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Data for "Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations"
<p>Data repository for "Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations"</p>
Multi-ideology ISIS/Jihadist White Supremacist (MIWS) Dataset for Multi-class Extremism Text Classification
<p>**************Information on how to use our Multi-ideology ISIS/Jihadist White Supremacist Dataset(MIWS)for Multi-class Extremism Text Classification ************</p> <p>Folder name: Seed_MIWS<br> Sub Folder 1 : Seed_Dataset<br> Inside this folder there are two .csv files.<br> 1) ISIS/Jihadist_Seed_Dataset<br> 2) White_Supremacist_Seed_Dataset</p> <p>These files have common features as:<br> ***************************** Common Features in Seed ******************************************************<br> *********Source :- Contains Author, Article Name or Hyperlink to Article************************************<br> *********Type_of_Source :- Whether Source is Research Article or Report or Website**************************<br> *********Text :- Contains Extremist Text provided in Source*************************************************<br> *********Ideology :- Ideology of Text mentioned in Source i.e. ISIS/Jihadist or White Supremacist***********<br> *********Label :- Labels for Text provided by Source i.e. Propaganda, Radicalization or Recruitment*********<br> *********Geographical_Location :- Location mentioned in Text. Geographical Location is manually identified**<br> *********Author_Country_Affiliation :- Country of origin of Research Article, Report or Website in Source***</p> <p>Sub Folder 2 : MIWS<br> Inside this folder ther is one .csv file:</p> <p>It contains features as:<br> *****************************Features in MIWS********************************************************************************<br> **********Tweet_ID :- Unique Identification for a Tweet provided by Twitter**************************************************<br> **********Created_Date :- Date and Time at whic Tweet was created or posted**************************************************<br> **********Geo_Enabled :- Boolean value. True if location is made public by User**********************************************<br> **********Geographical_Location :- Manually extracted list of Locations within the tweet. 'Undefined' if no location present*<br> **********Ideology :- Manually provided during Tweet Collection i.e. ISIS/Jihadist or White Supremacist**********************<br> **********Labels :- Annotated by comparing with Seed, i.e. Propaganda, Radicalization and Recruitment***********************</p> <p>MIWS file can be used to collect tweets and train model for extremism detection.</p>
1847 in Atlas of European millipedes 3: Order Chordeumatida (Class Diplopoda)
1847, according to Enghoff et al. (2015) with recent modifications: 1. Family Anthogonidae Ribaut, 1913 has been reinstated and added to superfamily Anthroleucosomatoidea; it includes Acherosomatidae Verhoeff, 1929 and Biokoviellidae Mršić, 1992 as junior synonyms (Antić et al. 2015a, 2016). 2. Family Dalmatosomatidae Antić & Makarov, 2018, was described as new and placed in Branneroidea by Antić et al. (2018c). 3. Family Hungarosomatidae Ceuca,1974, was reinstated by Mock et al. (2016) but not assigned to a superfamily. Based on the discussion by Mock et al. (2016), it is here placed as Craspedosomatidea incertae sedis. Families covered by the present volume are shown in bold and are followed by the number of known European species
Labeled points for four land cover classes referring to the year 2019
<p>This dataset provides hand-labeled points for four different land cover classes in coastal areas (sand, seawater, grass, trees).</p> <p>It was created based on photointerpretation of high-resolution imagery in Google Earth Pro and QGIS, referring to the year 2019.</p> <p>This dataset was used for the random forest classification of satellite imagery in the following manuscript: </p> <p>"<em>Satellite image processing for the coarse-scale investigation of sandy coastal areas</em>".</p> <p>If you use any part of this dataset, please cite as follows:</p>
APPENDIX 1 in Inventory of Cenozoic radiolarian species (Class Polycystinea) - 1834-2020
APPENDIX 1. — Cenozoic part of the International Chronostratigraphic, showing the subseries/subepoch scheme for the Paleogene and for the Neogene. Despite the wide use in the Cenozoic literature, the subseries/subepochs ranks are not yet officially accepted by the International Commission on Stratigraphy (with the exception for the Holocene and Pleistocene series). Since the beginning of this revision work, we have preferred to use the rank of "subseries" for the ages of the genera in this genera catalogue, as well as for the species in the appendices. We think that there are many solid reasons to keep using the subseries; even though, they have yet to be formally defined. Modified from Head et al. 2017 (see there a good discussion and proposal) and from the ICS International Chronostratigraphic Chart, July 2021 (http://www.stratigraphy.org/ICSchart/ChronostratChart2021-07.pdf). Abbreviations:L/E, Lower/Early;M, Middle;U/L, Upper/Late;e, early;l, late. Bold stages/ages are ratified by the Global Boundary Stratotype Section and Points (GSSP). Italic fonts indicate informal units and placeholders for unnamed units.
