Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
406
datasets available to search
ShareScore release 0.7.1
Dataset results
406 results for “Apis”
Extended datasets from MM-IMDB and Ads-Parallelity dataset with the features from Google Cloud Vision API
<p>This is extended datasets from MM-IMDB [<a href="https://openreview.net/forum?id=S12_nquOe">Arevalo+ ICLRW'17</a>], Ads-Parallelity [<a href="https://arxiv.org/abs/1807.08205">Zhang+ BMVC'18</a>] dataset with the features from Google Cloud Vision API. These datasets are stored in jsonl (JSON Lines) format.</p> <p><strong>Abstract (from our paper):</strong></p> <p>There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce. Typical methods for extracting important information from multimodal data rely on a mid-fusion architecture that combines the feature representations from multiple encoders. However, as the number of modalities increases, several potential problems with the mid-fusion model structure arise, such as an increase in the dimensionality of the concatenated multimodal features and missing modalities. To address these problems, we propose a new concept that considers multimodal inputs as a set of sequences, namely, deep multimodal sequence sets (DM<sup>2</sup>S<sup>2</sup>). Our set-aware concept consists of three components that capture the relationships among multiple modalities: (a) a BERT-based encoder to handle the inter- and intra-order of elements in the sequences, (b) intra-modality residual attention (IntraMRA) to capture the importance of the elements in a modality, and (c) inter-modality residual attention (InterMRA) to enhance the importance of elements with modality-level granularity further. Our concept exhibits performance that is comparable to or better than the previous set-aware models. Furthermore, we demonstrate that the visualization of the learned InterMRA and IntraMRA weights can provide an interpretation of the prediction results.</p> <p><strong>Dataset (MM-IMDB and Ads-Parallelity):</strong></p> <p>We extended two multimodal datasets, namely, MM-IMDB [<a href="https://openreview.net/forum?id=S12_nquOe">Arevalo+ ICLRW'17</a>], Ads-Parallelity [<a href="https://arxiv.org/abs/1807.08205">Zhang+ BMVC'18</a>] for the empirical experiments. The MM-IMDB dataset contains 25,925 movies with multiple labels (genres). We used the original split provided in the dataset and reported the F1 scores (micro, macro, and samples) of the test set. The Ads-Parallelity dataset contains 670 images and slogans from persuasive advertisements to understand the implicit relationship (parallel and non-parallel) between these two modalities. A binary classification task is used to predict whether the text and image in the same ad convey the same message.</p> <p>We transformed the following multimodal information (i.e., visual, textual, and categorical data) into textual tokens and fed these into our proposed model. We used the <a href="https://cloud.google.com/vision">Google Cloud Vision API</a> for the visual features to obtain the following four pieces of information as tokens: (1) text from the OCR, (2) category labels from the label detection, (3) object tags from the object detection, and (4) the number of faces from the facial detection. We input the labels and object detection results as a sequence in order of confidence, as obtained from the API. We describe the visual, textual, and categorical features of each dataset below.</p> <p><em><strong>MM-IMDB</strong></em>: We used the title and plot of movies as the textual features, and the aforementioned API results based on poster images as visual features.</p> <p><em><strong>Ads-Parallelity</strong></em>: We used the same API-based visual features as in MM-IMDB. Furthermore, we used textual and categorical features consisting of textual inputs of transcriptions and messages, and categorical inputs of natural and text concrete images.</p>
Telraam sensor dataset API - www.telraam-api.net
<p>This dataset is a collection of publicly available API call methods that can be used to access Telraam (<a href="https://telraam.net/">https://telraam.net/</a>) data, connected to our server.</p> <p>The dataset includes all data collected with Telraam sensors, including the sensors deployed during the WeCount project, but also in scope of other projects or by private individuals.</p> <p>The data and API documentation is accessible via <a href="http://www.telraam-api.net">http://www.telraam-api.net</a></p>
Analisis Data Kereta Api dan Stasiun pada Daerah Operasi VIII Surabaya
