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548 results for “data classification”
LAGOS-US HUMAN v2: Data module of human population(1990-2020), urbanization classification, and lake access in the conterminous U.S.
The LAGOS-US HUMAN v1 data package is an extension module of the LAGOS-US research platform that includes data characterizing human population (population count, race, ethnicity, socioeconomic information), urbanization, and lake access of 479,950 lakes larger than or equal to 1 ha in the conterminous U.S. (48 states plus the District of Columbia). This data module contains four data tables linked through the unique lake identifier for the LAGOS-US research platform, lagoslakeid. Human population characteristics (race, ethnicity, and socioeconomic factors) were derived from U.S. census data for 1990, 2000, 2010, and 2020. Lakes were classified as urban or not using two different classifications: one based on the ‘Developed’ land category in the National Land Cover Dataset; and another based on the 2020 Census Urban Areas category. Metrics for lake access were developed from national datasets on public boat launches, transportation, and public lands. LAGOS-US HUMAN v1 provides a link between lake data and human contexts, facilitating interdisciplinary research in limnology, urban ecology, environmental justice, and conservation. To facilitate such studies, users are encouraged to use the other three core data modules of the LAGOS-US platform: LOCUS (location, identifiers, and physical characteristics of lakes and their watersheds); GEO (geospatial ecological context at multiple spatial and temporal scales); and LIMNO (in situ lake physical, chemical, and biological measurements through time) that are each found in their own data packages.
Wrack classification data based on UAV imagery from Dean Creek on Sapelo Island, GA
We used a DJI Matrice 210 UAV with a MicaSense Altum to collect a total of 20 images from January 2020 - December 2021 in a the Dean Creek marsh on Sapelo Island, GA. Wrack was classified using a principal component analysis. Wrack patches under 1 m2 were excluded from analyses. Wrack classifications were converted to polygon and point data where each point represents a 5 cm x 5 cm pixel. Those files were then used to analyze wrack characteristics, their relation to environmental drivers, and landscape based patterns. For both polygon and point data, we used the National Elevation Dataset (https://gdg.sc.egov.usda.gov/Catalog/ProductDescription/NED.html) to determine the elevation of each wrack patch. Creeks and shorelines were digitized and used to determine each wrack patches' distance to water. We calculated the frequency of wrack deposition at each point by adding together the number of images where that pixel was classified as wrack over the course of the study. Polygon data were related to tide height from a NOAA tidal station data product (Ft. Pulaski, Station 8670870; https://tidesandcurrents.noaa.gov) and wind speed and wind direction from the Marsh Landing weather station (downloaded data for the SAPMLMET met station from: https://cdmo.baruch.sc.edu/) to evaluate the relationship of wrack to environmental drivers.
Landscape Ecosystem Classification Soils and Vegetation Plots Data at the University of Michigan Biological Station, Pellston, Michigan from 1987 to 2015 remeasurements
Landscape ecosystems are a means of understanding the spatial patterns of and the functional interrelationships in forest ecosystems. Landscape ecosystem research is a multifactor, holistic approach to identifying, classifying, describing, and mapping terrain ecosystems. Abiotic and biotic factors are integrated in the field to distinguish repeating units similar in ecological structure and function. Landscape ecosystems are identified by simultaneous integration of physiographic, soil, and vegetation information. The more stable components--physiography and soil--largely determine local climate, and water and nutrient relations, and thus the interrelationships of physiography and soil form the foundation of a landscape ecosystem classification. Vegetation is seen as a phytometer that integrates the many abiotic factors and their interactions, and therefore reflects differences in ecosystem structure and function. When the three main ecosystem factors are analyzed simultaneously, one can perceive interrelationships that result in ecologically meaningful differences among segments of the ecosphere. Landscape ecosystems are spatial; they are volumetric, multi-dimensional segments of earth, whose components include soil, water, atmosphere, solar radiation, and