Topological states in superlattices of HgTe class of materials for engineering three-dimensional flat bands
<p>In search of materials with three-dimensional flat band dispersions, using ab-initio computations we investigate how topological phases evolve as a function of hydrostatic pressure and uniaxial strain in two types of superlattices: HgTe/CdTe and HgTe/HgSe. In short-period HgTe/CdTe superlattices, our analysis unveils the presence of isoenergetic nodal lines, which could host strain-induced three-dimensional flat bands at the Fermi level without requiring doping, when fabricated, for instance, as core-shell nanowires. In contrast, HgTe/HgSe short-period superlattices are found to harbor a rich phase diagram with a plethora of topological phases. Notably, the unstrained superlattice realizes an ideal Weyl semimetal with Weyl points situated at the Fermi level. A small-gap topological insulator with multiple band inversions can be obtained by tuning the volume: under compressive uniaxial strain, the material transitions sequentially into a Dirac semimetal to a nodal-line semimetal, and finally into a topological insulator with a single band inversion.</p> <p>The provided repository contains data to reproduce the figures of the corresponding article.</p>
Dataset and software to analyze and simulate neuronal morphogenesis in Drosophila class IV da neurons
<p>The highly ramified arbors of neuronal dendrites provide the substrate for the high connectivity and computational power of the brain. Altered dendritic morphology is associated with neuronal diseases. Many molecules have been shown to play crucial roles in shaping and maintaining dendrite morphology. Yet, the underlying principles by which molecular interactions generate branched morphologies are not understood. To elucidate these principles, we visualized the growth of dendrites throughout larval development of Drosophila sensory neurons and discovered that the tips of dendrites undergo dynamic instability, transitioning rapidly and stochastically between growing, shrinking, and paused states. By incorporating these measured dynamics into a novel, agent-based computational model, we showed that the complex and highly variable dendritic morphologies of these cells are a consequence of the stochastic dynamics of their dendrite tips. These principles may generalize to branching of other neuronal cell-types, as well as to branching at the subcellular and tissue levels.</p>
Replication package of `Can We Automatically Generate Class Comments in Pharo?`
<pre><code class="language-markdown"># RP Automatic-comment-generation This folder contains all the material needed to replicate the experiments. ## Content - [RP Automatic-comment-generation](#automatic-comment-generation) - Appendix.pdf - [Content](#content) - [Dataset/](#dataset/) - [Online evaluation/](#online-evaluation/) - [Results/](#results/) - [SI-Approach/](#SI-Approach/) ## Dataset/ This contains the data for the online evaluation - #### Online evaluation/ Contains set of questions used in online evaluation, results of these questions and lists of classes used for each online evaluation - [Class_understanding_questions.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Class_understanding_questions.xlsx) Questions used to determin if a participant understood the functionality of a class. - [Q_and_A_class_comment_characteristics.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_class_comment_characteristics) Questions and possible answers used for the evaluation of the characteristics adequacy, conciseness and comprehensibility of a generated class comment. - [Q_and_A_what_participants_write_and_look_for.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_what_participants_write_and_look_for.xlsx) Questions and possible answers used to determine what participants look for and write in class comments. - [Classes_used_in_evaluation1.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation1.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 1. - [Classes_used_in_evaluation2.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation2.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 2. - [Classes_used_in_evaluation3.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation3.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 3. - [Classes_used_in_evaluation4.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation4.