Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Catalogues of LISA-detectable sBHB inspirals and confusion backgrounds following the GWTC-3 fiducial model posterior

<h2>Data set</h2> The contents of <code>SNR_min_2_z1_LISA_SNR.tar.gz</code> consist of approximately 10k folders, each corresponding to each of the samples of the public GWTC-3 fiducial sBHB population model posterior, and containing the following files: <ul> <li><code>population.yaml</code>: population parameters of this sample.</li> <li><code>background.txt</code>: frequencies and characteristic strain squared of the sBHB confusion noise in the LISA band for this population.</li> <li><code>population_detector_frame_SNR.h5</code>: subset of loud sBHB sources, including but not limited to those with LISA SNR larger than 4 (missing in some samples). </li> </ul> For a description of the population parameters in <code>population.yaml</code> and the individual source parameters in <code>population_detector_frame_SNR.h5</code>, see the contents of the <code>Demo.ipynb</code> notebook. To be able to run the notebooks described below, uncompress the <code>SNR_min_2_z1_LISA_SNR.tar.gz</code> file inside a <code>data/</code> subfolder under that of the notebook. <h2>Demo notebook</h2> For examples of how to load and process the catalogues, see the <code>Demo.ipynb</code> notebook. This notebook requires the following Python packages:     <code>numpy, scipy, pandas, matplotlib, pyyaml, tqdm</code>      Some of the plots in the notebook require the <a href="https://github.com/JesusTorrado/extrapops" target="_blank" rel="noopener">extrapops</a> simulation package:     <code>$ git clone git@github.com:JesusTorrado/extrapops.git</code>     <code>$ cd extrapops</code>     <code>$ pip install .</code> <h2>References</h2> For detailed descriptions of the generation of the data set, see the references mentioned in the Zenodo page. <h2>Questions and comments</h2> Please use the <a href="https://github.com/JesusTorrado/LISA_sBHB_catalogues/issues">GitHub issue tracker</a> or contact the corresponding authors of the papers cited under the "described by" header of the Zenodo entry.

opencc-by-4.0Sep 2021View details →

Datasets from the RecSys 2021 article "Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders"

We publicly release : <ol> <li>the anonymized deezer_graph.csv and deezer_features.csv datasets</li> <li>the pre-trained node embedding vectors from all pre-trained models</li> </ol> described in the <a href="https://github.com/deezer/similar_artists_ranking/">deezer/similar_artists_ranking/</a> GitHub repository.

opencc-by-4.0Jan 2023View details →

INSPIRE Minimal Dataset and Scripts

Raw data related to the paper titled "INSPIRE: Intensity and spatial information-based deformable image registration". It includes the raw data-points from which the plots in the paper and statistics can be computed, as well as the scripts that are used to compute them for the paper.

opencc-by-4.0Jan 2023View details →

Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation - Supplementary Material

