Find research datasets worth reusing

Data for the publication "Repurposing weather modification for cloud research showcased by ice crystal growth"

This repository contains the data of the publication: "Repurposing weather modification for cloud research showcased by ice crystal growth" Ramelli et al., 2024, PNAS Nexus

opencc-by-4.0Aug 2024View details →

Colony forming unit (CFU) data accompanying publication: Bacterial microbiome dynamics in commercial integrated aquaculture systems growing Ulva in abalone effluent water.

Excel sheet with colony forming unit (CFU) data from an abalone farm growing the green seaweed Ulva in abalone effluent water. This dataset characterises the bacterial communities isolated from abalone effluent water entering and leaving the Ulva raceways, as well as from Ulva itself. Dataset is accompanied by two SigmaPlot files showing statistical analyses (statistical outcomes also available in publication).

Public Procurement: a systemic approach to sustainability and technology – SAPIENS Network Webinar Series

On the 5th of June 2024, the SAPIENS Network Early Stage Researchers <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/sap-in-construction/">Alexandru Buftic</a> (Babeș-Bolyai University), <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/delivering-sap-through-collaboration/">Felippe Vilaça</a> (University of Gävle), <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/measuring-lcc/">Haitham Ghaida</a> (Hasselt University) and <a href="https://sapiensnetwork.eu/research/early-stage-researcher-projects/industry-4-for-sp/">Nadia Sava</a> (Babeș-Bolyai University) delivered a scientific webinar on the topic of Public Procurement: a systemic approach to sustainability and technology. The webinar was moderated by <a href="https://sapiensnetwork.eu/dacian-dragos/">Dacian C. Dragoș</a>, Professor of Administrative and European Law with the Public Administration and Management Department at Babes-Bolyai University, <a href="https://sapiensnetwork.eu/sebastien-lizin/">Sebastien Lizin</a>, Assistant Professor at the Faculty of Business Economics and vice-chair of the Department of Economics at Hasselt University and <a href="https://www.linkedin.com/in/roxana-vornicu-phd-49470347/">Roxana Vornicu</a>, PhD, Senior Lecturer at King´s College London, Centre of Construction Law, and Managing Partner of Sirbu & Vornicu Law. The webinar tackled various current challenges related to sustainability in public procurement. The presentations covered different topics, united by the concepts of sustainability and digitalisation in public procurement. The speakers highlighed the value of taking a systemic approach to sustainability in public procurement, the role of monitoring green and sustainable considerations in the process, the importance of EU legislation in the uptake of sustainability as well as effect of life cycle assessment reserch for sustainable building design.

