Find research datasets worth reusing

opencc-by-4.0Jan 2020View details →

Carbon dioxide, methane, and chemical data from Batang Ai reservoir

The dataset contains biogeochemical in situ field measurements taken in Batang Ai reservoir (located on the Borneo Island, Malaysia). Samples were taken over four sampling campaigns from 2016 to 2018. Data was used to analyse carbon dioxide and methane flux patterns and to calculate the carbon footprint of the reservoir in the paper: “The carbon footprint of a Malaysian tropical reservoir: measured versus modeled estimates highlight the underestimated key role of downstream processes” (<a href="https://doi.org/10.5194/bg-17-1-2020">https://doi.org/10.5194/bg-17-1-2020</a>). Data were also used to calculate budgets of CO2 and CH4 in the epilimnion of Batang Ai reservoir in the paper: “Changing sources and processes sustaining surface CO2 and CH4 fluxes along a tropical river to reservoir system” (<a href="https://doi.org/10.5194/bg-2020-258">https://doi.org/10.5194/bg-2020-258</a>).

opencc-by-4.0Jan 2024View details →

Data inputs and results from AI-supported title and abstract screening "Lack of evidence regarding markers identifying acute heart failure in patients with COPD: an AI-supported systematic review"

These comma-separated data files were used to conduct the AI supported screening of [Lack of Evidence Regarding Markers Identifying Acute Heart Failure in Patients with COPD: An AI-supported Systematic Review (working title)], following the methodology described in the publication (URL/doi to be uploaded). These files provide insight into the AI-supported screening process and the choices made by the human reviewer.

opencc-by-4.0Oct 2024View details →

First discovery and confirmation of PN candidates found from AI and deep learning techniques applied to VPHAS+ survey data

<div> <div> <div> <div> <div> <div> Appendices: VPHAS+ detected PNG images labelled by red boxes, SHS Hα-Rband quotient images, VPHAS+ Hα-Rband quotient images, SAAO spectra with spectral lines labelled. </div> </div> </div> Table: Parameters of the observed PN candidates and any associated nebulosity or outflows. Reduced spectra. </div> </div> </div>

opencc-by-4.0Jun 2021View details →

Environmental and AIS data collected during the EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network testbed

Environmental and AIS data collected during the H2020 project EUMarineRobots Trans-National Access activities experiments using the NATO STO-CMRE Littoral Ocean Observatory Network (LOON) testbed. Environmental data consists of temperature measured across the water column; sound velocity measured close to the surface and close to the sea bottom; meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain). The environmental dataset is complemented with Automatic Identification System (AIS) data for the ships transiting close to  the LOON area (Gulf of La Spezia, Italy) Temperature measured across the water column in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for: i) Nov 12, 19-20, 23-24 - 2020 ii) Dec 1-4, 14-20 - 2020 iii) Jan 12-13, 15, 18-24, 27-28 - 2021 Meteorological data at the surface (i.e., pressure, temperature, wind speed and direction, humidity and rain) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for: i) Nov 12, 19-20, 23-24 - 2020 ii) Dec 1-4, 14-20 - 2020 iii) Jan 12-13, 15, 18-24, 27-28 - 2021 Sound velocity measured close to the surface (SVP1) and close to the sea bottom (SVP2) in the LOON area (Gulf of La Spezia, Italy). The dataset includes measurements for: i) Nov 12, 19-20, 23-24 - 2020 ii) Dec 1-4, 14-20 - 2020 iii) Jan 12-13, 15, 18-24, 27-28 - 2021 SVP2 data  missing for  Dec 14-20 (2020) and Jan 24, 27-28 (2021). Automatic Identification System (AIS) data for the ships transiting close to  the LOON area (Gulf of La Spezia, Italy). The dataset includes AIS data for: i) Nov 12, 19-20, 23-24 - 2020 ii) Dec 1-4, 14-20 - 2020 iii) Jan 12-13, 15, 18-24, 27-28 - 2021   For reference, see: "Environmental data collected on the CMRE LOON tested during the EUMR project: dataset description",  Petroccia, Roberto; Zappa, Giovanni; Cimino, Giampaolo; Grati, Alberto; Alves, João. CMRE-DA-2021-001. July 2021, available  at https://www.cmre.nato.int/research/publications/latest-techreports/1638-cmre-da-2021-001