This is the supplementary material for the paper "Strategies, Benefits and Challenges App Store-inspired Requirements Elicitation" (also included in this repository).  Abstract: App store-inspired elicitation is the practice of exploring competitors’ apps, to get inspiration for requirements. This activity is common among developers, but little insight is available on its practical use, advantages, and possible issues. This paper aims to study strategies, benefits, and challenges of app store-inspired elicitation, and compare this technique with more traditional requirements elicitation interviews. We conduct an experimental simulation with 58 analysts and collect qualitative data. Our results show that specific guidelines and procedures are required to better conduct app store-inspired elicitation. Furthermore, current search features made available by app stores are not suitable for this practice, and more tool support is required to help analysts in the retrieval and evaluation of competing products. While interviews focus on the why dimension of requirements engineering (i.e., goals), app store-inspired elicitation focuses on how (i.e., solutions), offering indications for implementation and improved usability. Our study provides a framework for researchers to address existing challenges and suggests possible benefits to foster app store-inspired elicitation among practitioners. The package contains the following files: 1.Protocol.pdf - it describes in details the steps of the protocol and the intermediate results obtained during the execution. 2. Codebooks: 2.a. Codebook Strategies: codebook of the strategies to select apps 2.b Codebook Benefits: codebook of the benefits of use IBE (sheet 1) and ASE (sheet 2) 2.c Codebook Challenges: codebook of the challenged of use IBE (sheet 1) and ASE (sheet 2) 2.d Differences IBE-ASE: table of the identified (categorized) differences between IBE and ASE 3. Labelled Data  3.a Strategies - labeled data: the file contains the name of the selected apps, the motivation behind the selection, and the themes assigned to them (refer to 2.a for the explanation of the themes). 3.b Benefits IBE - labeled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.b for the explanation of the themes). 3.c Challenges IBE - labeled data: the file contains the extract of the raw data about IBE challenges and the themes assigned (refer to 2.c for the explanation of the themes). 3.d Benefits ASE - labeled data: the file contains the extract of the raw data about ASE benefits and the themes assigned (refer to 2.b for the explanation of the themes). 3.e Challenges ASE - labeled data: the file contains the extract of the raw data about IBE benefits and the themes assigned (refer to 2.c for the explanation of the themes). 4. Raw data.xls: it contains the raw data used in the work (two sheets, one for strategies and one for reflections). 5. SLR data: data related to the lightweight systematic literature review  5.a Codebook Scopus.xlsx: codebook for the themes elicited from the SLR. The themes are also present in the files in the folder Codebooks. 5.b SLR-scopus-results-and-selected.xlsx: results of the search string, and, in green, the selected papers.  6. Readme.txt: summary file.  Note that some of the rows in Raw data.xls (and in the corresponding "Labelled Data" files)  are substituted with N/A. This corresponds to those participants who asked to not publicly share their responses.

opencc-by-4.0Feb 2023View details →

The geological map of Italy 1:100,000 scale INSPIRE harmonised

The INSPIRE Directive institute a European infrastructure for spatial information to support the environmental policies of the European Union. In the mainframe of the Directive, 34 different themes that represents different environmental information has been identified. One of this is the Geology theme; it is split into three subthemes and represent a "reference data theme” because it provides basic knowledge on the physical properties and composition of rocks and sediments, their structure and their age as represented in geological maps, as well as geomorphological features. In the feature catalogue of the INSPIRE application schema Geology has been defined the term lists for the information types. Some of these are fully compliant with the features defined in the 1:100.000 scale geological map database and are used in the semantic harmonization procedure. The Geological Map of Italy at 1:100,000 scale is, at present, the most detailed complete geological map of Italy. It consists of a collage of 277 sheets, and it was produced over a period of more than 100 years, with some sheets in two editions. In the late 1990s, it was transformed into a vector database by digitising from raster format. The dataset was revised, integrated, and corrected between 2005-2009 by the Geological Survey of Italy. In 2021-22 the dataset was harmonised according to the INSPIRE and GeoSciML data models. The whole area has been subdivided into three different .gml format dataset on geographic base: Northern, Central and Southern Italy.

dryad36/100

Data from: Towards automated ethogramming: Cognitively-inspired event segmentation for streaming wildlife video monitoring

Our dataset, Nest Monitoring of the Kagu, consists of around ten days (253 hours) of continuous monitoring sampled at 25 frames per second. Our proposed dataset aims to facilitate computer vision research that relates to event detection and localization. We fully annotated the entire dataset (23M frames) with spatial localization labels in the form of a tight bounding box. Additionally, we provide temporal event segmentation labels of five unique bird activities: Feeding, Pushing leaves, Throwing leaves, Walk-In, and Walk-Out. The feeding event represents the period of time when the birds feed the chick. The nest-building events (pushing/throwing leaves) occur when the birds work on the nest during incubation. Pushing leaves is a nest-building behavior during which the birds form a crater by pushing leaves with their legs toward the edges of the nest while sitting on the nest. Throwing leaves is another nest-building behavior during which the birds throw leaves with the bill towards the nest while being, most of the time, outside the nest. Walk-in and walkout events represent the transitioning events from an empty nest to incubation or brooding, and vice versa. We also provide five additional labels that are based on time-of-day and lighting conditions: Day, Night, Sunrise, Sunset, and Shadows. In our manuscript, we provide a baseline approach that detects events and spatially localizes the bird in each frame using an attention mechanism. Our approach does not require any labels and uses a predictive deep learning architecture that is inspired by cognitive psychology studies, specifically, Event Segmentation Theory (EST). We split the dataset such that the first two days are used for validation, and performance evaluation is done on the last eight days.