<p>Data terkait Nama Stasiun, Nama Kereta Api dan Kota yang termasuk dalam Daerah Operasi VIII di Indonesia</p>
African Swine Fever Worldwide Epidemiology Data - OIE Webscrape example - Geocoded using Google API and Manual
<p>Example African Swine Fever dataset generated by programs described in following publication </p> <p>Title: Web-scraping programmatic techniques in aggregating difficult to access OIE WAHIS animal disease outbreak information; using African Swine Fever in Europe as an example.</p> <p>Short running title: Methods for web-scraping OIE WAHIS data.</p> <p>Abstract: This study describes and makes available new methods for acquiring difficult to access, publicly available, disease surveillance data. It uses World Organisation for Animal Heath (OIE) data on African Swine Fever (ASF) outbreaks in Belarus and its neighbouring European countries to showcase the importance of adequate disease surveillance data to inform decision-making. The data acquired from these methods allow for large-scale, geospatial outbreak mapping and summary statistics of any terrestrial disease listed on the OIE World Animal Health Information System (WAHIS) database. These techniques will make important epidemiological data more accessible to the scientific community and aid in gaining further insight into the occurrence and spread of OIE listed diseases in a timely manner, fulfilling an important function of disease surveillance.</p>
Mining API Interactions to Analyze SoftwareRevisions for the Evolution of Energy Consumption (MSR'2021 Dataset)
<p><strong>Motivation</strong></p> <p>This repository contains the data-set used as a basis for our MSR'2021 paper <em>Mining API Interactions to Analyze Software Revisions for the Evolution of Energy Consumption</em>.</p> <p><strong>Description of the dataset</strong></p> <p>The dataset is stored in a file <em>msr_2021_dataset.csv</em> and contains the following data:</p> <ul> <li>id - an individual identifier</li> <li>sampleNr - a number identifying the group this sample relates to</li> <li>name - the name of the library examined</li> <li>className - the class name as an abbreviation</li> <li>method - the name of the executed method</li> <li>duration - duration of method execution</li> <li>durationAdjusted - duration after alignment between method trace and energy profile</li> <li>energyConsumption - computed energy consumption</li> <li>watts - recorded wattage</li> <li>`package-names` - per package uAPI profile</li> <li>uApi - the computed uAPI profile value</li> </ul> <p>The files <em>joule_anova_posthoc_result.csv</em> and <em>uAPI_anova_posthoc_result.csv</em> contain the results of the ANOVA and Tukey HSD posthoc analysis to determine accuracy and F1-score of the presented approach.</p> <p> </p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p>
Twitter Account Dataset - 10,000 accounts tested through the Botometer API
<p>This dataset contains 10,420 accounts extracted from Twitter from tweets containing the word "vaccine". We verified each one through the Botometer API.</p> <p>Includes:</p> <ul> <li>Account ID</li> <li>Echo Chamber Score (0-5)</li> <li>Fake Follower Score (0-5)</li> <li>Financial Score (0-5)</li> <li>Self Declared Score (0-5)</li> <li>Spammer Score (0-5)</li> <li>Other Score (0-5)</li> <li>Overall Score (0-5)</li> </ul> <p>Those appearing with an 'X' correspond to deleted, modified or private accounts.</p>
Data sets and models for the Deep API Learning Revisited paper
<p>Training and test data for the machine learning experiments described in the paper Deep API Learning Revisited paper. Trained models are also included.</p> <p>Deep API Learning Revisited paper: https://doi.org/10.1145/3524610.3527872</p> <p>GitHub repository: https://github.com/hapsby/deepAPIRevisited</p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2019
<p><strong># GPM_API 2019</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2016
<p><strong># GPM_API 2016</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2018
<p><strong># GPM_API 2018</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2017
<p><strong># GPM_API 2017</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
GPM_API - Global Hourly Soil Moisture from GPM IMERG Data - 2020