biota. These segments can be identified, classified, described, and mapped at various scales. From the years of 1988 to 2001, various graduate students of Burton V. Barnes completed their masters thesis and dissertations in this pursuit. The attached data set is a culmination of these individual work. Each plot has measurements at various scales within the 10 by 30 meet plot. A stratified random design was used to locate plot locations. The random design was stratified by major and minor landforms in the region. All trees within the plot where identified and dbh was measured. All individual shrubs where identified and abundance was counted within the entire plot. Soils pits locations for each plot where selected
Data for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>This dataset accompanies a replication package provided for a study on Semantic Versioning for Ansible Galaxy roles.</p> <p>The replication package is available at https://github.com/ROpdebee/ansible_semver_ext_replication</p>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Unblinded Data for PLAsTiCC Classification Challenge
<p>For classification challenge (https://plasticc.org), the unblinded data files are included here. See PDF note above for more information. The original challenge (Sep 28, 2018 - Dec 17, 2018) was hosted at https://www.kaggle.com/c/PLAsTiCC-2018 .</p>
Training data for: CoastSat image classification
<p><strong>CoastSat image classification training data </strong></p> <p>CoastSat is an open-source global shoreline mapping toolbox, available at https://github.com/kvos/CoastSat, which enables users to extract time-series of shoreline change from 30+ years of publicly available satellite imagery (Landsat 5, 7, 8 and Sentinel-2).</p> <p>The automated shoreline extraction relies on a classifier (Multilayer Perceptron from scikit-learn) which labels each pixels on the images with one of four classes: sand, water, white-water and other land features.</p> <p>The data used to train the classifier is stored here, the README.md file provides information on the data organisation and content of each file.</p>
IDLAB-UA Dataset for Traffic Classification using Spectrum Data
<p>This dataset contains IQ values of physical layer (L1) packets associated with WLAN transmission and the set of labels that associated each of the packets to properties/features at different radio stack layer (from L1 to L7). </p>
Data set for (binary) text classification, involving spoken utterances and written text
<p>This data set contains sentences belonging to either of two classes: Transcripts of spoken<br> (informal) text (Class 0), and written, formal text (Class 1). Sentences in Class 0 were<br> obtained from publicly available transcripts of radio shows (e.g. NPR),<br> whereas Sentences in Class 1 were obtained from Wikipedia.</p> <p>The data set is divided into three subsets: Training, validation, and test (specified by the file names).<br> Each set contains a large number of sentences, belonging to either of the two classes:</p> <p>In total, there are 13,640,458 sentences, of which 6,374,487 in Class 0 and 7,265,971.<br> The training set contains 9.743,188 sentences (of which 4,553,205 in Class 0 and 5,189,983 in Class 1), <br> the validation set contains 1,948,639 sentences (of which 910,641 in Class 0 and 1,037,998 in Class 1), and the <br> test set contains 1,948,631 sentences (of which 910,641 in Class0 and 1,037,990 in Class1). </p> <p>The data sets are in plain text format. Every row contains (i) the class label (0 or 1) and<br> (ii) the text of the sentence, separated from the class label by a tab character.</p> <p>Note that the sentences contain 5 tokens or more (including punctuation marks). </p>
Datasets and results from: "Random Forest Classification and Solar Flares Data: Analysis and Validation"
<p><strong>Instructions for the data and code repository</strong></p> <p>Results, post-processing workflow, and datasets for the research paper titled "Random Forest Classification and Solar Flares Data: Analysis and Validation".</p> <p>The folder contains three .csv files: the complete dataset (dataset.csv), the balanced training dataset (train_dataset.csv), and the testing dataset (test_dataset.csv).</p> <p>The folder also contains the result files from the research (.csv output files with predictions and .html files with evaluation metrics, etc.) exported from the JASP software. The number in each file name corresponds to the number of trees utilized in Random Forest modelling.</p> <p>In addition, the Python script for the post-processing workflow is provided, with comments located in the script.</p> <p>The soft range X-ray irradiance and VLF amplitude data were obtained from:<br> National Centers for Environmental Information (NCEI) Available online: https://www.ncei.noaa.gov/. Accessed on: 24th June 2023. <br> Worldwide archive of low-frequency data and observations (WALDO) Available online: https://waldo.world/. Accessed on: 24th June 2023.</p>