opencc-zeroMar 2023View details →

opencc-by-4.0Jun 2023View details →

Raw dataset for "A Bio-Inspired SONAR System for Autonomous Flying Drones".

Created on Mon Sep 26, 2023 Written by Yasufumi Yamada This repository was uploaded for the original article entitled  "A Bio-Inspired SONAR System for Autonomous Flying Drones". This repository includes the raw dataset for Fig.1, Fig.2, and Fig.3. Note that, the ".data" format can be read by changing the format name to ".txt". all "chk_" files indicate signal data which was recorded with a sampling frequency of 1MHz.  chk_rxwv    : time amplitude signal of the received sound. chk_txwv    : digital time amplitude signal for emission. chk_env      : Cross-correlation signal processed with received sound and digital emission sound.           Each data was conducted the Hilbert transform to extract the envelope of the cross-correlation signal.

opencc-by-4.0Jul 2023View details →

Push-broom NIR-HSI scanning of painting reconstruction, inspired by Sandro Botticelli's "Venus"

The dataset contains the push-broom scanning NIR-HSI of Sandro Botticelli’s Venus painting detail, produced in the Microchemistry and Microscopy Art Diagnostic Laboratory (M2ADL) of the University of Bologna (2018). The painting was executed with egg tempera on a wood panel prepared with a layer of gypsum and glue. Ancient and modern pigments were selected and employed according to their vibrational signals, detectable by NIR spectroscopy. In a more detail, earth-based pigments were used for the hair and for the flesh tone, while zinc white (ZnO) was applied in the white areas of the eyes and in the hair ribbon. Finally, dammar varnish was applied to half of the painted surface, according to ancient practices which involved the use of a terpenic varnish to improve color rendering and protect the underlying painted layer (Figure 8A). The painting was scanned with a SWIR3 hyperspectral push-broom camera working in the 1000–2500 nm spectral range, at 5.6 nm spectral resolution (Specim Ltd, Finland). The scanning system is also characterized by three halogen lamps (35 W, 430 lm, 2900 K, each) used as illumination sources and a horizontal moving stage (40 × 20 cm) on which the painting was scanned. The scanning parameters were set as follows: scan rate equal to 0.7 mm/s, push-broom camera frame rate equal to 50.00 Hz and exposure time equal to 9 ms. The hyperspectral system was controlled with the Lumo Scanner software (Specim Ltd, Finland). Before the scan, dark (with the camera shutter closed) and white (using a Spectralon reference tile) reference images were acquired, to compute reflectance values. ENVI hyperspectral files (.raw and .hdr) related to the scanned sample, white reference, and dark reference are included. Please, note that ALL the files must be downloaded and included in the same folder. Please, cite as: R. Rocha de Oliveira, C. Malegori, G. Sciutto, P. Oliveri PoliBrush – A user-friendly software to aid multivariate image analysis dissemination Chemometrics and Intelligent Laboratory Systems, 240 (2023) 104918 <a href="https://doi.org/10.1016/j.chemolab.2023.104918">https://doi.org/10.1016/j.chemolab.2023.104918</a>   You may also be interested in:    XRF-HSI Vermeer dataset    <a href="https://www.doi.org/10.5281/zenodo.8143464">10.5281/zenodo.8143464</a>    PoliBrush    A freely distributed software for exploratory multivariate analysis in RGB and spectral imaging    <a title="PoliBrush" href="https://doi.org/10.5281/zenodo.8143341" target="_blank" rel="noopener">10.5281/zenodo.8143341</a>

