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252 results for “platform data”
Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform"
<p>Data and code for "Search Algorithm, Repetitive Information, and Sales on Online Platform".</p> <p>The R code containsboth code for simulation and code for estimation.</p>
Platforms & registries for sharing participant-level COVID-19 data
<p>This dataset presents an overview of platforms and registries that store, harmonize, and, in some cases, share COVID-19-related participant-level data, including clinical-epidemiological, human and pathogen OMICs, and high dimensional imaging data. The dataset provides an in-depth review of adherence to the <a href="https://www.go-fair.org/fair-principles/">FAIR principles</a>, governance, benefit sharing and other ethical concerns related to resources that share harmonized, participant-level data for the research response to COVID-19. Systematic searches were conducted between April 2020 and June 2021 to identify relevant platforms and registries. We applied natural language processing to the CORD-19 dataset in March of 2021 and consulted with COVID-19 focused researchers in Asia, Africa, and Latin America to identify non-English language COVID-19-related data sharing resources.</p>
Hyperlocal Environmental Data with a Mobile Platform
<p>This document provides expanded definitions, units, and more information on the data provided in the .CSV files collected using the City Scanner platform, <a href="http://senseable.mit.edu">MIT Senseable City Lab </a>over the years 2019-2022</p> <p><strong>Data Fields</strong></p> <p><em>time</em>: The time field is in Unix time and can be converted to local time with a simple script or using online tools.</p> <p><em>latitude, longitude</em>: The latitude and longitude readings from the devices are determined via GPS and given in degrees for projected corrdinates.</p> <p><em>pm1, pm25, pm10</em>: The particulate matter (pm) readings from the optical particle counter after calibration. The units for each are instantaneous mass concentrations in μg/m3.</p> <p><em>no2</em>: The nitrogen dioxide (no2) readings from the gas sensors after conversion and calibration. The unit is instantaneous concentrations in parts per billion (ppb).</p> <p><em>bin0-bin23</em>: Twenty-four bins from the OPC containing particle number counts for different sizes, ranging from 0.35 to 40 μm. The bin boundary sizes are as follows from bin0 to bin23: 0.35, 0.46, 0.66, 1.0, 1.3, 1.7, 2.3, 3.0, 4.0, 5.2, 6.5, 8.0, 10.0, 12.0, 14.0, 16.0, 18.0 , 20.0, 22.0, 25.0, 28.0, 31.0, 34.0, 37.0, 40.0</p> <p>temperature: Ambient temperature reading in degrees Celsius.</p> <p>humidity: Ambient relative humidity reading in percentage</p> <p><strong>Overview of Data Collection Studies</strong></p> <table> <caption>Datasets</caption> <thead> <tr> <th scope="col">City</th> <th scope="col">Sensing target pollutants</th> <th scope="col">Sensing fleet</th> <th scope="col">Duration</th> <th scope="col">Files</th> </tr> </thead> <tbody> <tr> <td>New York City (USA)</td> <td>Particulate matter, particle size distribution, NO2</td> <td>Five municipal vehicles</td> <td> <p>Oct. 2020 - Feb. 2021</p> <p>Sep. 2021 - Dec. 2021</p> </td> <td> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot1_PM.csv?download=1">NYC_Pilot1_PM.csv</a> </p> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot2_NO2_Part1.csv?download=1">NYC_Pilot2_NO2_Part1.csv</a></p> <p> </p> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot2_NO2_Part2.csv?download=1">NYC_Pilot2_NO2_Part2.csv</a></p> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot2_PM_Part1.csv?download=1">NYC_Pilot2_PM_Part1.csv</a></p> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot2_PM_Part2.csv?download=1">NYC_Pilot2_PM_Part2.csv</a></p> <p><a href="https://zenodo.org/record/7762000/files/NYC_Pilot2_PM_Part3.csv?download=1">NYC_Pilot2_PM_Part3.csv</a></p> </td> </tr> <tr> <td>Boston (USA)</td> <td>Particulate matter, particle size distribution, NO2</td> <td>One mobile air laboratory</td> <td>Feb. 2022 - Apr. 2022</td> <td><a href="https://zenodo.org/record/7762000/files/Boston_Pilot_PM_NO2.csv?download=1">Boston_Pilot_PM_NO2.csv</a></td> </tr> <tr> <td>Beirut (Lebanon)</td> <td>Particulate matter, particle size distribution</td> <td>Two Taxies </td> <td>Feb. 2022 - Jun. 2022</td> <td><a href="https://zenodo.org/record/7762000/files/Beirut_Pilot_PM.csv?download=1">Beirut_Pilot_PM.csv</a></td> </tr> </tbody> </table> <p> </p>
Data platform (genotyping data set) related to ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 "The role of vitamin D gene polymorphisms and its receptors in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis".
