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1,037 results for “large-scale”
Harnessing large-scale herbarium image datasets through representation learning (application images)
<p>Herbarium specimen images used in the manuscript <a href="https://www.frontiersin.org/articles/10.3389/fpls.2021.806407/full"><em>Harnessing large-scale herbarium image datasets through representation learning</em></a> for application tasks 2 and 3. These tasks were formulated to test the applicability of representations learned by three different neural networks trained on <a href="https://www.kaggle.com/c/herbarium-2021-fgvc8">the Half-Earth Challenge dataset</a>.</p> <p>All images are for specimens held by Royal Botanic Gardens, Kew. The images from Kew's HerbCat service, using URLs collated by <a href="https://www.idigbio.org/portal/search">iDigBio</a>. We first search iDigBio for the specimens we wanted, then downloaded the associated occurrence and image metadata files. We then used the URLs in the image metadata files to download the specimen images. The code used to download these images can be found in the <a href="https://doi.org/10.5281/zenodo.5776894">GitHub repository for the paper</a>.</p> <p>We searched iDigBio using these parameters:</p> <pre><code>[x] Must have media Genus: Dendrobium Institution Code: K Basis of Record: PreservedSpecimen</code></pre> <p> </p> <p>©<em> All images copyright of the Board of Trustees of the Royal Botanic Gardens, Kew.</em></p>
Supporting Information: Environmental benefits of large-scale second-generation bioethanol production in the EU: An integrated supply chain network optimization and Life Cycle Assessment approache
<p>This supporting information provides all input data of the model, the assumptions made and the literature and database references for the publication<em> Environmental benefits of large-scale second-generation bioethanol production in the EU: An integrated supply chain network optimization and Life Cycle Assessment approache</em>. The environmental data is based on life cycle assessments, with full information on the life cycle inventory and the results of the life cycle impact assessment based on the ReCiPe method. It also includes detailed results for all objective functions in all scenarios (optimization of 18 midpoints, 3 endpoints, and economic optimization in 5 tax scenarios and 2 feedstock scenarios), and detailed results of the sensitivity analysis and Pareto optimization.</p>
A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set
<p>It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G × E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. In this study, we developed a data-driven approach termed <u>E</u>nvironmental <u>C</u>ovariate Search Affecting <u>G</u>enetic <u>C</u>orrelations (ECGC) to identify environmental stimuli and genes responsible for the G × E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (<i>Glycine max</i>) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G × E interactions in six traits including yield, flowering time, and protein content and when they were involved. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields. This dataset provides the data used in this study and supplementary tables cited in the manuscript.</p>
Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets
<p>This contains data for described in detail in our paper, "Elucidating gene expression patterns across multiple biological contexts through a large-scale investigation of transcriptomic datasets" (Figueiredo <em>et al.</em>, 2022) which aims at revealing common and specific biological processes and mechanisms across contexts by identifying transcriptional patterns that are unique to various cell types, tissues, and cell lines, as well as patterns which are consistent across them.</p>
Data and code from: A large-scale experiment demonstrates line marking reduces power line collision mortality for large terrestrial birds, but not bustards, in the Karoo, South Africa
<p>Line markers are widely used to mitigate bird collisions with power lines, but few studies have robustly tested their efficacy. Power line collisions are an escalating problem for several threatened bird species endemic to southern Africa, so it is critical to know whether or not marking works to adequately manage this problem. Over 8 years, a large-scale experiment was set up on 72 of 117 km of monitored transmission power lines in the eastern Karoo, South Africa, to assess whether line markers reduce bird collision mortality, particularly for Blue Cranes <i>Grus paradisea</i> and Ludwig's Bustards <i>Neotis ludwigii</i>. We tested the two marking devices commonly used in South Africa: bird flappers and static bird flight diverters. Using a before-after-control-impact design, we show that line marking reduced collision rates for Blue Cranes by 92% (95% CI 77-97%) and all large birds by 51% (95% CI 23-68%), but had no effect on bustards. Both marker types appeared similarly effective. Given that monitoring at this site also confirmed high levels of mortality of a range of species of conservation concern, we recommend that marking be widely installed on new power lines. However, other options need to be explored urgently to reduce collision mortality of bustards. Five bustard species were in the top ten list of most frequently found carcasses, and high collision rates of Ludwig's Bustards (0.68 birds·km<sup>-1</sup>·year<sup>-1</sup> uncorrected for survey biases) add to wider concerns about population level effects for this range-restricted and Endangered species. </p> <p>This dataset includes the data and R code for this journal paper.</p>
