Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Figures for RAE Editorial 'What Teachers and Researchers in the Area of Business Management Need to Know About Open Science'

<pre>This set of figures is related to an Editorial dedicated to Open Science, published by <a href="https://bibliotecadigital.fgv.br/ojs/index.php/rae/index">Revista de Administração de Empresas (RAE)</a>: </pre>

Alert Type Frequency Assessment of Open-Source Static Analysis Tools and Codebases

This includes all data needed to replicate and validate our frequency analysis of static analysis (SA) alerts produced using open-source SA tools on several OSS codebases. It includes instructions how to get and run the SA tools, a Dockerfile to conveniently get and use the SA tools, raw SA tool output, some python scripts to parse that output, parsed SA data and aggregate analyses, and SA data augmented with CERT coding rule and CWE data.   The SA tools used: <ul> <li>clang-tidy version 15.07   </li> <li>cppcheck version 2.9   </li> <li>CERT Rosecheckers   </li> </ul> The codebases analyzed: <ul> <li>zeek version 5.1.1</li> <li>git version 2.39.0</li> <li>dos2unix version 7.4.3</li> </ul>

MULTIPLIERS_WP2_Dataset on good practices in open-schooling_UCY_UAB_20230615_v1

This dataset (related to Deliverable D2.1) contains data on the review of good practices in open schooling, consisting mainly in: <ul> <li>A bibliographic database containing the precise list of all bibliographic items on open-schooling issues as of 2011 – 2021 as well as data documenting the article selection process,</li> <li>Instructions for data collection,</li> <li>Collected data relating to both EU and national projects related to Open Schooling.</li> </ul>

Helsinki Tomography Challenge 2022 (HTC2022) open tomographic dataset

This dataset was primarily designed for the Helsinki Tomography Challenge 2022 (HTC2022), but it can be used for generic algorithm research and development in 2D CT reconstruction.The dataset contains 2D tomographic measurements, i.e., sinograms and the affiliated metadata containing measurement geometry and other specifications. The sinograms have already been pre-processed with background and flat-field corrections, and compensated for a slightly misaligned center of rotation in the cone-beam computed tomography scanner. The log-transforms from intensity measurements to attenuation data have also been already computed. The data has been stored as MATLAB structs and saved in .mat file format.The purpose of HTC2022 was to develop algorithms for limited angle tomography. The challenge data consists of tomographic measurements of two sets of plastic phantoms with a diameter of 7 cm and with holes of differing shapes cut into them. The first set is the teaching data, containing five training phantoms. The second set consists of 21 test phantoms used in the challenge to test algorithm performance. The test phantom data was released after the competition period ended.The training phantoms were designed to facilitate algorithm development and benchmarking for the challenge itself. Four of the training phantoms contain holes. These are labeled ta, tb, tc, and td. A fifth training phantom is a solid disc with no holes. We encourage subsampling these datasets to create limited data sinograms and comparing the reconstruction results to the ground truth obtainable from the full-data sinograms. Note that the phantoms are not all identically centered. The teaching data includes the following files for each phantom:<ul><li>The sinogram and all associated metadata (.MAT).</li><li>A pre-computed FBP reconstruction of the phantom (.MAT and .PNG).</li><li>A segmentation of the FBP reconstruction created with the procedure described below (.MAT and .PNG).</li></ul>Also included in the teaching dataset is a MATLAB example script for how to work with the CT data.The challenge test data is arranged into seven different difficulty levels, labeled 1-7, with each level containing three different phantoms, labeled A-C. As the difficulty level increases, the number of holes increases and their shapes become increasingly complex. Furthermore, the view angle is reduced as the difficulty level increases, starting with a 90 degree field of view at level 1, and reducing by 10 degrees at each increasing level of difficulty. The view-angles in the challenge data will not all begin from 0 degrees.The test data includes the following files for each phantom:<ul><li>The full sinogram and all associated metadata (.MAT).</li><li>The limited angle sinogram and all associated metadata, used to test the algorithms submitted to the challenge (.MAT).</li><li>A pre-computed FBP reconstruction of the phantom using the full data (.MAT and .PNG).</li><li>A pre-computed FBP reconstruction of the phantom using the limited angle data. These are of poor quality, and serve mainly as a demonstration of how FBP fails with limited angle data (.MAT and .PNG).</li><li>A segmentation of the FBP reconstruction using the full data, created with the procedure described below. This was used as the ground truth reference in the challenge (.MAT and .PNG).</li><li>A segmentation of the FBP reconstruction using the limited angle data, created with the procedure described below. These are of poor quality, and serve mainly as a demonstration of how FBP fails with limited angle data (.MAT and .PNG).</li><li>A photograph of the phantom, rotated and resized to match the ground truth segmentation (.PNG).</li></ul>Also included in the test dataset is a collage in .PNG format, showing all the ground truth segmentation images and the photographs of the phantoms together.As the orientation of CT reconstructions can depend on the tools used, we have included the example reconstructions for each of the phantoms to demonstrate how the reconstructions obtained from the sinograms and the specified geometry should be oriented. The reconstructions have been computed using the filtered back-projection algorithm (FBP) provided by the ASTRA Toolbox.We have also included segmentation examples of the reconstructions to demonstrate the desired format for the final competition entries. The segmentation images for obtained by the following steps: 1) Set all negative pixel values in the reconstruction to zero. 2) Determine a threshold level using Otsu's method. 3) Globally threshold the image using the threshold level. 4) Perform a morphological closing on the image using a disc with a radius of 3 pixels.The competitors were not obliged to follow the above procedure, and were encouraged to explore various segmentation techniques for the limited angle reconstructions.For getting started with the data, we recommend the following MATLAB toolboxes:HelTomo - Helsinki Tomography Toolbox https://github.com/Diagonalizable/HelTomo/ The ASTRA Toolbox https://www.astra-toolbox.com/ Spot – A Linear-Operator Toolbox https://www.cs.ubc.ca/labs/scl/spot/ Using the above toolboxes for the Challenge was by no means compulsory: the metadata for each dataset contains a full specification of the measurement geometry, and the competitors were free to use any and all computational tools they want to in computing the reconstructions and segmentations.All measurements were conducted at the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki.