<p><strong>Data platform </strong><strong>(genotyping dataset)</strong> <strong>related to the ERDF postdoctoral project No. </strong><strong>1.1.1.2/VIAA/4/20/718</strong><strong> “</strong><strong>The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis</strong><strong>”.</strong></p> <p><strong>About the project and gathered data:</strong></p> <p>The dataset contains genotyping data on 289 sex-balanced samples (approximately 60% women / 40% men)) were created at the the multiple sclerosis (MS) Clinic of the Latvian Maritime Medical Center (LMMC) in 2011 (disease duration of 1-51 years); the collection was updated within the framework of the ERDF MS project (2017-2020) and replenished during the ERDF postdoctoral project No. 1.1.1.2/VIAA/4/20/718 “The role of vitamin D and its receptor gene polymorphisms in the modulation of intestinal inflammation in patients with relapsing and progressive forms of multiple sclerosis” (2021-2023).</p> <p>For the <strong>Genotyping dataset </strong>relevant information for each patient from the MS disease cohort, referring to proteasomal gene genetic variations (microsatellites and SNPs): (HSMS006 <em>(PSMA6),</em> HSMS602 <em>(FAM177A1),</em> HSMS701 <em>(KIAA0391)</em>, HSMS702 <em>(KIAA0391)</em> HSMS801 <em>(KIAA0391)</em>, rs11543947<em>(PSMB5), </em>rs2277460 (mi110), rs1048990 (mi8)<em> (PSMA6),</em> rs1048990 (mi8)<em> (PSMA6),</em> rs2295826/rs2295827<em>(PSMC6),</em> rs2348071 <em>(PSMA3),</em> rs2071543, rs9357155 <em>(PSMB8),</em> rs17587<em>(PSMB9),</em> rs74421874 <em>(PSMD9); </em>rs9275596 from HLA region; vitamin D-related genes (VDR and GC) polymorphisms: rs2228570, rs1544410, rs7975232, rs731236 (<em>VDR</em>) and rs7041, rs4588 <em>(GC).</em></p>
Snapshot data in Parquet format from ENTSO-E Transparency Platform
<p>This is a snapshot of the following three datasets downloaded from the <a href="https://transparency.entsoe.eu/content/static_content/Static%20content/knowledge%20base/SFTP-Transparency_Docs.html">ENTSO-E Transparency Platform SFTP</a>:</p> <ol> <li>ActualTotalLoad (6.1.A)</li> <li>AggregatedGenerationPerType (16.1.B C)</li> <li>DayAheadPrices (12.1.D)</li> </ol> <p>The data has been downloaded and converted from CSV to Parquet using the package {arrow} in R.</p> <p><strong>WARNING</strong>: as <a href="https://github.com/energy-modelling-toolkit/entsoe-tp-survival-kit">I have explained here</a>, the data in the Transparency Platform changes every day. This means that the data contained in these files may be different from the data currently shown on the website or available via the APIs.</p>
Knowledge Exchange - Data from the survey on Alternative Publishing Platforms
<p>The survey data are available in two formats (.csv and .xlsx). They each contain the answers of 45 respondents to the survey conducted at the start of 2023.</p> <p>The <a href="https://docs.google.com/forms/d/e/1FAIpQLScXVQ_4UqZzPh6tj4lGna_SLA4iD0DF5DGjkYKu4HlVJ9AitA/viewform">survey form is still open</a>.</p>
Data from: An experimental platform for stochastic analyses of single serotonergic fibers in the mouse brain
Open the record for dataset details and reuse information.
Data from: SLICE-MSI: A machine learning interface for system suitability testing of mass spectrometry imaging platforms
Open the record for dataset details and reuse information.
Data to support manuscript: A Comparison of Two ROV Survey Methods Used to Estimate Fish Assemblages and Densities Around a California Oil Platform
This fish dataset was collected at platform Harmony, between Aug 25 and Aug 26, 2018. The ROV fish survey was done with two methods: industry (mimicking an inspection survey conducted for platform structural inspections) and biological method. Due to differences between the two methods in the positioning of the ROV vis a vis the jacket crossbeams, fish densities and diversities were different between the two methods at the platform bottom. This dataset provides fish counts and the relevant survey parameters (transect length and depth).
