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19 results for “external datasets”
Earthquake Response of Reinforced Concrete Frames with Infill and Active External Confinement: Tests and Dataset
<p>One option to retrofit reinforced concrete (RC) frames is the construction of infill walls. Many studies have shown that infill increases lateral strength and stiffness but tends to reduce drift capacity relative to bare frames. Fewer studies have quantified reductions in drift demand attributed to infills prior to failure. This report summarizes experiments designed to compare drift demands of frames with and without infill. Included data comes from two theses completed at Purdue University which focused on the dynamic response of one-third scale, non-ductile RC frames to uniaxial simulated earthquake ground motions. Tests were conducted on bare frames, frames with masonry infill walls, and frames with timber infill walls. In 11 of 14 test series, active confinement was applied to columns using external post-tensioned reinforcement</p> <p> </p> <p>This dataset summarizes two experimental programs that studied the dynamic, in-plane response of one-third scale RC frames with full-height infills and active external column confinement. Theses summarized were written by Monical (2021) and Kerby (2022), and included data from 254 in-plane dynamic tests of non-ductile RC frames with various seismic retrofits.</p>
Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning - Dataset
<p>This page contains the data collected for the paper: <em><strong>Modeling Autonomic Pupillary Responses from External Stimuli Using Machine Learning </strong></em>(<a href="https://doi.org/10.26717/BJSTR.2019.20.003446">DOI:10.26717/BJSTR.2019.20.003446</a>). The dataset consists of spectral and pupillometric data collected during three outdoor/indoor walks. The folders “raw”, “merged”, and “cleaned” contain data collected by the Konica Minolta CL-500A Illuminance Spectrophotometer and Tobii Pro Glasses 2 at three different stages in the data preparation process. The “raw” folder contains uncleaned and unsynchronized .csv/.json files. The “merged” folder contains uncleaned, but synchronized light and ocular data in .csv format. The “cleaned” folder contains a single .csv of cleaned and synchronized data with the derived variables: average pupil diameter and pupil diameter difference. </p> <p>The best choice of data files will depend on desired analysis. More guidance on how to handle this data can be found in the readMe files located in each subsequent folder. More information on the sensing devices used here can be found at the Minolta and Tobii information links below. </p> <p><strong>Minolta Information</strong>: <a href="https://sensing.konicaminolta.us/uploads/cl-500a_instruction217a_eng-250cl60686.pdf">https://sensing.konicaminolta.us/uploads/cl-500a_instruction217a_eng-250cl60686.pdf</a></p> <p><strong>Tobii Information</strong>: <a href="https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/tobii-pro-glasses-2-user-manual.pdf/?v=1.1.3">https://www.tobiipro.com/siteassets/tobii-pro/user-manuals/tobii-pro-glasses-2-user-manual.pdf/?v=1.1.3</a></p> <p>The codes used to prepare, analyze, and visualize this data is available in the LightOcular GitHub Repository linked below. </p> <p><strong>LightOcular GitHub Repo</strong>: <a href="https://github.com/mi3nts/LightOcular">https://github.com/mi3nts/LightOcular</a></p>
Dataset for the paper "Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943
<p>This repository contains the data needed to reproduce the results in: </p> <p>Ripa M, Di Felice LJ, Giampietro M (2021). The energy metabolism of post-industrial economies. A framework to account for externalization across scales. Energy, 214." https://doi.org/10.1016/j.energy.2020.118943</p> <p>The dataset was also used for a case study in "Di Felice L., Dunlop T., Giampietro M., Kovacic Z., Renner A., Ripa M., Velasco-Fernández R. – Report on the Quality Check of the Robustness of the Narrative behind Energy Directives. MAGIC (H2020–GA 689669) Project Deliverable 5.4, 30 November 2018". (link: https://magic-nexus.eu/documents/d54-report-narratives-behind-energy-directives).</p> <p>Sources of data are specified in the dataset (under tab "References")</p>
The effects of increased thermal insulation in timber-framed external walls with thin gypsum board as a wind barrier - Dataset
<p>This dataset includes measured data from the field measurement of four timber-framed exterior wall constructions. All the walls were equipped with gypsum board wind barrier having a minimal thermal resistance. Insulation thicknesses of 150 mm and 300 mm, demonstrating a moderate and a very effective levels of thermal insulation, were compared. Wooden cladding and brick veneer were compared as façade materials.</p> <p>Measurements were done in a test building of Tampere University, Tampere, Finland. The coordinates of the campus are 61°27' N, 23°52’ E. Ground height at test building site is approximately 135 m above sea level. The site is rather protected area, with the modest wind and driving rain load. </p> <p>The test was performed between 13 September 2020 and 30 November 2021. </p> <p> </p>
A dataset of scholarly journals in wikidata : (selected) external identifiers