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 4. - [How_often_participants_write_comments.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\How_often_participants_write_comments.xlsx) Results for distribution of how often participants claim to write comments. - [How_participants_follow_class_comment_template.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\How_participants_follow_class_comment_template.xlsx) Results for distribution of how participants follow the class comment template. - [What_participants_look_for_in_comments.xlsx](RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_look_for_in_comments.xlsx) Results of evaluation for what participants look for in class comments. - [What_participants_write_in_comments.xlsx](\RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_write_in_comments.xlsx) Results of evaluation for what participants write in class comments. ## Results/ - #### SI-Approach/ Contains all the data for the results related to SI-Approach and the scripts used to generate said data. - [Class_stereotype_distribution.xlsx](RP-Automatic-comment-generation\Results\SI-Approach\Class_stereotype_distribution.xlsx) Extracted class stereotypes for 350 random classes in the Pharo base image. Evaluated in 10 classes per step. - [Class_stereotype_script.txt](RP-Automatic-comment-generation\Results\SI-Approach\Class_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many classes get assigned the specific class stereotypes. Returns numbers for the class stereotypes in alphabetical order. Boundary => Small - [Method_stereotype_dstribution.xlsx](RP-Automatic-comment-generation\Results\SI-Approach\Method_stereotype_distribution.xlsx) Extracted method stereotypes for 500 random classes in the Pharo base image. Evaluated in 20 classes per step. - [Method_stereotype_script.txt](RP-Automatic-comment-generation\Results\SI-Approach\Method_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many methods get assigned the specific method stereotypes. Returns numbers for the method stereotypes in the order Accessors, Getters, Mutators, Setters, Collaborators, Controllers, Factories and Degenerate. </code></pre> <p>Replication package of 'Can We Automatically Generate Class Comments in Pharo?'</p> <pre><code class="language-markdown"># RP Automatic-comment-generation This folder contains all the material needed to replicate the experiments. ## Content - [RP Automatic-comment-generation](#automatic-comment-generation) - [Content](#content) - [Dataset/](#dataset/) - [Online evaluation/](#online-evaluation/) - [Results/](#results/) - [SI-Approach/](#SI-Approach/) ## Dataset/ This contains the data for the online evaluation - #### Online evaluation/ Contains set of questions used in online evaluation, results of these questions and lists of classes used for each online evaluation - [Class_understanding_questions.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Class_understanding_questions.xlsx) Questions used to determin if a participant understood the functionality of a class. - [Q_and_A_class_comment_characteristics.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_class_comment_characteristics) Questions and possible answers used for the evaluation of the characteristics adequacy, conciseness and comprehensibility of a generated class comment. - [Q_and_A_what_participants_write_and_look_for.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Q_and_A_what_participants_write_and_look_for.xlsx) Questions and possible answers used to determine what participants look for and write in class comments. - [Classes_used_in_evaluation1.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation1.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 1. - [Classes_used_in_evaluation2.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation2.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 2. - [Classes_used_in_evaluation3.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation3.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 3. - [Classes_used_in_evaluation4.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\Classes_used_in_evaluation4.xlsx) Extracted classes with information to LOC, instance variables, number of methods, number of classes using it and class stereotypes for evaluation 4. - [How_often_participants_write_comments.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\How_often_participants_write_comments.xlsx) Results for distribution of how often participants claim to write comments. - [How_participants_follow_class_comment_template.