Data and supplementary materials in support of "Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator"

Data and supplementary materials in support of  "Evgenios Vlachos, Regine Ejstrup, Thea Marie Drachen, Bertil Fabricius Dorch (2023) Development and Preliminary Validation of an Open Access, Open Data and Open Outreach Indicator, Frontiers in Research Metrics and Analytics, doi: 10.3389/frma.2023.1218213"   It includes the anonymized dataset with the OADO values for all researchers from 10 departments (2 per faculty), the R code to perform the analysis and the graphs, the Scopus search string for the background search on Open Access and metrics, and the documentation for pulling data out from Pure.

OLID I: An Open Leaf Image Dataset of Bangladesh's Major Crops

Artificial intelligence (AI) has taken the globe by storm since its inception, and the enormous agriculture sector is no exception. The progress of any AI-assisted mechanism is heavily reliant on massive training data. Although the application of AI in plant leaf management has garnered prominence in recent years, there is still a dearth of data, especially in the case of tropical and subtropical crops. In light of this, we present a public dataset containing 4,749 leaf images which include healthy, nutritionally deficient, and pest-infested leaves of tomato (Solanum lycopersicum), eggplant (Solanum melongena), cucumber (Cucumis sativus), bitter gourd (Momordica charantia), snake gourd (Trichosanthes cucumerina), ridge gourd (Luffa acutangula), ash gourd (Benincasa hispida), and bottle gourd (Lagenaria siceraria). The dataset comprises 57 unique classes with high-resolution photos (3024 x 3024). The images have been captured at three different sites in Bangladesh in natural field settings and arduously labeled by an expert panel. This collection features the highest number of plant stress classes and the first multi-label classification problem in the agro-domain. The effective utilization of our dataset will result in an abundance of leaf disease diagnosis algorithms, pest identification and classification tools, and nutritional deficiency estimation strategies, to highlight a few.

opencc-by-4.0Feb 2023View details →

Evaluation of Agriculture Open Data Ecosystem Maturity

This dataset represents an evaluation of the key elements of the agricultural open data ecosystem in Croatia: Stakeholders, Infrastructure, Data, Policy/Governance. The evaluation was made by experts. For each element, an evaluation was made according to certain criteria. The experts involved in the research were representatives of each stakeholder group, namely: Management and Support Organizations, Agriculture Producers/Farmers, Suppliers, Researchers and Scientists, Consumers/Consumer Organizations and Other Stakeholders.