Conceptualization of public data ecosystems

This dataset contains data collected during a study <a href="https://www.sciencedirect.com/science/article/pii/S0740624X23000989">"Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems"</a> conducted by Martin Lnenicka (University of Hradec Králové, Czech Republic), Anastasija Nikiforova (University of Tartu, Estonia), Mariusz Luterek (University of Warsaw, Warsaw, Poland), Petar Milic (University of Pristina - Kosovska Mitrovica, Serbia), Daniel Rudmark (Swedish National Road and Transport Research Institute, Sweden), Sebastian Neumaier (St. Pölten University of Applied Sciences, Austria), Karlo Kević (University of Zagreb, Croatia), Anneke Zuiderwijk (Delft University of Technology, Delft, the Netherlands), Manuel Pedro Rodríguez Bolívar (University of Granada, Granada, Spain). As there is a lack of understanding of the elements that constitute different types of value-adding public data ecosystems and how these elements form and shape the development of these ecosystems over time, which can lead to misguided efforts to develop future public data ecosystems, the aim of the study is: (1) to explore how public data ecosystems have developed over time and (2) to identify the value-adding elements and formative characteristics of public data ecosystems. Using an exploratory retrospective analysis and a deductive approach, we systematically review 148 studies published between 1994 and 2023. Based on the results, this study presents a typology of public data ecosystems and develops a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems, and develops a conceptual model of the evolutionary generation of public data ecosystems represented by six generations called Evolutionary Model of Public Data Ecosystems (EMPDE). Finally, three avenues for a future research agenda are proposed. This dataset is being made public both to act as supplementary data for "Understanding the development of public data ecosystems: from a conceptual model to a six-generation model of the evolution of public data ecosystems ", Telematics and Informatics*, and its Systematic Literature Review component that informs the study.  ***Description of the data in this data set*** PublicDataEcosystem_SLR.docx provides the structure of the protocol PDEtypes.png provides a typology of public data ecosystems PDE_conceptual_model.png provides a conceptual model of elements and formative characteristics that contribute most to value-adding public data ecosystems PublicDataEcosystem_SLR.xlsx, Spreadsheet#1 provides the list of results after the search over three indexing databases and filtering out irrelevant studies Spreadsheets #2 provides the protocol structure. Spreadsheets #3 provides the filled protocol for relevant studies. The information on each selected study - presented in PublicDataEcosystem_SLR.xlsx - was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information Descriptive Information <ul> <li>Article number</li> <li>A study number, corresponding to the study number assigned in an Excel worksheet</li> <li>Complete reference</li> <li>The complete source information to refer to the study (in APA style), including the author(s) of the study, the year in which it was published, the study's title and other source information.</li> <li>Year of publication</li> <li>The year in which the study was published.</li> <li>Journal article / conference paper / book chapter</li> <li>The type of the paper, i.e., journal article, conference paper, or book chapter.</li> <li> Journal / conference / book</li> <li> Journal article, conference, where the paper is published.</li> <li>  DOI / Website</li> <li> A link to the website where the study can be found.</li> <li>Number of words</li> <li>A number of words of the study.</li> <li>Number of citations in Scopus and WoS</li> <li>The number of citations of the paper in Scopus and WoS digital libraries.</li> <li>Availability in Open Access</li> <li>Availability of a study in the Open Access or Free / Full Access.</li> <li>Keywords</li> <li>Keywords of the paper as indicated by the authors (in the paper).</li> <li>Relevance for our study (high / medium / low)</li> <li>What is the relevance level of the paper for our study</li> </ul> Approach- and research design-related information <ul> <li>Approach- and research design-related information</li> <li>Objective / Aim / Goal / Purpose & Research Questions</li> <li>The research objective and established RQs.</li> <li>Research method (including unit of analysis)</li> <li>The methods used to collect data in the study, including the unit of analysis that refers to the country, organisation, or other specific unit that has been analysed such as the number of use-cases or policy documents, number and scope of the SLR etc.</li> <li>Study’s contributions</li> <li>The study’s contribution as defined by the authors</li> <li>Qualitative / quantitative / mixed method</li> <li>Whether the study uses a qualitative, quantitative, or mixed methods approach?</li> <li>Availability of the underlying research data</li> <li>Whether the paper has a reference to the public availability of the underlying research data e.g., transcriptions of interviews, collected data etc., or explains why these data are not openly shared?</li> <li>Period under investigation</li> <li>Period (or moment) in which the study was conducted (e.g., January 2021-March 2022)</li> <li>Use of theory / theoretical concepts / approaches? If yes, specify them</li> <li>Does the study mention any theory / theoretical concepts / approaches? If yes, what theory / concepts / approaches? If any theory is mentioned, how is theory used in the study? (e.g., mentioned to explain a certain phenomenon, used as a framework for analysis, tested theory, theory mentioned in the future research section).</li> </ul> Quality-related information <ul> <li>Quality concerns</li> <li>Whether there are any quality concerns (e.g., limited information about the research methods used)?</li> </ul> Public Data Ecosystem-related information <ul> <li>Public data ecosystem definition</li> <li>How is the public data ecosystem defined in the paper and any other equivalent term, mostly infrastructure. If an alternative term is used, how is the public data ecosystem called in the paper?</li> <li> Public data ecosystem evolution / development</li> <li>Does the paper define the evolution of the public data ecosystem? If yes, how is it defined and what factors affect it?</li> <li>What constitutes a public data ecosystem?</li> <li>What constitutes a public data ecosystem (components & relationships) - their "FORM / OUTPUT" presented in the paper (general description with more detailed answers to further additional questions).</li> <li>Components and relationships</li> <li>What components does the public data ecosystem consist of and what are the relationships between these components? Alternative names for components - element, construct, concept, item, helix, dimension etc. (detailed description).</li> <li>Stakeholders</li> <li>What stakeholders (e.g., governments, citizens, businesses, Non-Governmental Organisations (NGOs) etc.) does the public data ecosystem involve?</li> <li>Actors and their roles</li> <li>What actors does the public data ecosystem involve? What are their roles?</li> <li>Data (data types, data dynamism, data categories etc.)</li> <li>What data do the public data ecosystem cover (is intended / designed for)? Refer to all data-related aspects, including but not limited to data types, data dynamism (static data, dynamic, real-time data, stream), prevailing data categories / domains / topics etc.</li> <li>Processes / activities / dimensions, data lifecycle phases</li> <li>What processes, activities, dimensions and data lifecycle phases (e.g., locate, acquire, download, reuse, transform, etc.) does the public data ecosystem involve or refer to?</li> <li>Level (if relevant)</li> <li>What is the level of the public data ecosystem covered in the paper? (e.g., city, municipal, regional, national (=country), supranational, international).</li> <li>Other elements or relationships (if any)</li> <li>What other elements or relationships does the public data ecosystem consist of?</li> <li>Additional comments</li> <li>Additional comments (e.g., what other topics affected the public data ecosystems and their elements, what is expected to affect the public data ecosystems in the future, what were important topics by which the period was characterised etc.).</li> <li>New papers</li> <li>Does the study refer to any other potentially relevant papers?</li> <li>Additional references to potentially relevant papers that were found in the analysed paper (snowballing).</li> </ul> ***Format of the file*** .xls, .csv (for the first spreadsheet only), .docx ***Licenses or restrictions*** CC-BY   For more info, see README.txt