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\How_participants_follow_class_comment_template.xlsx) Results for distribution of how participants follow the class comment template. - [What_participants_look_for_in_comments.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_look_for_in_comments.xlsx) Results of evaluation for what participants look for in class comments. - [What_participants_write_in_comments.xlsx](thesis\RP-Automatic-comment-generation\Dataset\Online_evaluation\What_participants_write_in_comments.xlsx) Results of evaluation for what participants write in class comments. ## Results/ - #### SI-Approach/ Contains all the data for the results related to SI-Approach and the scripts used to generate said data. - [Class_stereotype_distribution.xlsx](thesis\RP-Automatic-comment-generation\Results\RQ2\Class_stereotype_distribution.xlsx) Extracted class stereotypes for 350 random classes in the Pharo base image. Evaluated in 10 classes per step. - [Class_stereotype_script.txt](thesis\RP-Automatic-comment-generation\Results\RQ2\Class_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many classes get assigned the specific class stereotypes. Returns numbers for the class stereotypes in alphabetical order. Boundary => Small - [Method_stereotype_dstribution.xlsx](thesis\RP-Automatic-comment-generation\Results\RQ2\Method_stereotype_distribution.xlsx) Extracted method stereotypes for 500 random classes in the Pharo base image. Evaluated in 20 classes per step. - [Method_stereotype_script.txt](thesis\RP-Automatic-comment-generation\Results\RQ2\Method_stereotype_script.txt) Script used in the Playground of Pharo to return counts of how many methods get assigned the specific method stereotypes. Returns numbers for the method stereotypes in the order Accessors, Getters, Mutators, Setters, Collaborators, Controllers, Factories and Degenerate.</code></pre> <p> </p>
Replication Package for "Evaluating the layout quality of UML class diagrams using machine learning"
<p>Open Science material including dataset and replication instructions accompanying the article "Evaluating the layout quality of UML class diagrams using machine learning."</p>
Systematic analysis of disease-linked rare germline variants reveals new classes of cancer predisposing genes
<ul> <li>GEMs_Liver-HCC: 312 cancer patient-specific genome-scale metabolic models (GEMs) for Liver-HCC reconstructed using the RNA-seq data from PCAWG-TCGA Liver-HCC samples and generic human GEM 'Recon 2M.2'</li> <li>GEMs_Lung-SCC: 493 cancer patient-specific GEMs for Lung-SCC reconstructed using the RNA-Seq data from PCAWG-TCGA Lung-SCC samples and generic human GEM 'Recon 2M.2'</li> </ul> <p>All the patient-specific GEMs were generated using a previously developed method (i.e., tINIT algorithm with a rank-based weight function), which is available at <a href="https://bitbucket.org/kaistmbel/recon-manager">https://bitbucket.org/kaistmbel/recon-manager</a>.</p>
Geological Map of the Derain (H10) Quadrangle of Mercury (3 crater class version)
<p>Geological (morphostratigraphic) map recognising 3 crater degradation classes. We also have a 5 crater class version that is otherwise identical. This version is slightly revised after review for publication in J Maps (3 Aug 2022).</p>
Geological Map of the Derain (H10) Quadrangle of Mercury (5 crater class version)
<p>Geological (morphostratigraphic) map recognising 5 crater degradation classes. We also have a 3 crater class version, that is otherwise identical. This version is slightly revised after review for publication in J Maps 3 Aug 2022.</p>
e-shape P5.6 EO based phytoplankton biomass for WFD reporting - samples of Chl-a and WFD classes maps of lakes Lauwersmeer and Saadjärv
<p>The current data set is a sample of the data generated within the e-shape pilot 5.6 called "EO based phytoplankton biomass for WFD reporting" and consists of maps based on earth observation (EO) data from the Sentinel 2 satellite. There is a set of chlorophyll-a (Chl-a) maps, and a set maps of WFD ecological status of phytoplankton biomass, derived from the Chl-a maps by applying local WFD phytoplankton biomass thresholds. These maps can complement the monitoring for the Water Framework Directive (WFD).<br> The lakes within this sample set are lakes Saadjärv (Estonia) and Lauwersmeer (the Netherlands).</p> <p>For more info please see the readme files which are provided with the data files.</p>