Additional 1000 notebooks for the paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts"
<p>Additional 1000 notebooks for the review of the paper "A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts".</p> <p>The notebooks can be processed using Matroskin tool from the supplementary materials. To do that, place:</p> <p>- the folder "1k_notebooks_dataset" into the folder ".../databases/datasets"</p> <p>- the file "mapping_of_1k_notebooks.json" into the folder ".../databases/mappings"</p>
Data from: Large-scale evolution of body temperatures in land vertebrates
<p>Body temperature is a crucial variable in animals that affects nearly every aspect of their lives. Here we analyze for the first time large-scale patterns in the evolution of body temperatures across terrestrial vertebrates (tetrapods, including amphibians, mammals, birds and other reptiles). Despite the traditional view that endotherms (birds and mammals) have higher body temperatures than ectotherms, we find they are not significantly different. However, rates of body-temperature evolution are significantly different, with lower rates in endotherms than ectotherms, and the highest rates in amphibians. We find that body temperatures show strong phylogenetic signal over 350 million years of evolutionary history in tetrapods, and some lineages appear to have retained similar body temperatures over time for hundreds of millions of years. Although body temperatures are often unrelated to climate in tetrapods, we find that body temperatures are significantly related to day-night activity patterns. Specifically, body temperatures are generally higher in diurnal species than nocturnal species, both across ectotherms and, surprisingly, across endotherms also. Overall, our results suggest that body temperatures are significantly linked to phylogeny and diel-activity patterns within and among tetrapod groups, rather than just climate and the endotherm-ectotherm divide.</p>
Diet analysis of bats killed at wind turbines suggest large-scale losses of trophic interactions
<p>Agricultural practice has led to landscape simplification and biodiversity decline, yet recently, energy producing infrastructures, such as wind turbines, have been added to these simplified agroecosystems, turning them into multi-functional energy-agroecosystems. Here, we studied the trophic interactions of bats killed at wind turbines using a DNA metabarcoding approach to shed light on how turbine-related bat fatalities may possibly feedback on local habitats. Specifically, we identified insect DNA in the stomachs of common noctule bats (<em>Nyctalus noctula</em>) killed by wind turbines in Germany to infer in which habitats these bats hunted. Common noctule bats consumed a wide variety of insects from different habitats, ranging from aquatic to terrestrial ecosystems (e.g. wetlands, farmland, forests, and grasslands). Agricultural and silvicultural pest insects made up about 20% of insect species consumed by the studied bats. Our study suggests that the potential damage of wind energy production goes beyond the loss of bats and the decline of bat populations. Bat fatalities at wind turbines may lead to the loss of trophic interactions and ecosystem services provided by bats, which may add to the functional simplification and impaired crop production, respectively, in multi-functional ecosystems.</p>
Data related to "Near-bed sediment transport processes during onshore bar migration in large-scale experiments. Comparison with offshore bar migration."
<p>Abstract:</p> <p>This paper presents novel insights into nearshore sediment transport processes during bar migration on the basis of large-scale laboratory experiments with bichromatic wave groups on a relatively steep initial beach slope (1:15). Insights are based on detailed measurements of velocity and sand concentration near the bed from shoaling up to the outer breaking zone including suspended sediment and sheet flow transport. The analysis focuses on onshore migration under an accretive wave condition but comparison to an erosive condition highlights important differences. Decomposition shows that total net transport mainly results from a balance of short wave-related, bedload net onshore transport and current-related, suspended net offshore transport. When comparing the accretive to the more energetic erosive condition, the balance shifts towards net onshore transport, and onshore migration, because the short wave-related transport does not decrease as much as the current-related transport. This is related to the effects of skewness and asymmetry combined with larger sediment entrainment and undertow magnitude under the erosive condition. Net transports from streaming in the wave boundary layer and from infragravity waves are noticeable but only play a subordinate role. Identified priorities for numerical model development include parametrization of wave nonlinearity effects and better description of wave breaking and its influences on sediment suspension. The present data, unique in their combination of high measurement detail with fully-evolving accretive beach profiles, help to improve numerical modeling of long-term morphological evolution.