Appendix Figures A.1 - A.15 of the paper "Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data". This work has been accepted for publication in the journal Astronomy & Astrphysics (A&A) on September 24, 2024.

Figure captions:   Fig. A.1. Heatmap with the number of common stars (true positives) labeled as binary for the five methods used.   Fig. A.2. Heatmap with the number of common stars (true negatives) labeled as single for the five methods used.   Fig. A.3. Color-magnitude diagrams, showing the 2815 stars of our sample from Sect. 3. Colors indicate the label predictions by SOM (left panel) and CNN (right panel).   Fig. A.4. K-S test comparing radial SOM (black) and CNN (blue).   Fig. A.5. Spectra of the star "LAMOSTJ112914.11+471501.7" (blue color) and in the background (gray color) the 35 stars classified as binary by Solano et al. (2022) with VOSA tools.   Fig. A.6. Spectra of the star "HD14829" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).   Fig. A.7. Spectra of the star "Feige98" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).   Fig. A.8. Spectra of the star "PG0304+184" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).   Fig. A.9. Spectra of the star "PG1510+635" (red color) and in the background (gray color) the 53 stars classified as single by Drilling et al. (2013).   Fig. A.10. Cluster 0 of spectra (blue color) with the other spectra in the background (gray color).   Fig. A.11. Cluster 4 of spectra (brown color) with the other spectra in the background (gray color).   Fig. A.12. Cluster 1 of spectra (yellow color) with the other spectra in the background (gray color).   Fig. A.13. Cluster 3 of spectra (red color) with the other spectra in the background (gray color).   Fig. A.14. Cluster -1 of spectra (pink color) with the other spectra in the background (gray color).   Fig. A.15. Cluster 2 of spectra (green color) with the other spectra in the background (gray color).

Supplementary material to the publication entitled "Digital transformation at what cost? A case study from Germany estimating the adoption potential of precision farming technologies under different scenarios" in Smart Agricultural Technology, https://doi.org/10.1016/j.atech.2024.100585

The file 'PAT_Descriptions_Assumptions_Supplementary Material.pdf' contains descriptions of the selected Precision Agricultural Technologies (PATs) and detailed explanations of the assumptions made in the calculation model.   The file 'Calculation Model_NUTS3_BW.xlsx' includes the calculation model created for the publication.