Associated data underlying the publication "Flipped Classroom Real-World Activities for Learning Open Computing Concepts"

Various “open” concepts in Computing, such as open standards, open data, open licenses or system interoperability are becoming more important in the professional lives of software engineers. However, students usually do not receive a systematic education about these concepts ; rather they sporadically learn about a subset of these topics. This paper presents the revised version of the Open Computing course in the University of Zagreb, Faculty of Electrical Engineering and Computing (FER), which teaches a clear set of current topics focused on open data and correlating concepts. The new e-learning course is carried out using the “flipped classroom” educational method ; students construct their knowledge in a set of real-world mini-activities throughout the course, instead of passively learning from the official course resources. In this paper, we discuss our flipped classroom activities, their relation to revised Bloom’s taxonomy of educational objectives, and present the evaluation of the first course instance us- ing this model. Students’ feedback shows they welcome this change in approach, finding the course useful and interesting, and preferring this method to the traditional full lecture setting.

opencc-by-4.0Apr 2023View details →

Data set - Measured in a context : making sense of open access book data

For more than a decade, open access book platforms have been distributing titles in order to maximise their impact. Each platform offers some form of usage data, showcasing the success of their offering. However, the numbers alone are not sufficient to convey how well a book is actually performing. Our data set is consists of 18,014 books and chapters. The selected titles have been added to the OAPEN Library collection before 1 January 2022, and the usage data of twelve months (January to December 2022) has been captured. During that period, this collection of books and chapters has been downloaded more than 10 million times. Each title has been linked to one broad subject and the title’s language has been coded as either English, German or other languages. The titles are rated using the TOANI score. The acronym stands for Transparent Open Access Normalised Index. The transparency is based on the application of clear regulations, and by making all data used visible. The data is normalised, by using a common scale for the complete collection of an open access book platform. Additionally, there are only three possible values to score the titles: average, less than average and more than average. This index is set up to provide a clear and simple answer to the question whether an open access book has made an impact. It is not meant to give a sense of false accuracy; the complexities surrounding this issue cannot be measured in several decimal places. The TOANI score is based on the following principles: <ul> <li>Select only titles that have been available for at least 12 months;</li> <li>Use the usage data of the same 12 months period for the whole collection;</li> <li>Each title is assigned one – high level – subject;</li> <li>Each title is assigned one language;</li> <li>All titles are grouped based on subject and language;</li> <li>The groups should consists of at least 100 titles;</li> <li>The following data must be made available for each title: <ul> <li>Platform</li> <li>Total number of titles in the group</li> <li>Subject</li> <li>Language</li> <li>Period used for the measurement</li> <li>Minimum value, maximum value, median, first and third quartile of the platform’s usage data</li> </ul> </li> <li>Based on the previous, titles are classified as: <ul> <li>“Less than average” – First quartile; 25 % of the titles</li> <li>“Average” – Second and third quartile; 50% of the titles</li> <li>“More than average” – Fourth quartile; 25 % of the titles</li> </ul> </li> </ul>

Open Access on GNSS Permanent Networks Data in Case of Disaster

Earthquakes, as a natural phenomenon causing large physical and social destruction, are the subject of intensive research throughout the world. Spurred by the fact that in year 2020, two catastrophic earthquakes hit Croatia, in March with epicenter near Zagreb and December with epicenter near Petrinja, at the Faculty of Geodesy, University of Zagreb activities were initiated with the aim of strengthening the ability to react in these situations. Focus of those activities is on providing fast, adequate, and complete information on the disaster in the field of geodesy and geoinformatics. The research was focused on interpretation of kinematics of surface motion during the earthquake itself for what high rate permanent GNSS (Global Navigation Satellite System) network stations registrations are necessary. The Croatian earthquakes experience as well as the Mexico (June 2020) and Samosa earthquake (October 2020), pointed out, related to the use of high-rate registration GNSS data, that the primary problem in the use of this data is open access to the data itself. That is why this study has been launched - to gain a global picture of the availability of data from permanent GNSS networks around the world. The research included the collection and processing of information on open access policies for permanent GNSS networks data in the event of natural disasters with an emphasis on earthquakes. A global survey of institutions around the world responsible for managing GNSS permanent networks has been conducted. The survey contains three groups of questions that include general information on the type of permanent networks, models of access to network data and the readiness of countries to reach an international agreement on the opening data of the GNSS network in the event of a disaster. The results indicated that a high percentage of countries participating in the survey were ready to agree to open the data and introduce a common international portal through which scientists and researchers would be able to download GNSS permanent network data free of charge in the event of natural disasters.