Fast MLE and Supervised Classification for the Beta-Liouville Multinomial -- Gold Standard Data
<p>Gold standard datasets used in the publication Fast Maximum Likelihood Estimation and Supervised Classification for the Beta-Liouville Multinomial. Datasets were prepared by Cardoso-Cachopo (2007).</p>
Data for "Systematic Mapping of Open Data Studies: Classification and Trends from a Technological Perspective"
<p>Data used to perform a systematic mapping to classify and analyse existing research on open data from a technological viewpoint from 2006 to 2019. This dataset contains information from six key facets from the collected publications coming from several scientific repositories/databases: publication venue, impact, subject, domain, life-cycle and research type.</p>
Data release for "OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks"
<p><strong>Abstract</strong></p> <p>Taxonomic expertise for the identification of species is rare and costly. On-going advances in computer vision and machine learning have led to the development of numerous semi- and fully automated species identification systems. However, these systems are rarely agnostic to specific morphology, rarely can perform taxonomic “approximation” (by which we mean partial identification at least to higher taxonomic level if not to species), and frequently rely on costly scientific imaging technologies.</p> <p>We present a generic, hierarchical identification system for automated taxonomic approximation of organisms from images. We assessed the effectiveness of this system using photographs of slipper orchids (Cypripedioideae), for which we implemented image pre-processing, segmentation, and colour and shape feature extraction algorithms to obtain digital phenotypes for 116 species. The identification system trained on these digital phenotypes uses a nested hierarchy of artificial neural networks for pattern recognition and automated classification that mirrors the Linnean taxonomy, such that user-submitted photos can be assigned a genus, section, and species classification by traversing this hierarchy.</p> <p>Performance of the identification system varied depending on photo quality, number of species included for training, and desired taxonomic level for identification. High quality photos were scarce for some taxa and were under-represented in the training set, resulting in imbalanced network training. The image features used for training were sufficient to reliably identify photos to the correct genus but less so to the correct section and species.</p> <p>The outcomes of this project include a library of feature extraction algorithms called <em>ImgPheno</em>, a collection of scripts for neural network training called <em>NBClassify</em>, a library for evolutionary optimization of artificial neural network construction called <em>AI::FANN::Evolving</em> and a planned web application called <em>OrchID</em> for identification of user-submitted images. All project outcomes are open source and freely available.</p> <p><strong>About this release</strong></p> <p>This release corresponds belongs with our response to the reviewers of PLoS One. At this stage of the review cycle the manuscript is assessed as 'minor revision'. Consequently, we don't anticipate making more releases until publication.</p>
Raw data for the article "Games on Climate Change: Identifying Development Potentials through Advanced Classification and Game Characteristics Mapping"
<p>Raw data used for the article "Gerber, Andreas, Markus Ulrich, Flurin X. Wäger, Marta Roca-Puigròs, João S.V. Gonçalves, and Patrick Wäger. 2021. "Games on Climate Change: Identifying Development Potentials through Advanced Classification and Game Characteristics Mapping" <em>Sustainability</em> 13, no. 4: 1997. <a href="https://doi.org/10.3390/su13041997">https://doi.org/10.3390/su13041997</a>"</p> <p>The documents include the raw data (both as .csv and .xlsx files with the same content), as well as the publication (.pdf file). The data collection process and the data itself are described in the publication. The data is published as "supplementary material" on the publisher's homepage.</p>
Social Innovations for Circularity in the Built Environment – a Scoping Review and Classification. Supplementary Data to the Bibliometric Review.