MACHINE LEARNING ALGORITHMS FOR ANOMALY DETECTION IN PUBLIC DATA USING GITHUB AS AN EXAMPLE

This study explores the application of machine learning algorithms for detecting anomalies in GitHub data to enhance the evaluation of technological projects. The research aims to develop a robust methodology for identifying data anomalies, such as artificial activity spikes, that can distort project assessments. Methods such as Isolation Forest, One-Class SVM, and advanced deep learning techniques like autoencoders and GANs are employed to analyze and identify irregular patterns in GitHub repositories. The findings demonstrate that these algorithms effectively detect both obvious and subtle anomalies, offering reliable insights into project authenticity. The proposed conceptual model integrates these methods into a scalable system, enhancing transparency and accuracy in technological project evaluation. The novelty of this work lies in its comprehensive approach to analyzing GitHub data, combining traditional and deep learning techniques to improve the reliability of assessments, making it a significant contribution to the field.

opencc-by-4.0Aug 2024View details →

Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform

This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform".  Source Data Raw.zip has the entire data set used to generate the images. Source Data.zip contains the processed data  from "Source Data Raw.zip".    Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab. For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.

Dataset: BEYOND public consultation survey

This is the data file and questionairre for BEYOND public consultation survey.

Data for publication: 'Publication Patterns in the Humanities: Generational Shifts and Changing Research Agendas'

This repository contains data supporting the paper Publication Patterns in the Humanities: Generational Shifts and Changing Research Agendas. The study analyzes the publication behaviors of approximately 60,000 humanities scholars from Spanish-speaking countries, focusing on generational shifts and research topic trends. Due to copyright restrictions from Dialnet, raw data used in the study cannot be publicly shared. Included files: <ul> <li>archetypes_summary.csv - Percentiles of archetype attributes by research fields and their labels after clustering.</li> <li>authors_metrics_dialnet.tsv - Aggregated metrics for 60,063 authors from Dialnet used to build archetypes (anonymized data).</li> <li>basemap_network.json - Basemap of the thematic landscape.</li> <li>topic_descriptions.tsv - Titles and descriptions of the 1437 research topics.</li> </ul>

Dataset Public Opinion of UAE and Sentiment Analysis Process

This article explains the UAE public opinion towards Indonesia in the era of President Jokowi's administration. In the digital era, in IR studies, it must be understood that public opinion influences a country's foreign policy, including in the economic field. The research team crawled data from Twitter (X), a data set (raw data), to find out the UAE's public opinion. The raw data was then processed using SVM machine learning to find out the UAE public's positive, negative, and neutral levels regarding Indonesia. After that, the results of public opinion were compared with the UAE's and Indonesia's economic cooperation to determine whether there was a relationship between public opinion and the level of economic cooperation between the two countries. So, the data in this study are the UAE public opinion data collection, which involved tracking and analyzing public sentiment in various UAE-based media outlets and sentiment analysis processes.

Data for Publication - Farmer Preferences and Selection Criteria for Shade Trees in Robusta Coffee Agroforestry Systems in Tshopo Province, DRC

Data used for publication - "Farmer Preferences and Selection Criteria for Shade Trees in Robusta Coffee Agroforestry Systems in Tshopo Province, DRC"

Data from public granaries as a source of proxy data on grain harvests and weather extremes (Sušice region, Czech Republic)