Quality Assurance Awareness in Open Source Software Projects on GitHub Analysis Dataset

Dataset for the paper "Quality Assurance Awareness in Open Source Software Projects on GitHub", submitted to the 23rd IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM), 2023.

Open Data of the article "Literal vs. default translation. Challenging the constructs with Middle Egyptian translation as an extreme case in point"

The file contains the datasets and the code generated for the analyses presented in the article "Literal vs. default translation. Challenging the constructs with Middle Egyptian translation as an extreme case in point", to be published in Sendebar in 2023.   Abstract of the article: This paper presents the results of a study that compares the constructs of literal translation (Schaeffer & Carl, 2014) and default translation (Halverson, 2019) by means of an observational, exploratory study with Middle Egyptian translation as an extreme case in point. Two graduating students of the MA in Egyptology at Universitat Autònoma de Barcelona and three recent graduates of the same MA took part in the study. They translated two excerpts from two Middle Egyptian literary texts into Spanish. InputLog was used to collect translation-process data and derive word-level indicators of cognitive effort (typos per word, word typing speed, and within-word pause) from them. Results showed a clear link between default translations and cognitive effort (low number of typos, low number of respites, and fast writing speed). However, the assumption that deviations from literality cause greater cognitive effort was not observed. Hence, default translation may serve as a more adequate construct to describe the regular way translators perform.

Stereo PIV measurement of open channel flows in RA8 flume at the University of Sheffield

Stereo PIV measurement of open channel flows in RA8 flume at the University of Sheffield Six flow conditions over a rough bed of spheres with 24mm diameter. PIV plane was at the centerline of the flow shining through the bed of spheres. Where the laser PIV plane shone up, the spheres were replaced with translucent hollow spheres to allow the light to go through. Gradient of the flow was 0.001. <table> <tbody> <tr> <td>Water Depth</td> <td>Flow rate</td> <td>Velocity</td> <td>Reynolds’ number</td> <td>Manning’s number</td> <td>Froude number</td> <td>Weber number</td> <td>Relative Submergence</td> </tr> <tr> <td>(mm)</td> <td>(l/s)</td> <td>(m/s)</td> <td>(with depth)</td> </tr> <tr> <td>49</td> <td>1.87</td> <td>0.08</td> <td>3,740</td> <td>0.049</td> <td>0.11</td> <td>3.96</td> <td>2.04</td> </tr> <tr> <td>69</td> <td>5.05</td> <td>0.15</td> <td>10,100</td> <td>0.031</td> <td>0.18</td> <td>20.53</td> <td>2.88</td> </tr> <tr> <td>89</td> <td>7.46</td> <td>0.17</td> <td>14,920</td> <td>0.031</td> <td>0.18</td> <td>34.74</td> <td>3.71</td> </tr> <tr> <td>109</td> <td>11.21</td> <td>0.21</td> <td>22,420</td> <td>0.028</td> <td>0.2</td> <td>64.05</td> <td>4.54</td> </tr> <tr> <td>129</td> <td>15.4</td> <td>0.24</td> <td>30,800</td> <td>0.026</td> <td>0.21</td> <td>102.14</td> <td>5.38</td> </tr> <tr> <td>149</td> <td>20.7</td> <td>0.28</td> <td>41,400</td> <td>0.023</td> <td>0.23</td> <td>159.77</td> <td>6.21</td> </tr> </tbody> </table>

opencc-by-4.0May 2023View details →

Acoustic measurements in the ancient open-air theatre of Tyndaris

The data set represents the measured impulse response in the ancient Theatre of Tyndaris performed  in September 2015 by the Applied Acoustics Research Group of the Department of Energy of the Politecnico di Torino. - Measurement set-up has been described in https://www.mdpi.com/2076-3417/10/16/5680 - Receiver and source positions have been shown in Figure 2 in https://www.mdpi.com/2076-3417/10/16/5680

HeatResilientCity II - work package 2.3: Interactions between buildings and open space adaptation measures – Meteorological input data for building performance simulation