Simulation Data for Project on Incentivizing Participation in Peer-to-Peer Ride-Sharing Platform
<p>Unlike commercial ridesharing, non-commercial peer-to-peer (P2P) ridesharing has been subject to limited research---although it can promote viable solutions in non-urban communities. This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers. We elevate users' preferences as a first-order concern and introduce novel notions of fairness and stability in P2P ridesharing. We propose algorithms for efficient matching while considering user-centric factors including users' preferred departure time, fairness, and stability.</p> <p>The dataset includes our simulation settings and results. Results suggest that individually rational, fair and stable solutions can be obtained in reasonable computational times, and can improve baseline outcomes based on system-wide efficiency exclusively. </p>
Data for Precursor intensity-based label-free quantification software tools for Galaxy Platform.
<p><strong>Precursor intensity-based label-free quantification software tools for proteomic and multi-omic analysis within the Galaxy Platform.</strong></p> <p> </p> <p><strong>ABRF:</strong> Data was generated through the collaborative work of the ABRF Proteomics Research Group (<a href="https://abrf.org/research-group/proteomics-research-group-prg">https://abrf.org/research-group/proteomics-research-group-prg</a>). See Reference for details: Van Riper, S. et al. ‘<a href="http://cbs.umn.edu/sites/cbs.umn.edu/files/public/downloads/2016_ABRF_PRG_Poster_for_ASMS_20160501.pdf">An ABRF-PRG study: Identification of low abundance proteins in a highly complex protein sample</a>’ at the 64th Annual Conference of American Society of Mass Spectrometry and Allied Topics" at San Antonio, TX."</p> <p><strong>UPS:</strong> <strong>MaxLFQ Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M.<a href="https://www.ncbi.nlm.nih.gov/pubmed/24942700/"> Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ.</a> Mol Cell Proteomics. 2014 Sep;13(9):2513-26. doi: 10.1074/mcp.M113.031591. Epub 2014 Jun 17. PubMed PMID: 24942700; PubMed Central PMCID: PMC4159666;</strong></p> <p><strong>PRIDE #5412; ProteomeXchange repository PXD000279: </strong>ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2014/09/PXD000279</p>
easyMF: A Web Platform for Matrix Factorization-based Biological Discovery from Large-scale Transcriptome Data
<p>With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we present easyMF, a user-friendly web platform that aims to facilitate biological discovery from large-scale transcriptome data through matrix factorization (MF). The easyMF platform enables users with little bioinformatics experience to streamline transcriptome analysis from raw reads to gene expression and to decompose expression matrix from thousands of genes to a handful of metagenes. easyMF also offers a series of functional modules for metagene-based exploratory analysis with an emphasis on functional gene discovery. As a modular, containerized and open-source platform, easyMF can be customized to satisfy users’ specific demands and deployed as a web server for broad applications. easyMF is freely available at https://github.com/cma2015/easyMF. We demonstrated the application of easyMF with four case studies using 940 RNA sequencing datasets from maize (<em>Zea mays </em>L<em>.</em>).</p>
Data from the ENTSO-E Transparency Platform - SQLite files
<p>Data extracted from the ENTSO-E Transparency Platform (transparency.entsoe.eu/).</p> <p> </p> <p>## AggregatedGenerationPerType (UPDATED 5/1/2021)</p> <p>Data in SQLite format. Timestamps (fields Datetime and Submission) are stored as seconds since 1970-1-1.</p> <p>CREATE TABLE gen_data<br> (Datetime INTEGER,<br> Submission INTEGER,<br> Resolution TEXT,<br> Area TEXT,<br> Type TEXT,<br> Generation REAL,<br> Consumption REAL,<br> PRIMARY KEY (Datetime, Area, Type))<br> </p>
Data from: Conodont size, trophic level and the evolution of platform elements.
Conodonts are among the first vertebrates to have evolved mineralized tooth-like structures. Among these, the so-called P1 elements are known to have been used to break down food, and display a wide variety of morphologies. In particular, the repeated independent evolution of platform-like P1 elements have been suggested to correspond to similar functional constraints linked to diet. To test this hypothesis of convergence, we measured size (as element length) for various conodont taxa, and used it as a proxy for trophic level. We then tested the correlation between size and platform presence/absence, both on raw data and in a phylogenetic context. Retaining or excluding the platform traits from the character matrix has limited impact on the resulting phylogeny. Contrary to platform presence/absence, size shows no phylogenetic signal. Using the raw data, size and platform presence appear positively correlated. That correlation however is no longer significant if one corrects for the phylogeny. We conclude that platform presence cannot be explained by an enlargement of the conodont element, be it via a trophic level change or developmental constraints. This suggests that conodonts as a whole, and in particular platform-bearing conodonts, were an ecologically diverse group and that the various known platform types are likely to reflect different, rather than convergent, ecological niches.