This deposit contains eight .xlsx files related to the study of public granary data and their relation to grain harvests and weather extremes in the Su&scaron;ice region (Czech Republic). In the study, annual values of grain borrowed by serfs, their grain depositions, the total grain storage and the total debt of serfs at the end of year were used to calculation of weighted grain indices considering the balance between borrowed and returned grain: a weighted bad harvest index (WBHI), a weighted good harvest index (WGHI), a weighted stored grain index (WSGI: WSGI-, more borrowed than returned; WSGI+, more returned than borrowed) and a weighted serf debt index (WSDI: WSDI+, more borrowed than returned grain; WSDI-, more returned than borrowed grain). WBHI, WSGI- and WSDI+ were used to select years of extreme bad harvest and WGHI, WSGI+ and WSDI- years of extreme good harvest. Selected extreme harvest years were tested against documentary weather data and reconstructed temperature, precipitation and drought series of the Czech Lands. First six files (01a_borrowed grain_plus, 01b_returned grain_plus, 01c_stored grain_minus, 01d_stored grain_plus, 01e_serfs debt_plus, 01f_serfs debt_minus) contain three sheets representing individual cereals (rye, oats and barley). For each cereal, there are series of multiples of standard deviation used for calculation of individual indices mentioned above. These multiples were obtained from detrended series (using high-pass filter) of every grain characteristic as arithmetic mean plus/minus corresponding multiple of standard deviation. The file &bdquo; 02_indices“ contains three sheets with weighted grain indices: WBHI, WGHI, WSGI-, WSGI+, WSDI+, WSDI-. Sheets represent individual cereals – rye, oats and barley. The file &bdquo; 03_clima factors“ contains mean seasonal (DJF, MAM, JJA, and SON) temperature, precipitation and scPDSI for the period 1789–1849, expressed in deviations relative to the 1961–1990 reference period. The series are reconstructed temperatures for central Europe (Dobrovolný et al., 2010), reconstructed precipitation for the Czech Lands (Dobrovolný et al., 2015) and both of them were used for creation of scPDSI series (Brázdil et al., 2016).

MULTIPLIERS_WP5_Science learning project on Forest use vs. forest protection_UBO_Public data_20241014_v1

This dataset contains the following data related to the science learning project on Forest use vs. forest protection: <ul> <li>Summary of transcripts from interviews with OSC members, and student groups (Pseudo-/Anonymised)</li> </ul>

MULTIPLIERS_WP5_Science learning project on Biodiversity and agriculture_UBO_Public data_20241014_v1

This dataset contains the following data related to the science learning project on Biodiversity and agriculture: <ul> <li>Summary of transcripts from interviews with teachers, and OSC members (Pseudo-/Anonymised)</li> </ul>

Processed data used for JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations"

This is the processed dataset used in the JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations" by Xiao Ge. Please contact the author (gexiao@tamu.edu) for all the original/processed outputs of R-CESM, and use the following original papers as citations. The dataset used in this research includes: 1. Loop Current Dynamics 2009-2011: LC_*.nc is the processed (reorganized) data for each in-situ station, * represents their station ID <ul> <li>https://digital.library.unt.edu/ark:/67531/metadc955416/</li> <li>https://www.sciencedirect.com/science/article/pii/S0377026516301348?via%3Dihub</li> <li>https://search.dataone.org/view/%7BBD2513E6-3B34-4B7C-BCB9-3C4ED5E8D0FB%7D</li> </ul> 2. Regional Community Earth System Model, R-CESM: <a href="https://zenodo.org/api/records/13932074/draft/files/h.nc/content" target="_blank" rel="noopener noreferrer">h.nc</a> is the bathymetry data of R-CESM; cmpr_*.nc files are provided as examples of the original R-CESM outputs; pvsf_prho_*.nc are the processed (subsampled at the target region and interpolated on potential density layers, derived stream function, potential vorticity, and relative vorticity) R-CESM outputs used in this research; and <a href="https://zenodo.org/uploads/13932074" target="_blank" rel="noopener noreferrer">LC_pv_40hlp_2013.nc</a> is the example of organized processed R-CESM (pvsf_prho_*.nc files) containing potential vorticity and relative vorticity for figures plotting <ul> <li>https://journals.ametsoc.org/view/journals/bams/102/9/BAMS-D-20-0024.1.xml?tab_body=fulltext-display</li> </ul>

Dataset for publication: "Magnesium and Aluminum in Contact with Liquid Battery Electrolytes: Ion Transport through Interphases and in the Bulk"

<div> </div> <div> This is the experimental raw data set associated with the following publication: M. Löw, J. Grill, MM May, and J. Popovic-Neuber, Magnesium and Aluminium in Contact with Liquid Battery Electrolyte: Ion Transport through Interphases and in the Bulk, ACS Material Letters (2024). DOI:10.1021/acsmaterialslett.4c01589 The data set is organized according to the publication's figures.  </div>