opencc-by-4.0Jun 2021View details →

Insights to Inspire - Informatics: The Journey to Interoperability

To accelerate translation, researchers need access to a broad range of data from a variety of sources (electronic health records, imaging, genetics, behavioral, etc.). These sources manage and store data differently, which creates the need for standardization. The Informatics Common Metric, which is in its second year, addresses the need to harmonize data across the CTSA Program. This will enhance our ability to collaborate on initiatives both within and outside the consortium. The metric also supports the following NCATS strategic objective: “Develop interoperable and integrative biomedical informatics resources to facilitate translational innovation in disease prevention, diagnosis and treatment.”   The Common Metrics Initiative team is disseminating a series of brief webcasts from CTSA Program experts with topics ranging from foundational information to understand the field of informatics to how to get to interoperability. The goal for I2I 2021 is to build a community of expertise among CTSA Programs to create true data interoperability across the consortium. To meet this goal, the focus of these webcasts is to: <ul> <li>Encourage hubs to assess their current status with regard to data quality and completeness and standardization to advance clinical and translational science</li> <li>Assist hubs in improving their processes, such as implementing protocols and developing new tools</li> <li>Identify the needs of personnel and interdisciplinary teams, and finally, to</li> <li>Determine key national data networks and local partnerships</li> </ul> The first five webcasts are an introduction to informatics, created to build foundational knowledge and to define key terms. We encourage you to watch them in order.

opencc-by-4.0Oct 2023View details →

Supplementary data for paper "Extreme mass-ratio inspiral and waveforms for a spinning body into a Kerr black hole via osculating geodesics and near-identity transformations"

Radiation-reaction fluxes and NIT interpolant data for paper "Extreme mass-ratio inspiral and waveforms for a spinning body into a Kerr black hole via osculating geodesics and near-identity transformations"

opencc-byMay 2022View details →

10 genshin impact inspired sword 3d models

*****THIS IS NOT SPONSERED BY GENSHIN IMPACT IN ANY WAY But you can still like and follow me and tell me what should I make next Source: Objaverse 1.0 / Sketchfab

The INSPIRE-Lung Study

ClinicalTrials.gov study NCT05824273. IPD Sharing: NO. Countries: 1. Publications: 35.

The Bio-Inspired Artificial Pancreas for the Home

ClinicalTrials.gov study NCT03740698. IPD Sharing: NO. Countries: 1. Publications: 5.

The INSPIRE-ASP UTI Trial

ClinicalTrials.gov study NCT03697096. IPD Sharing: NO. Countries: 1. Publications: 1.

Inspire HER: Inspiring the Heart and Emotions for Radical Health

ClinicalTrials.gov study NCT06966258. IPD Sharing: YES. Countries: 1. Publications: 1.

Effect of Different Inspired Oxygen Concentrations on Intraoperative Recruitment Outcomes in Patients Undergoing Abdominal Surgery

ClinicalTrials.gov study NCT06746181. IPD Sharing: YES. Countries: 1. Publications: 6.

Accountability Support Through Peer-Inspired Relationships and Engagement (ASPIRE) Trial

ClinicalTrials.gov study NCT06617702. IPD Sharing: YES. Countries: 1. Publications: 3.

restrictedIPD-UNDECIDEDFeb 2026View details →

Efficacy and Safety of Inhaled Isoflurane Delivered Via the Sedaconda ACD-S Compared to Intravenous Propofol for Sedation of Mechanically Ventilated Intensive Care Unit Adult Patients (INSPiRE-ICU2)

ClinicalTrials.gov study NCT05327296. IPD Sharing: Not stated. Countries: 1. Publications: 16.

A Gamified, Social Media Inspired Personalized Normative Feedback Alcohol Intervention for Sexual Minority Women

ClinicalTrials.gov study NCT03884478. IPD Sharing: YES. Countries: 1. Publications: 2.

The INSPIRE-ASP PNA Trial

ClinicalTrials.gov study NCT03697070. IPD Sharing: NO. Countries: 1. Publications: 1.