<p><strong># GPM_API 2020</strong></p> <p>GPM_API data root: <a href="https://zenodo.org/record/6489998">https://zenodo.org/record/6489998</a></p> <p><strong># Related article:</strong></p> <p>Ramsauer, T., & Marzahn, P. (2023). Global Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation Index. <em>International Journal of Remote Sensing</em>, 44(2), 542-566.</p> <p>Article: <a href="https://doi.org/10.1080/01431161.2022.2162351">https://doi.org/10.1080/01431161.2022.2162351</a></p> <p>Free PDF: <a href="https://www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351">www.tandfonline.com/eprint/6VHQ7JCCJXSPAMG3PVIG/full?target=10.1080/01431161.2022.2162351</a></p>
bulk-tumour-api: a programmatically accessible dataset of pre-processed bulk tumour sequencing data
<p><strong>This repository, including the API, are currently under development.</strong></p> <p><strong>bulk-tumour-api</strong>: A programmatically accessible dataset of pre-processed bulk tumour sequencing data. The python API can be found at https://github.com/tomouellette/bulk-tumour-api. All data stored in this repository have been collected from open access online sources. Original references and sources are provided in database.tsv (for empirical patient data) and synthetic.tsv (for simulated data).</p> <p><strong>A note on datasets: </strong></p> <ul> <li>All <em>empirical patient sequencing </em>samples have been processed into pseudo-VCF files which at minimum contain the following columns: sample identifier (sample), patient identifier (patient), chromosome (chr), position (pos), variant allele frequency (VAF), alternate read counts (t_alt_count), depth (DP), and total copy number (total_cn). However, if more data is required, unprocessed data including copy number segments or gene-level calls, clinical, and/or biopsy level information can be found in the /raw/. </li> <li>All <em>synthetic datasets </em>have also been processed in pseudo-VCF files. In some cases, all ground truth information (e.g. subclone frequency) is contained within the pseudo-VCF. In other cases, additional meta/ground-truth information are in separate files; any simulated sample with a column marked has_meta = True will have multiple files that will be downloaded together.</li> </ul>
API traces for malware detection
<p>The dataset consists of traces of benign and malware samples. There are approximately 330k traces in the dataset, each meticulously collected and curated for research and analysis purposes, with an uncompressed size of 550GB. The dataset was collected during the second half of 2023. The file "shas_by_families.json" links each SHA(which is also the individual filenames) with the associated malware or benign family. Each file is in json format and includes the parameters of the API call as well.</p> <p> </p> <p>If you are using this dataset, please cite our work on Arxiv.<br>@misc{fellicious2025malwaredetectionbasedapi,<br> title={Malware Detection based on API calls}, <br> author={Christofer Fellicious and Manuel Bischof and Kevin Mayer and Dorian Eikenberg and Stefan Hausotte and Hans P. Reiser and Michael Granitzer},<br> year={2025},<br> eprint={2502.12863},<br> archivePrefix={arXiv},<br> primaryClass={cs.CR},<br> url={https://arxiv.org/abs/2502.12863}, <br>}</p>
Comprehensive Evaluation of Alternative API, Excipient, and Packaging Material Sources: A Regulatory Perspective
<p>Incorporating alternative sources for Active Pharmaceutical Ingredients (API), excipients, and primary packaging materials in approved pharmaceutical products necessitates rigorous evaluation to ensure product quality, safety, and efficacy are maintained. Regulatory agencies, including the World Health Organization (WHO), the United States Food and Drug Administration (US FDA), the European Medicines Agency (EMA), and the UK Medicines and Healthcare products Regulatory Agency (UK MHRA), require comprehensive data to assess such changes. This abstract summarizes the key data requirements across these agencies, focusing on API, excipients, and primary packaging materials.</p> <p>For APIs, critical data include comparative analytical testing, impurity profiling, stability studies under ICH conditions, and bioequivalence studies when applicable. Agencies mandate risk assessments and process validation to ensure the new API source does not compromise the product's critical quality attributes (CQAs) or therapeutic performance.</p> <p>For excipients, regulatory bodies require detailed information on the new supplier’s compliance with Good Manufacturing Practices (GMP), certificate of analysis (CoA), and comparative studies to demonstrate equivalence in performance, stability, and compatibility with the drug product. Stability testing, extractables and leachables analysis, and risk assessment play crucial roles in ensuring the excipient change does not negatively impact product quality or patient safety.