This repository contains meteorological data from urban climate simulations that were carried out in districts of the cities of Dresden and Erfurt as part of the <a href="http://heatresilientcity.de/">HeatResilientCity II</a> project. The data was extracted at specific points (receptors) of the urban climate model. In addition to the data, a script is attached that can be utilized to generate a time series for IDA ICE building performance simulations using IceWeather.exe. Therefore, a Microsoft Windows operating system is required. To create a time series, simply use the function createIdaIceInput() at the end of the script createTimeSeries.py. Further explanations can be found at the beginning of the script. Information about the ENVI-met data used to create the IDA ICE input can be found in README_RawENVImetOutput_DD.txt and README_RawENVImetOutput_EF.txt. Some input data files have already been generated and can be directly used for thermal building performance simulations with IDA ICE. These files can be found in the folder 0.3_Input_Timeseries (Climate) for IDA ICE. The naming convention of the final input data files for IDA ICE is as follows: <ul> <li>TOWN_SCENARIO_RECEPTOR_AVERAGING_INTERFACE_LATITUDE_LONGITUDE_VERSION</li> <li>TOWN: Choose between 'Erfurt' and 'Dresden'</li> <li>SCENARIO: See further information in README_RawENVImetOutput_DD.txt and README_RawENVImetOutput_EF.txt</li> <li>RECEPTOR: Location in the modelled area (ENVI-met simulation) where data was extracted.</li> <li>AVERAGING: Information about averaging the hourly values of the urban climate simulation (see createTimeSeries.py and READMEs)</li> <li>INTERFACE: Information on how single days were joined together (see createTimeSeries.py).</li> <li>LATITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function setIceWeatherParams() if you are using other cities/custom ENVI-met simulation data.</li> <li>LONGITUDE: Default values for Dresden and Erfurt are set in the script. Add additional values in the function setIceWeatherParams() if you are using other cities/custom ENVI-met simulation data.</li> <li>VERSION: The version number can be set in the script.</li> </ul> Example: Dresden_2y_A1_a_timeSeries_24-24_51.0468_13.6707_v11.prn Folder overview: <ul> <li>The ENVI-met raw data is stored in 0.1_Input_RawENVImetOutput.</li> <li>The script is stored in 0.2_Input_ScriptsToCreateTimeSeries.</li> <li>The final datasets ready for simulation with IDA ICE are stored in 0.3_Input_Timeseries(Climate)ForIDAICE. This folder also contains some weather data time series that have already been created and can be used for IDA ICE (subfolders Erfurt_v11 and Dresden_v11).</li> </ul>

Deep-SDMs in the open oceans - OUTPUTS - World +2°C

This repository contains global distribution maps with a +2°C increase in sea surface temperature, on 4 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a> This deposit contains: 1. A 00-predictions.csv file containing the raw outputs of the model. 2. Distribution maps as png files (named after taxon and date). 3. Distribution maps as GeoTIFF rasters (zipped in 01-geotiff-rasters.zip). Each of these elements can be downloaded separately by scrolling to the Files section.

Deep-SDMs in the open oceans - OUTPUTS - Western Indian Ocean

This repository contains distribution maps for the Western Indian Ocean on 53 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a> This deposit contains: 1. A 00-predictions.csv file containing the raw outputs of the model. 2. Distribution maps as animated gifs. 3. Distribution maps as GeoTIFF rasters (zipped in 01-geotiff-rasters.zip). Each of these elements can be downloaded separately by scrolling to the Files section.

Deep-SDMs in the open oceans - OUTPUTS - World

This repository contains global distribution maps on 4 dates in 2021, as described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a> This deposit contains: 1. A 00-predictions.csv file containing the raw outputs of the model. 2. Distribution maps as png files (named after taxon and date). 3. Distribution maps as GeoTIFF rasters (zipped in 01-geotiff-rasters.zip). Each of these elements can be downloaded separately by scrolling to the Files section.

Deep-SDMs in the open oceans - INPUT DATA

This repository contains input files to train the Deep-SDM model described in the preprint <a href="https://doi.org/10.1101/2023.08.11.551418">Predicting species distributions in the open oceans with convolutional neural networks.</a> This deposit contains: 1. Training data: CSV dataset + 38 subfolders with data for each species (named after its GBIF id) 2. Prediction data: 2.1. Global use case (solstices & equinoxes of 2021): CSV dataset + data folder 2.2. Western Indian Ocean use case: CSV dataset + data folder 3. species.csv contains the taxonomic name of each taxon, as well as its GBIF id. 4. stats.npy contains normalization factors for the data files <pre><code class="language-python">meds, perc1, perc99 = np.load("stats.npy") item = np.load(file)[:,:,:25] real_values = (perc99 - perc1) * item + perc1</code></pre>   Each of these elements can be downloaded separately by scrolling to the Files section.