opencc-by-4.0Jun 2023View details →

AIS data

Terrestrial vessel automatic identification system (AIS) data was collected around Ålesund, Norway in 2020, from multiple receiving stations with unsynchronized clocks. Features are 'mmsi', 'imo', 'length', 'latitude', 'longitude', 'sog', 'cog', 'true_heading', 'datetime UTC', 'navigational status', and 'message number'. Compact parquet files can be turned into data frames with python's pandas library. Data is irregularly sampled because of the <a href="https://imorules.com/GUID-D7E2DECA-C42B-419D-B613-9D03236FA4F1.html">navigational status</a>. The preprocessing script for training the machine learning models can be found <a href="https://github.com/WenjieDu/TSDB">here</a>. There you will find gathered dozen of trainable models and hundreds of datasets. Visit <a href="https://www.kystverket.no/navigasjonstjenester/ais/tilgang-pa-ais-data/">this</a> website for more information about the data. If you have additional questions, please find our information in the links below: <ul> <li><a href="https://www.ntnu.no/ansatte/luka.grgicevic">Luka Grgičević</a></li> <li><a href="https://www.ntnu.no/ansatte/ottar.osen">Ottar Laurits Osen</a></li> </ul>

opencc-by-4.0Oct 2023View details →

HITS Inc.'s models and data for Dacon challenge, Jump AI 2023

Here deposits model and data files developed during HITS Inc.'s participation in the Dacon challenge, Jump AI 2023: <a href="https://dacon.io/competitions/official/236127/overview/description">https://dacon.io/competitions/official/236127/overview/description</a> Note that the files here alone are less useful unless appropriate codes are employed. File description: <ul> <li>pred_model_AutoGluon.tar.xz: AutoGluon model parameters for prediction.</li> <li>valid_model_AutoGluon.tar.xz: AutoGluon model parameters for validation.</li> <li>ckpts_original.tar.xz: fine-tuned <a href="https://github.com/yuyangw/MolCLR">MolCLR</a> model parameters.</li> <li>qc_out.tar.xz: molecular electronic structure files (.wfn).</li> <li>sdf_optimized.tar.xz: molecular structure files (.sdf).</li> <li>atomwfn.tar.xz: atomic electronic structure files (.wfn).</li> </ul>

opencc-by-4.0Jul 2023View details →

Dead Sea Scrolls data collection (images, labels, prediction plots) for dating ancient manuscripts using radiocarbon and AI-based writing style analysis

