined using our methods due to their faint magnitudes. This limitation arises because Gaia’s observational capacity extends only to 21 mag.</p> <p> </p>
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Felix Zareie-Vaux, <a href="https://orcid.org/0000-0002-2882-7996">https://orcid.org/0000-0002-2882-7996</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Matheus Colli-Silva, <a href="https://orcid.org/0000-0001-7130-3920">https://orcid.org/0000-0001-7130-3920</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
Natural history specimens collected and/or identified and deposited.
Natural history specimen data collected and/or identified by Garth Holman, <a href="https://orcid.org/0000-0001-5023-8752">https://orcid.org/0000-0001-5023-8752</a>. Claims or attributions were made on Bionomia, <a href="https://bionomia.net">https://bionomia.net</a> using specimen data from the Global Biodiversity Information Facility, <a href="https://gbif.org">https://gbif.org</a>.
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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.