<p>To transition the built environment (BE) towards circularity, i.e., maximizing the time resources spend in the BE, thus minimizing negative environmental impacts of resource usage, social innovations (SI) – understood as new ways of doing, organizing, framing, and knowing – are just as important as technological advancements. This article provides an overview of the state of the knowledge regarding SI that contribute to circularity in the BE, and proposes a framework for coherently classifying such SI in terms of their main categories and effects.</p> <p>To identify and understand the current knowledge regarding social innovations (SI) that contribute to circularity in the built environment (BE), a bibliometric review of scientific literature is conducted. It shows that the term social innovation is not frequently used in this contexts although the buzzwords circularity and circular economy are themselves often framed as SI. To assess the characteristics and contribution of SI to circularity in the BE, a scoping review that includes grey literature into the context was conducted and a framework developed to classify predominant SI using concepts from transition studies as well as the systems thinking approach. The framework is designed to help assess the potential of SI and to identify research and/or action gaps. Key findings are (1) There is a broad diversity of SIs that contribute to circularity in the BE already in the focus of research, although they are not always identified as such; (2) Most SI focus on either the design or the demolition phase (i.e., market related phases), whereas user-centered SI are less frequently discussed; and (3) It is crucial to also consider potential sustainability goal conflicts in order to guide policies that address SI as a solution.</p> <p>These datasets are the basis to the bibliometric literature review.</p>
Method Classification of Open Access INTACT Molecular Interaction data.
<p>Simple classification data derived from open access papers indexed in the INTACT database (https://www.ebi.ac.uk/intact/downloads) based on PSI-MI25 codes for interaction detection methods or participant detection methods based on the subfigure caption text. <br> <br> intact_records_and_captions_complete.tsv - This file links available text of subfigure captions to PSI-MI25 codes for the interaction detection method and participant detection method. </p> <p>evidx_run_file.txt - This file provides execution codes for the 'EvidX' machine learning text classifier (https://github.com/SciKnowEngine/evidX/releases/tag/v0.1.0)</p> <p> </p> <p> </p> <p> </p>
Data and material for: "Content classification of development emails"
<p>This data and material support the paper "Content classification of development emails" published in the proceedings of the 34th International Conference on Software Engineering (ICSE 2012).</p> <p>Every software system has a history. <strong>We find traces of a system's history in software repositories</strong>, which are used by developers when building and maintaining their systems. Each repository tells us a part of the history, from its perspective: Issue repositories murmur dark events involving defective and flawed entities; versioning system repositories narrate about restless artifacts and classes that nobody would ever touch; mailing list archives report of unexpected stories on developers’ interactions and opinions.</p> <p>But... <strong>can we seriously trust these repositories?</strong> Can we just listen to what they tell us and behave accordingly? Many wise researchers warmly warned us about the risks of showing such a naive faith in data repositories: Versioning system repositories might seduce us with enchanting stories of always changing entities, but in reality many of these entities may just modify their make up and maintain their old behaviour; or issue repositories might tell us a partial truth about certain very special entities, or developers. We do agree with these researchers: Especially <strong>natural language documents contain information in different languages, surrounded by much noise</strong>. We must pay a special attention when using them.</p> <p>We created <strong>MUCCA</strong>, a classification method to use when dealing with natural language documents. It recognizes source code fragments, patches, stack traces, noise, and natural language with significantly high accuracy. In this way, it allows one to subsequently apply ad hoc analysis techniques to exploit the peculiarities of each category, and extract reliable information.</p> <p>This Zenodo upload supports the paper that describe our work on this topic.</p> <p> </p> <p><strong>1. Source code & Virtual Image</strong></p> <p>MUCCA is written in <a href="http://www.cincomsmalltalk.com/main/products/visualworks/overview/">Cincom VisualWorks Smalltalk</a> and is composed of several components.