Dataset: A continuous open source data collection platform for architectural technical debt assessment
<p>The dataset and replication package of the study "A continuous open source data collection platform for architectural technical debt assessment".</p> <p> </p> <p>Abstract</p> <p>Architectural decisions are the most important source of technical debt. In recent years, researchers spent an increasing amount of effort investigating this specific category of technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.</p> <p> </p> <p>However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.</p> <p>In response to this concern, researchers strive to expand the scope of their study by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.</p> <p> </p> <p>To address this issue, this paper presents our initial attempt at tackling this problem and enabling researchers to study architectural smells at large scale, a well-known indicator of architectural technical debt. Specifically, we introduce a novel approach to data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets using Arcan, a tool for architectural smells detection (or any other tool).</p> <p>Finally, we present the publicly-available dataset resulting from the first three months of execution of the pipeline, that includes over 30,000 analysed commits and releases from over 10,000 open source GitHub projects written in 5 different programming languages and amounting to over a billion of lines of code analysed.</p>
Replication Data for "Inverse-designed low-index-contrast structures on silicon photonics platform for vector-matrix multiplication"
<p>COMSOL files and Python post-processing code.</p>
ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries – Data and Models
<p>This repository contains data and models used in the following paper.</p> <p> </p> <p>Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V., & Zou, J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. In review.</p> <p> </p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/admet_ai">ADMET-AI</a> code, which runs the ADMET-AI web server at <a href="https://admet.ai.greenstonebio.com/">admet.ai.greenstonebio.com</a>.</p> <p> </p> <p>The data.zip file has the following structure.</p> <p>data</p> <p> drugbank: Contains files with drugs from the <a href="https://go.drugbank.com/">DrugBank</a> that have received regulatory approval. drugbank_approved.csv contains the full set of approved drugs along with ADMET-AI predictions, while the other files contain subsets of these molecules used for testing the speed of ADMET prediction tools.</p> <p> tdc_admet_all: Contains the data (.csv files) and RDKit features (.npz files) for all 41 single-task ADMET datasets from the <a href="https://tdcommons.ai/">Therapeutics Data Commons</a> (TDC).</p> <p> tdc_admet_multitask: Contains the data (.csv files) and RDKit features (.npz files) for the two multi-task datasets (one regression and one classification) constructed by combining the tdc_admet_all datasets.</p> <p> tdc_admet_all.csv: A CSV file containing all 41 ADMET datasets from tdc_admet_all. This can be used to easily look up all ADMET properties for a given molecule in the TDC.</p> <p> tdc_admet_group: Contains the data (.csv files) and RDKit features (.npz files) for the 22 TDC ADMET Benchmark Group datasets with five splits per dataset.</p> <p> tdc_admet_group_raw: Contains the raw data (.csv files) used to construct the five splits per dataset in tdc_admet_group.</p> <p> </p> <p>The models.zip file has the following structure. Note that the ADMET-AI website and Python package use the multi-task Chemprop-RDKit models below.</p> <p>models</p> <p> tdc_admet_all: Contains Chemprop and Chemprop-RDKit models trained on all 41 single-task TDC ADMET datasets.</p> <p> tdc_admet_all_multitask: Contains Chemprop and Chemprop-RDKit models trained on the two multi-task TDC ADMET datasets (one regression and one classification).</p> <p> tdc_admet_group: Contains Chemprop and Chemprop-RDKit models trained on the 22 TDC ADMET Benchmark Group datasets.</p>
Shine Dalgarno strength data for manuscript "Occlusion of the anti-Shine-Dalgarno on the 30S platform reduces the efficiency of prfB programmed frameshifting in Flavobacterium johnsoniae"
<p>This data set contains Shine Dalgarno strengths for every gene in 4362 bacterial genomes that were analyzed in the manuscript titled "Analysis of programmed frameshifting during translation of prfB in Flavobacterium johnsoniae". There is one comma separated file per genome. They are split between six zip archives. Each line in these files represents one gene. The columns are the genome ID, the name of the gene, the SD strength in kcal/mol as determined by free_scan, the mock SD strength in kcal/mol as determined by free_scan, the anti Shine Dalgarno sequence identified from the 16S ribosomal RNAs of the organism, the sequence of the SD region that was scanned, and the sequence of the mock SD region that was scanned. The mock SD region is further upstream of the gene and should not contain a true SD sequence and is provided as a control.</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>
Dataset Scooping review digital platforms and data management in Education
<p>Dataset used for a scoping review about digital platforms and data management in education</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.