</p> <p>About the data:</p> <p>The folder “Beach Profiles” contains the measurements from the mechanical profiler before and after each test. To save time, only the morphologically active section of the profiles was measured. Additionally, the folder contains the initial profiles at the start of each sequence (after application of the benchmark waves). Here the full profile was measured.</p> <p>The structure “MobFrame” contains the absolute cross-shore position of the mobile frame (from which detailed measurements were taken) in the considered tests.</p> <p>The folder “ACVP” contains structures with ensemble-averaged velocity and concentration measurements in vertical reference to the undisturbed bed level or a few bins below it (zeta<sub>0</sub>-coordinate system as described in the paper). For better interpretation of the measurements, it also features the ensemble-averaged intrawave instantaneous bed elevation (erosion depth) and the upper limit of the sheet flow layer.</p> <p>The folder “ADV” contains structures with the ensemble-averaged ADV data of each test. Apart from the velocity components of each ADV they contain the vertical elevation of each ADV with respect to the ACVP transceiver. The ADV measurements were not subject to the same vertical referencing procedure that was described in the paper for the near-bed ACVP measurements.</p> <p>The folder “OBS” contains structures with the ensemble-averaged OBS data of each test. Apart from the concentration measurements in each OBS sensor they contain the vertical elevation of each OBS with respect to the ACVP transceiver.</p> <p>The folder “ETA” contains structures with the ensemble-averaged surface elevation data of each test (from different instruments as described in the paper). The location of each instrument is given in absolute cross-shore coordinates x.</p> <p> </p> <p>For visualizing the near-bed concentration data, which may not be as trivial as visualizing the rest of the data, an example of MATLAB code is given:</p> <p>%S=ACVP_xx; %to choose which ACVP file you want to look into</p> <p>con=S.c;</p> <p>con(con<1)=1; %to cater for the cells where the logarithm is not defined</p> <p>xphase=linspace(0,1,length(S.solbed)).*ones(size(S.c,2),size(S.c,1));</p> <p>figure; hold on; box on;</p> <p>[C,h]=contourf(xphase,S.z,log10(transpose(con)),[0:0.1:3]);</p> <p>cbh=colorbar; caxis([0 3]);</p> <p>set(h,'edgecolor','none');</p> <p>tt=get(cbh,'Title'); set(tt,'String','$log_{10}(c)$ $[kg/m^3]$','Interpreter','Latex');</p> <p>plot(xphase(1,:),S.solbed,'k','Linewidth',1.5);</p> <p>plot(xphase(1,:),S.solflo,'r','Linewidth',1.5);</p> <p>xlabel('$t/T_r$','Interpreter','Latex')</p> <p>ylabel('$\zeta_0$ $[m]$','Interpreter','Latex')</p> <p>set(gca,'Fontsize',18)</p>
A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, “A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave,” Journal of Data, vol. 7, no. 8, p. 109, Aug. 2022, doi: 10.3390/data7080109</p> <p><strong>Abstract</strong></p> <p>The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations, centered around information seeking and sharing, related to online learning. Mining such conversations, such as Tweets, to develop a dataset can serve as a data resource for interdisciplinary research related to the analysis of interest, views, opinions, perspectives, attitudes, and feedback towards online learning during the current surge of COVID-19 cases caused by the Omicron variant. Therefore this work presents a large-scale public Twitter dataset of conversations about online learning since the first detected case of the COVID-19 Omicron variant in November 2021. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p><strong>Data Description</strong></p> <p>The dataset comprises a total of 52,984 Tweet IDs (that correspond to the same number of Tweets) about online learning that were posted on Twitter from 9th November 2021 to 13th July 2022. The earliest date was selected as 9th November 2021, as the Omicron variant was detected for the first time in a sample that was collected on this date. 13th July 2022 was the most recent date as per the time of data collection and publication of this dataset.