Dataset to accompany publication "Photodeposition-Based Synthesis of TiO2@IrOx Core-Shell Catalyst for Proton-Exchange Membrane Water Electrolysis with Low Iridium Loading"

<h2>Dataset description</h2> This dataset provides the raw data for the manuscript "Photodeposition-Based Synthesis of TiO2@IrOx Core-Shell Catalyst for Proton-Exchange Membrane Water Electrolysis with Low Iridium Loading" published in Advanced Science on 14 June 2024 (DOI: <a href="https://doi.org/10.1002/advs.202402991">https://doi.org/10.1002/advs.202402991</a>). The data consists of: <ol> <li>XRD pattern of TiO2@IrOx (40 wt% Ir) as shown in Fig. 3e.</li> <li>XPS spectra of 3 samples: 2.1 TiO2@IrOx (40 wt% Ir) as shown in Fig. 3f.; 2.2 TiO2@IrOx (only shell) as shown in Fig. 3g; 2.3 TiO2@IrOx (photodeposited seeds) as shown in Fig. S8.</li> <li>Datasets for NanoCT of the TiO2@IrOx catalyst layer as shown in Fig. 5 a-c : 3.1 HRES Tilt series; 3.2 reconstructed slices.</li> </ol> <h2>Abstract</h2> The widespread application of green hydrogen production technologies requires cost reduction of crucial elements. To achieve this, a viable pathway to reduce the iridium loading in proton exchange membrane water electrolysis (PEMWE) is explored. Herein, we present a scalable synthesis method based on a photodeposition process for a TiO2@IrOx core-shell catalyst with a reduced iridium content as low as 40 wt%. Using this synthesis route, we obtain titania support particles homogeneously coated with a thin iridium oxide shell of only 2.1 ± 0.4 nm. The catalyst exhibits not only high ex situ activity, but also decent stability compared to commercially available catalysts. Furthermore, the unique core-shell structure provides a threefold increased electrical powder conductivity compared to structures without the shell. In addition, the low iridium content facilitates the fabrication of sufficiently thick catalyst layers at decreased iridium loadings mitigating the impact of crack formation in the catalyst layer during PEMWE operation. We demonstrate that the novel TiO2@IrOx core-shell catalyst clearly outperforms the commercial reference in single-cell tests with an iridium loading below 0.3 mgIr cm‑2 exhibiting a superior iridium-specific power density of 17.9 kW gIr-1 compared to 10.4 kW gIr-1 for the commercial reference.

Images for a publication "Lossless Hyperspectral Image Compression in Comet Interceptor and Hera Missions with Restricted Bandwith" by Skog et al.

This archive contains following simulated hyperspectral datacubes for ASPECT (Asteroid Spectral Imager) instrument on ESA Hera mission used in the manuscript: <ul> <li>ASPECT_simulated_data_Vis.zip - ASPECT visible channel datacubes noisless and with simulated instrument noise for three exposure times indicated in the file name.</li> <li>ASPECT_simulated_data_NIR.zip - ASPECT near-infrared channels datacubes noisless and with simulated instrument noise for three exposure times indicated in the file name.</li> <li>ASPECT_simulated_data_Vis_NIR_filtered.zip - Same ASPECT visible and near-infrared channels datacubes filtered with noise filters indicated in the file name. The visible datacubes have designation Vis in file name. The remaining datacubes without channel designation are near-infrared ones.</li> </ul> The recorded scene and imaging distance is indicated in file name. D1 - Didymos asteroid, D2 - Dimorphos asteroid. All datacubes are in Matlab (.mat) format and with single wavelength .png preview included. For details please check associated manuscript.

MULTIPLIERS_WP3/4/5_Science learning project on Anti-microbial resistance_Public data_UCY_20241018_v1

This dataset contains the following data related to the science learning project on Anti-microbial resistance: <ul> <li>Summary of transcripts from interviews with teachers and students (Pseudo-/Anonymised)</li> <li>Summary of transcripts from focus group discussions with students (Pseudo-/Anonymised)</li> </ul>