</p> <p>Primary packaging material changes require a thorough evaluation of mechanical and barrier properties, container-closure integrity (CCI), and extractables and leachables studies. Stability testing with the new packaging material under ICH conditions, risk assessments, and functional equivalence testing are essential to confirm the packaging material’s suitability in protecting the drug product throughout its shelf life.</p> <p>In conclusion, stringent regulatory expectations underscore the importance of comprehensive data generation and risk mitigation strategies when incorporating alternative sources for APIs, excipients, and packaging materials in pharmaceutical products. Adherence to these guidelines ensures product safety, efficacy, and compliance with global regulatory standards.</p>
Apis mellifera graph genome
<p><strong>AmelGraph 1.1.0</strong></p> <p>We aligned 5 different publicly available assemblies with the cactus pangenome workflow (v2.0.5):</p> <table> <tbody> <tr> <td> <p><strong>Use </strong></p> </td> <td> <p><strong>Species </strong></p> </td> <td> <p><strong>Genome ID </strong></p> </td> <td> <p><strong>Accession </strong></p> </td> <td> <p><strong>Graph ID </strong></p> </td> <td> <p><strong>Size (Mb) </strong></p> </td> </tr> <tr> <td> <p>Reference </p> </td> <td> <p><em>A. mellifera</em> (DH4) </p> </td> <td> <p>Amel_HAv3.1 </p> </td> <td> <p>GCF_003254395.2 </p> </td> <td> <p>DH4 </p> </td> <td> <p>225.2 </p> </td> </tr> <tr> <td> <p>Derivate </p> </td> <td> <p><em>A. m. mellifera </em></p> </td> <td> <p>INRA_AMelMel_1.0 </p> </td> <td> <p>GCA_003314205.1 </p> </td> <td> <p>mellifera </p> </td> <td> <p>227.0 </p> </td> </tr> <tr> <td> <p>Derivate </p> </td> <td> <p><em>A. m. carnica </em></p> </td> <td> <p>ASM1384124v2 </p> </td> <td> <p>GCA_013841245.2 </p> </td> <td> <p>carnica </p> </td> <td> <p>226.0 </p> </td> </tr> <tr> <td> <p>Derivate </p> </td> <td> <p><em>A. m. caucasica </em></p> </td> <td> <p>ASM1384120v1 </p> </td> <td> <p>GCA_013841205.1 </p> </td> <td> <p>caucasica </p> </td> <td> <p>224.8 </p> </td> </tr> <tr> <td> <p>Derivate </p> </td> <td> <p><em>A. m. ligustica </em></p> </td> <td> <p>ASM1932182v1 </p> </td> <td> <p>GCA_019321825.1 </p> </td> <td> <p>ligustica </p> </td> <td> <p>231.1 </p> </td> </tr> </tbody> </table> <p> </p> <p>We used the cactus<sup>1</sup> <a href="https://github.com/ComparativeGenomicsToolkit/cactus/blob/master/doc/pangenome.md">pangenome workflow</a><a href="https://github.com/ComparativeGenomicsToolkit/cactus/blob/master/doc/pangenome.md"> </a>to generate a 5-ways pangenome alignment, masking in blocks of 10Kb, and considering all the sequences in the different data. Due to incompatibilities with the pangenome workflow generation of the indexes, we regenerated the giraffe indexes from the output GFA/VCF file. We also downloaded annotations available for three genomes (DH4, <em>A. m. carnica</em> and <em>A. m. caucasica</em>), modifying the naming of the contigs to include the subspecies name (i.e. 'LG1' for <em>A. m. caucasica</em> modified to 'caucasica.LG1'). We then ran the vg autoindex function:</p> <pre><code>vg autoindex -w giraffe -o pangenome -t 8 -T ./TMP -x carnica.gff -x caucasica.gff -R XG -x DH4.gff</code></pre> <p>We compared this 'full' cactus graph to one built using only sequence on the linkage groups, and to one generated using the <a href="https://github.com/pangenome/pggb">PGGB workflow</a>. We evaluated the different graphs using <a href="https://github.com/pangenome/pgge">PGGE</a>. This full cactus graph returned the highst aligned identity, sequence matches, and unique alignments, while having the lowest number of multiple mapping and missing alignments.</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>Parameter </strong></p> </td> <td> <p><strong>CACTUS (LG) </strong></p> </td> <td> <p><strong>CACTUS (FULL) </strong></p> </td> <td> <p><strong>PGGB (norm) </strong></p> </td> </tr> <tr> <td> <p>HAv3.1 size </p> </td> <td> <p>221,626,419 </p> </td> <td> <p>225,250,884 </p> </td> <td> <p>225,250,884 </p> </td> </tr> <tr> <td> <p>Graph length (bp) </p> </td> <td> <p>243,077,200 </p> </td> <td> <p>246,675,144 </p> </td> <td> <p>315,672,860 </p> </td> </tr> <tr> <td> <p>Extra sequence </p> </td> <td> <p>21,450,781 </p> </td> <td> <p>21,424,260 </p> </td> <td> <p>90,421,976 </p> </td> </tr> <tr> <td> <p># nodes </p> </td> <td> <p>17,120,383 </p> </td> <td> <p>11,664,080 </p> </td> <td> <p>17,952,597 </p> </td> </tr> <tr> <td> <p># edges </p> </td> <td> <p>21,385,634 </p> </td> <td> <p>15,990,593 </p> </td> <td> <p>21,702,958 </p> </td> </tr> </tbody> </table> <p> </p> <p><strong>References</strong></p> <p>1. Armstrong, J. <em>et al</em>. Progressive Cactus is a multiple-genome aligner for the thousand-genome era. <em>Nature</em> <strong>587</strong>, (2020). </p> <p> </p>