</p> <p>The dataset consists of 9 .txt files. An overview of these dataset files along with the number of Tweet IDs and the date range of the associated tweets is as follows. Table 1 shows the list of all the synonyms or terms that were used for the dataset development. </p> <ul> <li>Filename: TweetIDs_November_2021.txt (No. of Tweet IDs: 1283, Date Range of the associated Tweet IDs: November 1, 2021 to November 30, 2021)</li> <li>Filename: TweetIDs_December_2021.txt (No. of Tweet IDs: 10545, Date Range of the associated Tweet IDs: December 1, 2021 to December 31, 2021)</li> <li>Filename: TweetIDs_January_2022.txt (No. of Tweet IDs: 23078, Date Range of the associated Tweet IDs: January 1, 2022 to January 31, 2022)</li> <li>Filename: TweetIDs_February_2022.txt (No. of Tweet IDs: 4751, Date Range of the associated Tweet IDs: February 1, 2022 to February 28, 2022)</li> <li>Filename: TweetIDs_March_2022.txt (No. of Tweet IDs: 3434, Date Range of the associated Tweet IDs: March 1, 2022 to March 31, 2022)</li> <li>Filename: TweetIDs_April_2022.txt (No. of Tweet IDs: 3355, Date Range of the associated Tweet IDs: April 1, 2022 to April 30, 2022)</li> <li>Filename: TweetIDs_May_2022.txt (No. of Tweet IDs: 3120, Date Range of the associated Tweet IDs: May 1, 2022 to May 31, 2022)</li> <li>Filename: TweetIDs_June_2022.txt (No. of Tweet IDs: 2361, Date Range of the associated Tweet IDs: June 1, 2022 to June 30, 2022)</li> <li>Filename: TweetIDs_July_2022.txt (No. of Tweet IDs: 1057, Date Range of the associated Tweet IDs: July 1, 2022 to July 13, 2022)</li> </ul> <p>The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset the Hydrator application (<a href="https://github.com/DocNow/hydrator/releases">link to download</a> and a <a href="https://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets">step-by-step tutorial</a> on how to use Hydrator) may be used.</p> <p><strong>Table 1</strong>. List of commonly used synonyms, terms, and phrases for online learning and COVID-19 that were used for the dataset development</p> <table> <tbody> <tr> <td> <p>Terminology</p> </td> <td> <p>List of synonyms and terms</p> </td> </tr> <tr> <td> <p>COVID-19</p> </td> <td> <p>Omicron, COVID, COVID19, coronavirus, coronaviruspandemic, COVID-19, corona, coronaoutbreak, omicron variant, SARS CoV-2, corona virus</p> </td> </tr> <tr> <td> <p>online learning</p> </td> <td> <p>online education, online learning, remote education, remote learning, e-learning, elearning, distance learning, distance education, virtual learning, virtual education, online teaching, remote teaching, virtual teaching, online class, online classes, remote class, remote classes, distance class, distance classes, virtual class, virtual classes, online course, online courses, remote course, remote courses, distance course, distance courses, virtual course, virtual courses, online school, virtual school, remote school, online college, online university, virtual college, virtual university, remote college, remote university, online lecture, virtual lecture, remote lecture, online lectures, virtual lectures, remote lectures</p> </td> </tr> </tbody> </table> <p> </p>
Large-scale human tissue analysis identifies Uroplakin 3B as a useful diagnostic marker for mesothelioma and normal meso-thelial cells
<p><strong>Supplement Figure 1:</strong> IHC validation by comparison of antibodies. Using MSVA-736M, an apical membranous Upk3b positivity is seen in mesothelial cells covering an appendix (A), amnion cells of a placenta (B), and umbrella cells of the renal pelvis urothelium (C), while staining is absent in adrenal gland (D). Using clone C362, a similar membranous staining is seen in mesothelial cells of the appendix (E), amnion cells (F), and urothelial umbrella cells (G) despite of a higher level of background staining. Clone C362 results in a significant nuclear staining of adrenocortical cells (H) which was not seen by MSVA-736M.</p>
Microbial autotrophy explains large-scale soil CO2 fixation
<p><span>Microbial communities play critical roles in fixing carbon from the atmosphere and fixing it in the soils. However, the large-scale variations and drivers of these microbial communities remain poorly understood. Here, we conducted a </span><span>large-scale</span><span> survey across China and found that soil autotrophic organisms are critical for explaining CO2 fluxes from the atmosphere to soils. In particular, we showed that large-scale variations in CO2 fixation rates are highly correlated to those in autotrophic bacteria and phototrophic protists. Paddy soils, supporting a larger proportion of obligate bacterial and protist autotrophs, display four-fold of CO2 fixation rates over upland and forest soils. Precipitation and pH, together with key ecological clusters of autotrophic microbes, also played important roles in controlling CO2 fixation. Our work provides a novel quantification on the contribution of terrestrial autotrophic microbes to soil CO2 fixation processes at a </span><span>large </span><span>scale, with implications for global carbon regulation under climate change.</span></p>
Scalable mixed model approaches for set-based association studies on large-scale categorical data analysis and its application to 450k exome sequencing data in UK Biobank