<p> </p> <p><strong>For an updated list , see </strong></p> <p><a href="https://github.com/almugabo/openalex_qa/blob/main/coverage/OpenAlex_venues.md">Matching OpenAlex venues to Wikidata identifiers</a></p> <p><strong>Motivation : the selective/Inclusive approach in bibliometric databases </strong></p> <p>An important difference between bibliometric databases is their “<em>inclusion policy”</em>. </p> <p>Some databases like Web Of Science and Scopus select the sources they index, while others like Dimensions and OpenAlex are more inclusive (they index for example all data from a given source such as Crossref). </p> <p><a href="https://doi.org/10.1162/qss_a_00018">WOS</a></p> <p>“<em>selectivity remained a hallmark of coverage because Garfield had decided early on to focus on internationally influential journals.” (...)</em>.” </p> <p><a href="https://doi.org/10.1162/qss_a_00019">SCOPUS </a></p> <p>“<em>Serial content (i.e., journals, conference proceedings, and book series) submitted for possible inclusion in Scopus by editors and publishers is reviewed and selected, based on criteria of scientific quality and rigor. This selection process is carried out by an external Content Selection and Advisory Board (CSAB) of editorially independent scientists, each of which are subject matter experts in their respective fields. This ensures that only high-quality curated content is indexed in the database and affirms the trustworthiness of Scopus</em>”</p> <p> </p> <p><a href="https://doi.org/10.1162/qss_a_00020">Dimensions </a></p> <p><em>We have decided to take an “inclusive” approach to the publications we index in Dimensions. We believe that Dimensions should be a comprehensive data source, not a judgment call, and so we index as broad a swath of content as possible and have developed a number of features (e.g., the Dimensions API, journal list filters that limit search results to journals that appear in sources such as Pubmed or the 2015 Australian ERA</em><em>6</em><em> journal list) that allow users to filter and select the data that is most relevant to their specific needs.</em></p> <p><br> <br> <strong>Using wikidata to enable the filtering of “ venues subsets” in OpenAlex </strong></p> <p> </p> <p>We are interested in creating subsets of venues in OpenAlex (for example for comparative analysis with inclusive databases or other use cases). This would require matching identifiers of OpenAlex venues to other identifiers. </p> <p>Thanks to WikiCite, a project to record and link scholarly data, Wikidata has a large collection of metadata related to Scholarly journals. This repository provides a subset of the scholarly journals in Wikidata, focusing mainly on external identifiers.</p> <p>The dataset will be used to explore the extent to which wikidata journal external identifiers can be used to select the content in OpenAlex. </p> <p>(see here an <a href="https://github.com/almugabo/open_metadata/wiki/Journals">list of openly available lists of journals </a>)</p> <p><strong>Dataset creation & Documentation </strong></p> <ul> <li> <p>Wikidata dump from 2022-02-21</p> </li> <li> <p>Extract entities with following properties: </p> <ul> <li> <p>https://www.wikidata.org/wiki/Q5633421 # scientific journal (Q5633421)</p> </li> <li> <p>https://www.wikidata.org/wiki/Q737498 # academic journal (Q737498)</p> </li> </ul> </li> <li> <p>Extract the properties related to (selected) external identifiers </p> </li> </ul> <p> </p> <p>Some numbers : </p> <p>Number of journals in wikidata : 113,797 ; With issn_l 95,888 , With OpenAlex_venue id : 29,150</p> <p><strong>external identifiers</strong></p> <p>https://www.wikidata.org/wiki/Property:P236 # ext_id_issn</p> <p>https://www.wikidata.org/wiki/Property:P7363 # ext_id_issn_l</p> <p>https://www.wikidata.org/wiki/Property:P8375 # ext_id_crossref_journal_id</p> <p>https://www.wikidata.org/wiki/Property:P1055 # ext_id_nlm_unique_id</p> <p>https://www.wikidata.org/wiki/Property:P1058 # ext_id_era_journal_id</p> <p>https://www.wikidata.org/wiki/Property:P1250 # ext_id_danish_bif_id</p> <p>https://www.wikidata.org/wiki/Property:P10283 #ext_id_openalex_id</p> <p>https://www.wikidata.org/wiki/Property:P1156 # ext_id_scopus_source_id</p> <p><br> <strong>Indexing services</strong></p> <p>https://www.wikidata.org/wiki/Property:P8875</p> <p>https://www.wikidata.org/wiki/Q371467 # Scopus</p> <p>https://www.wikidata.org/wiki/Q104047209 # Science Citation Index Expanded</p> <p>https://www.wikidata.org/wiki/Q22908122 # Emerging Sources Citation Index</p> <p>https://www.wikidata.org/wiki/Q1090953 # Social Sciences Citation Index</p> <p>https://www.wikidata.org/wiki/Q713927 # Arts and Humanities Citation index</p> <p> </p> <p> </p> <p> </p> <p> </p>
Exploring Gender Bias in Remote Pair Programming among Software Engineering Students: The Twincode Original Study and First External Replication (datasets)
<p>This repository contains the datasets of the original experiment (University of Seville, December 2021) and its first external replication (University of California, Berkeley, May 2022) of the Twincode exploratory study on the effects of gender bias in remote pair programming among software engineering students.</p>
Dataset on computational and experimental external dose rates from nuclear medicine patients.