Relationship between genome-wide and MHC class I and II genetic diversity and complementarity in a nonhuman primate
<p>Although mate choice is expected to favor partners with advantageous genetic properties, the relative importance of genome-wide characteristics, such as overall heterozygosity or kinship, versus specific loci, is unknown. To disentangle genome-wide and locus-specific targets of mate choice, we must first understand congruence in global and local variation within the same individual. This study compares genetic diversity, both absolute and relative to other individuals (e.g., complementarity), assessed across the genome to that found at the major histocompatibility complex (MHC), a hyper-variable gene family integral to immune system function and implicated in mate choice across species. Using DNA from 22 captive olive baboons (<em>Papio anubis</em>), we conducted double digest restriction-site associated DNA sequencing to estimate genome-wide heterozygosity and kinship and sequenced two class I and two class II MHC loci. We found that genome-wide diversity was not associated with MHC diversity, and that diversity at class I MHC loci was not correlated with diversity at class II loci. Additionally, kinship was a significant predictor of the number of MHC alleles shared between dyads at class II loci. Our results provide further evidence of the strong selective pressures maintaining genetic diversity at the MHC in comparison to other randomly selected sites throughout the genome. Furthermore, our results indicate that class II MHC disassortative mate choice may mediate inbreeding avoidance in this population. Our study suggests that mate choice favoring genome-wide genetic diversity is not always synonymous with mate choice favoring MHC diversity, and highlights the importance of controlling for kinship when investigating MHC-associated mate choice.</p>
Sei whole-genome sequence class annotations
<p>Sei sequence class whole-genome annotations are available in the following files:</p> <ul> <li> <p>sorted.hg38.tiling.bed.ipca_randomized_300.labels.merged.bed - The sorted, merged sequence class assignments from Louvain community clustering of the 30 million sequences, uniformly tiling the whole human genome. The fourth column is the sequence class number, with any sequence classes numbering 40-61 excluded from our analyses in the publication. Sequence classes 0-39 can be mapped to the following labels: <a href="https://github.com/FunctionLab/sei-framework/blob/main/model/seqclass.names">https://github.com/FunctionLab/sei-framework/blob/main/model/seqclass.names</a></p> </li> <li> <p>sorted.hg19.tiling.bed.ipca_randomized_300.labels.merged.bed - lifted over version of the hg38 BED file.</p> </li> </ul>
Text-fig. 10. Platanoxylon cf. haydenii, a, e, h: UF 279-34470; b, c, d, f g: UF 279-34469. a, b: Diffuse porous wood with vessels solitary and in small multiples, which are mostly tangential or oblique, diffuse and diffuse-in-aggregates axial parenchyma., TS. c–e: Scalariform perforation plates. f, g: Opposite intervessel pits, TLS. h: Two size classes of rays, TLS. Platanus sp., UF 279- 24552. i: Predominantly solitary vessels, diffuse and diffuse-in-aggregates parenchyma, growth ring boundary distinct, noded rays, TS. j: Simple perforation plates (PP), RLS. k: Body of ray with procumbent ray cells, RLS. l: Scalariform perforation plate, RLS. m: Rays of two sizes, wide rays>10-seriate, TLS. Scale bars: 200 µm in a, b, h, i, m; 100 µm in j, k: 50 µm in c, d, e, f, l. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 10. Platanoxylon cf. haydenii, a, e, h: UF 279-34470; b, c, d, f g: UF 279-34469. a, b: Diffuse porous wood with vessels solitary and in small multiples, which are mostly tangential or oblique, diffuse and diffuse-in-aggregates axial parenchyma., TS. c–e: Scalariform perforation plates. f, g: Opposite intervessel pits, TLS. h: Two size classes of rays, TLS. Platanus sp., UF 279- 24552. i: Predominantly solitary vessels, diffuse and diffuse-in-aggregates parenchyma, growth ring boundary distinct, noded rays, TS. j: Simple perforation plates (PP), RLS. k: Body of ray with procumbent ray cells, RLS. l: Scalariform perforation plate, RLS. m: Rays of two sizes, wide rays>10-seriate, TLS. Scale bars: 200 µm in a, b, h, i, m; 100 µm in j, k: 50 µm in c, d, e, f, l.