Thoth's GraphQL API Workshop
<p>As Thoth continues to enable presses to manage metadata for their open access books and export it in a number of different formats to various platforms, catalogues and other dissemination channels, we want to make sure all its users are fully aware of Thoth’s open API capabilities. Led by Thoth’s software engineer Javier Arias, the team of COPIM’s Work Package 5 will guide users through its GraphQL API and demo how to query the API, write queries, and export the resulting data into a spreadsheet. Ultimately, this workshop aims to familiarize non-technical users who would like to learn how to directly retrieve data from Thoth’s API with Thoth’s versatile capabilities.</p> <p>Details of the workshop available at: <a href="https://www.copim.ac.uk/outputs/events/220819-thoth-api-workshop/">https://www.copim.ac.uk/outputs/events/220819-thoth-api-workshop/</a></p> <p>Learn more about Thoth at <a href="https://thoth.pub/">https://thoth.pub/</a></p>
Analisis Data Kereta Api dan Stasiun pada Daerah Operasi VIII Surabaya
<p>Data terkait Nama Stasiun, Nama Kereta Api dan Kota yang termasuk dalam Daerah Operasi VIII di Indonesia, beserta dengan syntax yang digunakan untuk memvisualisasikan data tersebut.<br> <br> Update versi ke 3 : <a href="https://zenodo.org/record/3853208">https://zenodo.org/record/3853208</a></p>
APIS Dataset artists
<p>This dataset contains biographical data produced in course of the digital humanities project “Mapping historical networks: Building the new Austrian Prosopographical/Biographical Information System (APIS)” at the Austrian Academy of Sciences. It was funded by the Austrian National Fonds for Research, Technology and Development. The biographies were manually annotated by the author via a web application (<a href="http://apis.acdh.oewa.ac.at">apis.acdh.oewa.ac.at</a>) which was developed at the Austrian Centre for Digital Humanities and Cultural Heritage (ACDH-CH).</p> <p>The starting point of the dataset (cl Kuenstlerhaus) were 506 annotated artists’ biographies from the Austrian Biographical Encyclopaedia 1815–1950 (<a href="https://www.oeaw.ac.at/acdh/oebl/">ÖBL</a>). For these persons, the membership in the Association of Fine Artists Vienna (Genossenschaft der bildenden Künstler Wiens) was confirmed by the comparison of the yearly published membership lists with the lemmas of the ÖBL. The data were collected primarily to enable a) statistics b) historical network analyses and c) cartographic analyses.</p> <p>The data is provided as graphml files:</p> <ul> <li>relations between persons (kinship, pupil/teacher)<br> <strong>cl_kuenstlerhaus_person-person_v1-01</strong></li> <li>relations between persons and institutions (education, career, social networks)<br> <strong>cl_kuenstlerhaus_person-institution_v1-01</strong></li> <li>relations between persons and places (mobility)<br> <strong>cl_kuenstlerhaus_person-place_v1-01</strong></li> </ul> <p>The datset was last reviewed in January 2020.</p>
Checkbot API raw results from Libraries, Archives and Museums websites for evaluating a data-driven Search Engine Optimization methodology
<p>Results from Checkbot API to measure and collect 341 websites compatibility on multiple SEO variables (34 variables). Checkbot API indexes the website's code to find features capable of impacting SEO performance. Each website has been tested with the maximum number of links allowed to be crawled equally to 10.000 per test. In this way, we retrieved data about the overall websites performance including their sub-pages, and not only the main domain names. A scale from 0 (lowest rate) to 100 (highest rate) was adopted for each examined variable. This constitutes a useful managerial indicator of dealing with the quantification of websites performance while avoiding complex measurement systems that are difficult to be adopted by administrators. Websites tested were also categorized by the CMS type used. More information about the variables and the meaning of the results can be found at https://www.checkbot.io/ </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.