<br> You can download the source code of the following MUCCA components from this upload (<code>mucca-source_code</code> folder):</p> <ul> <li>Miler2, the core of MUCCA, including metamodels, importers, classification engine, etc.;</li> <li>MailPeek, our web application for the manual classification of email content;</li> <li>PetitIsland, our grammar to generate island parsers;</li> <li>PetitJava, the grammar of Java, which we implemented for PetitParser;</li> <li>PetitSTrace, our island parser for java stack traces.</li> </ul> <p>Note that, in order to make Miler2 run, you will also need the following external Smalltalk components: <a href="http://www.moosetechnology.org/">Moose</a>, <a href="http://www.glorp.org/">Glorp</a>, <a href="http://www.seaside.st/">Seaside</a>, TwoFlower, MetaDB, <a href="http://www.lukas-renggli.ch/blog/petitparser-1">PetitParser</a>.</p> <p>In addition, we make use of the <a href="http://www.cs.waikato.ac.nz/ml/weka/">Weka</a> workbench, for the machine learning tasks. You can download the two trained classifiers that compose MUCCA (<code>mucca-classifiers</code> folder): Naive Bayes based classifier (<code>classifier1-nb.model</code>), Decision Tree based classifier (<code>classifier2-dt.model</code>).</p> <p>Alternatively, we created a <a href="http://www.virtualbox.org/">VirtualBox</a> image with a pre-configured VisualWorks environment, which includes all the MUCCA components, and pre-requisites (both Smalltalk and Java): <code>MUCCA.ova</code> (Both user and password are <code>muccauser</code>).</p> <p> </p> <p><strong>2. Benchmark</strong></p> <p>To train machine-learning classifiers and evaluate the effectiveness of the different approaches, we <strong>manually</strong> create a benchmark, in which emails are classified at character granularity.</p> <p>Given the time and effort needed to create such a benchmark, we humbly think it is a valuable contribution to the community. With the help of this benchmark, other researchers can reproduce our experiments and devise new classification methods, which can be immediately compared to ours.</p> <p>You can download the dataset from the <a href="https://github.com/ilredeitopi/mucca-dataset">GitHub repository</a> (a dump of the GitHub repository is uploaded here (<code>benchmark/githubDump.zip</code>), or download the full database dump in PostgreSQL format (<code>benchmark/benchmarkDump.tar.bz2</code>).</p>
Data for: Tang et al., Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline. bioRxiv 2018.
<p>Datasets containing 63 whole slide images (WSIs) and their segmented 256x256 pixel tiles with approximately 80,000 tile-level amyloid-β pathology expert annotations.</p> <p><strong>Paper</strong>: "Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline", bioRxiv 454793; DOI: <a href="https://doi.org/10.1101/454793">https://doi.org/10.1101/454793</a>.</p> <p><strong>Details:</strong> A total of 63 WSIs for 63 unique decedent cases spanning Alzheimer’s disease (AD) to non-AD and possessing a variety of CERAD scores. WSIs comprise three datasets as follows:</p> <ol> <li><em>Development (Phases I-II)</em>. 33 WSIs used for convolutional neural network (CNN) model development (29 training, 4 validation).</li> <li><em>Hold-out (Phase III)</em>. 10 WSIs selected by an expert neuropathologist as a held-out test set to assess the generalizability of the CNN model.</li> <li><em>CERAD-like hold-out</em>. 20 blinded WSIs collected solely for use in a CERAD-like scoring comparison study.</li> </ol> <p>Datasets 1 and 2 were color-normalized and segmented to 256x256 pixel image tiles for model training set (61,370 images), validation set (8,630 images), and hold-out test set (10,873 images). Dataset 3 was color-normalized but not segmented.</p> <p>Expert labels of plaques for Dataset 1 and 2 tiles are included in corresponding CSV files.</p> <p><strong>Slide source and preparation:</strong> All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 up to 40x magnification.</p> <p><strong>Code:</strong> Please visit <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p> <p> </p>
Data and Analyses for Defining Filler Particles: A Phonetic Account of the Terminology, Form, and Grammatical Classification of Filled Pauses.
<p>Aggregated data and analyses for the article Belz, Malte (2023): Defining Filler Particles: A Phonetic Account of the Terminology,<br> Form, and Grammatical Classification of "Filled Pauses". Languages. <a href="https://www.mdpi.com/journal/languages/special_issues/Pauses_in_Speech">https://www.mdpi.com/journal/languages/special_issues/Pauses_in_Speech</a></p>
New Particle Search at CERN open classification data
<p>This is a reduced, anonymised dataset containing the classifications made in the New Particle Search at CERN demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.</p>
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.