<p><strong>A journal paper published in EJNMMI Physics details the method to create the data, which support also the results described in the paper. The paper describes the development and validation of an advanced computational framework for the assessment of external dose rates from nuclear medicine patients. </strong></p>
Dataset: electrophysiological dynamics during internal–external attention switching
<p>data_raw.zip contains raw iEEG data, the structure of which is described in the .docx file.</p> <p>code_figures.zip contains numerical source data and code for each figure in the manuscript.</p> <p>code_SEEG_dataAnalysis.zip contains additional functions and code to reproduce the figures, as well as code library used to generate the results in the manuscript.</p> <p>Hammer2024_attentionSwitching.pdf is the published paper in Communications Biology.</p>
The Private and External Costs of Germany's Nuclear Phase-out, Replication Dataset
<p>Replication files for "The Private and External Costs of Germany's Nuclear Phase-out", published in the <em>Journal of the European Economic Association.</em></p>
Dataset from "An effective Active Self-Correction in standing without external aids is possible in Adolescents with Idiopathic Scoliosis: a Cross-Sectional Study"
<p>Dataset from a still not published paper titled "An effective Active Self-Correction in standing without external aids is possible in Adolescents with Idiopathic Scoliosis: a Cross-Sectional Study"</p>
Dataset for External quality assessment (EQA) of Neisseria gonorrhoeae antimicrobial susceptibility testing in primary laboratories in Germany
<p>This dataset is used for the publication "External quality assessment (EQA) of Neisseria gonorrhoeae antimicrobial susceptibility testing in primary laboratories in Germany" and contains data on performed Neisseria gonorrhoeae AMR tests in sentinel Laboratories.</p>
External test datasets for "SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis"
<p>In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays.</p> <p>To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results.</p> <p>SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at <a href="https://github.com/FunctionalUrology/SpheroScan">https://github.com/FunctionalUrology/SpheroScan</a>.</p>
Dataset for "Validation of the Astro dataset clustering solutions with external data"
<p><strong>Validation data for the <em>Astro</em> scientific publication clustering benchmark dataset</strong></p> <p>This is the dataset used in the publication Donner, P. "Validation of the Astro dataset clustering solutions with external data", Scientometrics, DOI 10.1007/s11192-020-03780-3</p> <p>Certain data included herein are derived from Clarivate Web of Science. © Copyright Clarivate 2020. All rights reserved.</p> <p>Published with permission from Clarivate.</p> <p>The original <em>Astro</em> dataset is not contained in this data. It can be obtained from http://topic-challenge.info/ and requires permission from Clarivate Analytics for use.</p> <p> </p> <p>This dataset collection consists of four files. Each file contains an independent dataset that relates to the Astro dataset via Web of Science (WoS) record identifiers. These identifiers are called UTs. All files are tabular data in CSV format. In each, at least one column contains UT data. This should be used to link to the <em>Astro </em>dataset or other WoS data. The datasets are discussed in detail in the journal publication.</p> <p> </p>
External RNA-seq dataset for manuscript entitled "Downregulated dual-specificity protein phosphatase 1 in ovarian carcinoma: a comprehensive study with multiple methods"
<p>RNA-seq dataset of ovarian cancer from TCGA and normal ovary from GTEx database</p>
External microarray datasets for manuscript entitled "Downregulated dual-specificity protein phosphatase 1 in ovarian carcinoma: a comprehensive study with multiple methods"
<p>Microarrays related to ovarian carcinoma from GEO database</p>
Supplementary Dataset: Biological oscillations without genetic oscillator or external forcing
Open the record for dataset details and reuse information.
Dataset related to article "External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era "
<p>This record contains raw data related to article “External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era"</p> <p>Abstract</p> <p><strong>Introduction: </strong> To externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND).</p> <p><strong>Materials and methods: </strong> After the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis.</p> <p><strong>Results: </strong> A median of 13 (IQR 9-18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (<em>p</em> = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA.</p> <p><strong>Conclusion: </strong> The direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.</p>
Expression data in HK-2 cells following exposure to CsA (external dataset 2)
GEO Series GSE27167. Homo sapiens. 12 samples. Type: Expression profiling by array.
Expression data in HK-2 cells following exposure to CsA (external dataset 1)
GEO Series GSE27096. Homo sapiens. 12 samples. Type: Expression profiling by array.
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.