<p>The ongoing release of large-scale sequencing data in the UK Biobank allows for identifying associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a novel method for rare-variant association tests, POLMM-GENE, in which a proportional odds logistic mixed model was used to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole exome-sequencing data for 5 ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.</p>
OQM9HK: A Large-scale Graph Dataset for Machine Learning in Materials Science
<p>This is a large-scale graph dataset of materials science based on the Open Quantum Materials Database (OQMD) v1.5 .</p> <p><a href="https://storage.googleapis.com/rimcs_cgnn/oqm9hk_dataset_Sep_30_2022.pdf" target="_blank" rel="noopener">Technical Report</a></p> <p><a href="https://www.rimcs.co.jp" target="_blank" rel="noopener">RIMCS Website</a></p> <p><strong>Data Loading</strong></p> <p>A Python code example:</p> <pre><code>import sys sys.path.append('/your/path/to/data/OQM9HK_BEL') import OQM9HK bel_path='/your/path/to/data/OQM9HK_BEL' config = OQM9HK.load_config(path=bel_path) print(config['atomic_numbers']) split = OQM9HK.load_split(path=bel_path) print(len(split['train']), len(split['val']), len(split['test'])) graph_data = OQM9HK.load_graph_data(path=bel_path) name = next(iter(graph_data)) # Frist entry's name graph = graph_data[name] # Graph object print(graph.nodes) print(graph.edge_sources) print(graph.edge_targets) dataset = OQM9HK.load_targets(path=bel_path) # Pandas dataframe print(dataset) train_set = dataset.iloc[split['train']] val_set = dataset.iloc[split['val']] test_set = dataset.iloc[split['test']]</code></pre> <p> </p>
Machine learning on large-scale proteomics data identifies tissue- and cell type-specific proteins
<p>Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyse the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines</p>
"Software is the easy part of Software Engineering" - Lessons and Experiences from A Large-Scale, Multi-Team Capstone Course
<p>Our data contains the peer review form that students used to assess each member in a sub-team. Additionally, we included the project description for the capstone project. Please note that our domain experts provided additional in person requirements that students elicited for the project. However, this project description can serve as a good starting point for determining the primary project criteria. Finally, we include a sample software requirements specification that we provided for our students to use for their initial requirements specification. </p>
How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed
<p>These datasets provide precipitation data over Central Plateau watershed of Iran. Four different variants of the Inverse Distance Weighting (IDW) method are utilized to create these datasets. Two out of four IDW variants are proposed and developed by the authors to enhance the performance of the available standard models.</p> <p> </p> <p>Data Format: xlsx</p> <p>Spatial Resolution: ~0.08˚ & 0.25˚</p> <p>Spatial Coverage: Central Plateau watershed, Iran (48˚07'E to 61˚25'E - 26˚33'N to 37˚27'N)</p> <p>Temporal Resolution: Monthly</p> <p>Temporal Coverage: 2005 - 2015 (Inclusively)</p> <p> </p> <p><strong>How to cite:</strong></p> <p><strong>Arash Ghomlaghi, Mohsen Nasseri & Bardia Bayat (2022): How to enhance the inverse distance weighting method to detect the precipitation pattern in a large-scale watershed, Hydrological Sciences Journal, DOI: 10.1080/02626667.2022.2124874</strong></p>
Code and data associated with the paper 'Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes'
<p>Code and data associated with the paper 'Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes'.</p> <p>See Readme.pdf for data and code details.</p>
Additional Material for Beyond SonarQube: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture
<p>This upload contains the replication package for the study "Beyond SonarQube: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture" submitted for review on the ECSA 2024 Industry Track.<br><br>It contains the following:<br>Focus group-plan.pdf - The plan for the focus group discussion created by the authors.<br>Focus group slides.pdf - The slides used during the focus group discussion.<br>SonarQube measurement results.xlsx - Detailed results of the SonarQube analysis for each microservice in the researched system.</p>
AlleNoise - large-scale text classification benchmark dataset with real-world label noise
<div> <div> <div> <div> <p><span>AlleNoise</span><span> is a benchmark dataset for large-scale multi-class text classification with real-world label noise. It consists of e-commerce product titles from Allegro.com with corresponding category labels. The noise distribution comes from actual users of a major e-commerce marketplace, so it realistically reflects the semantics of human mistakes. In addition to the noisy labels, we provide human-verified clean labels and a meaningful, hierarchical taxonomy of categories. Code and data is available at https://github.com/allegro/AlleNoise.<br></span></p> </div> </div> </div> </div>
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.