Fig. 1 in Molecular data suggests the ciliate Mesodinium (Protista: Ciliophora) might represent an undescribed taxon at class level
Fig. 1. Photomicrographs of Mesodinium sp. in vivo. A. Representative individual, front view. B–E. Body shape, top/bottom
Fig. 2 in Molecular data suggests the ciliate Mesodinium (Protista: Ciliophora) might represent an undescribed taxon at class level
Fig. 2. Models of the secondary structure of variable region 4 (V4) of the small subunit rRNA molecule, comparing helices 23_1, 23_2, 23_5 species. GenBank/EMBL accession numbers are enclosed in brackets. The number of nucleotides in Helix E23_1 for each species is given above which these species represent are marked in blue below each illustration.
Fig. 3 in Molecular data suggests the ciliate Mesodinium (Protista: Ciliophora) might represent an undescribed taxon at class level
Fig. 3. The comprehensive phylogenetic tree inferred from SSU rRNA gene sequences using Maximum Likelihood analysis with the model selected by AIC in MRMODELTEST for Bayesian analysis. Numbers at the nodes represent the bootstrap percentages from 1 000 replicates for ML analysis. Asterisks indicate bootstrap values less than 50% at a given node. Evolutionary distance is represented by the branch length separating the species in the figure. The scale bar corresponds to ten substitutions per 100 nucleotide positions.
Samples of solar flares classes, active regions and time of occurrence
<p>This dataset contains samples of solar flares measurements of classes X, M, C and B.</p> <p>For clarification, the flares are classified as follows:</p> <ul> <li>Class X: flares <span class="math-tex">\(>10^{-4} watts/m^{2}\)</span></li> <li>Class M: <span class="math-tex">\(10^{-5} watts/m^{2} <\)</span>flares <span class="math-tex">\(<10^{-4} watts/m^{2}\)</span></li> <li>Class C: <span class="math-tex">\(10^{-6} watts/m^{2}<\)</span>flares<span class="math-tex">\(<10^{-5} watts/m^{2}\)</span></li> <li>Class B: <span class="math-tex">\(10^{-7} watts/m^{2}<\)</span>flares<span class="math-tex">\(<10^{-6} watts/m^{2}\)</span></li> </ul> <p>This dataset was assembled with data from https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares</p> <p>The date (yyyy-mm-dd hh:mm:ss) the authors assembled the data is 2017-11-14 13:48:37</p> <p>The original data source is the National Oceanic & Atmospheric Administration (NOAA), U.S. Departement of Commerce.</p> <p>Data description:</p> <ul> <li><strong>Class</strong>: Class of the flare: X, M, C or B</li> <li><strong>Date</strong>: Date of occurence in yyyy-mm-dd format.</li> <li><strong>AR</strong>: Active Region ID attributed by NOAA.</li> <li><strong>Begin</strong>: time the flare begins in hh:mm:ss format.</li> <li><strong>Max</strong>: time the flare reaches its max value in hh:mm:ss format.</li> <li><strong>End</strong>: time the flare vanishes in hh:mm:ss format.</li> </ul> <p>The dataset has 2,256 tuples divided as follows:</p> <ul> <li>171 tuples with X class flares data (7.58%).</li> <li>572 tuples with M class flares data (25.35%).</li> <li>767 tuples with C class flares data (34%).</li> <li>746 tuples with B class flares data (33.07%).</li> </ul> <p>The data collected refer to the period between August 25, 1996 and May 29, 2017.</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.