The dataset is associated with the following article: Title: Dating ancient manuscripts using radiocarbon and AI-based writing style analysis Authors: Mladen Popović, Maruf A. Dhali, Lambert Schomaker, Johannes van der Plicht, Kaare Lund Rasmussen, Jacopo La Nasa, Ilaria Degano, Maria Perla Colombini, and Eibert Tigchelaar (Under review) This data set is collected for the ERC project: The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls PI: Mladen Popović Grant agreement ID: 640497 Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a>   Copyright (c)     University of Groningen, 2024. All rights reserved. Disclaimer and copyright notice for all data contained on the *.tar.gz files: 1) permission is hereby granted to use the data for research purposes. It is not allowed to distribute this data for commercial purposes. 2) provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed. 3) provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this data. 4) the user should refer to the first public article mentioned above on this data set. 5) the recipient should refrain from proliferating the data set to third parties external to his/her local research group. Please refer interested researchers to this site to obtain their own copy.   Organization of the data: (Update on 19 April 2024: OxCal data for accepted 2-sigma ranges are updated with the incusion and exclusion of minor peaks. New prediction plots are added after the model is trained with accepted 2-sigma ranges, including minor peaks. The old plots are also kept.  <OLD Updates below; disregard> updated on 07-Feb-2024: OxCal data for selected ranges added in a new directory in addition to previously available original OxCal data. Enoch's prediction plots and test images are reorganized for easy access to the users. <OLD Updates above; disregard> Please use the files from this version and disregard the previous two versions: 10.5281/zenodo.10629480 and 10.5281/zenodo.8168210) There are four *.tar.gz files: C14-Oxcal-data-updated.tar.gz contains one directory with radiocarbon data (OxCal [1] raw data) for all 30 manuscripts. Three additional directories contain name-corrected files for original OxCal data, files with accepted ranges, and files with accepted ranges including minor peaks. Please refer to the original article for details about OxCal data and the manuscripts. 25 out of 30 raw OxCal data are used (accepted ranges only) as the training labels during the training of Enoch, the date prediction model. train-images-c14.tar.gz contains the clean and preprocessed (binarized, aligned, and arrangement corrected) training images for the 25 radiocarbon-dated training manuscripts (including 4Q52; 64 images in total).  test-images-all.tar.gz contains the clean and preprocessed test images for 135 previously undated manuscripts. The images are organized in three different directories: the first one with all 359 images for the 135 manuscripts, the second one with the selected 135 images, and the final one with 25 images to illustrate the poor quality of images.  Enoch-prediction-new-with-minor-peaks.tar.gz contains the new date prediction plots for each of the 135 test images,  where Enoch was trained with the inclusion of minor peaks for the 2-sigma accepted ranges and with a data balancing threshold of 0.05. These plots are used by expert palaeographers' evaluation of Enoch's style-based date predictions of 135 previously undated manuscripts. Enoch-predictions.tar.gz contains the date prediction plots for each of the 135 test images. There are two directories inside the *.tar.gz file: - prediction-plots-for-selected-135: Prediction plots with data balancing threshold of 0.05.  - extra-plots: contains four additional directories:      - Enoch-predictions-c14wo4Q52-balanced05: Prediction plots with data balancing threshold of 0.05.       - Enoch-predictions-c14wo4Q52-balanced10: Prediction plots with data balancing threshold of 0.1.      - Enoch-predictions-c14wo4Q52-unbalanced: Unbalanced raw predictions.      - Enoch-predictions-c14wo4Q52-combined: Combined plots with all three prediction plots (unbalanced, 0.05, 0.1). Please refer to the original article for more details. The updated code to run the plot is available here: <a href="https://doi.org/10.5281/zenodo.10998860">https://doi.org/10.5281/zenodo.10998860</a> If you have any questions, please get in touch with us: Mladen Popović <m.popovic(at)rug.nl> Maruf A. Dhali <m.a.dhali(at)rug.nl> Lambert Schomaker <l.r.b.schomaker(at)rug.nl>   References: 1. Bronk Ramsey, C. (2001). Development of the radiocarbon calibration program. Radiocarbon, 43(2A), 355-363.

opencc-by-4.0Sep 2024View details →

Data analysis Protocol for a Joint Study into the Impacts of AI on professional Competencies of IT Professionals and Implications for Computing Students. ITiCSE 2024 Working Group 02.

<h1><a name="_Toc169648661"></a>Overview</h1>   The purpose of this protocol is to help us define a common protocol for sharing and analysing data for the ITiCSE 2024 working group: “WG02: A Multi-Institutional-Multi-National Study into the Impacts of AI on Work Practices of IT Professionals and Implications for Computing Students”.  Excerpts from the working group plan to place the protocol in context (Clear et al., 2024) are given below.   Background and Related Work As Artificial Intelligence (AI) continues to make its presence felt in transforming workplaces around the world [1,10], and the Information Technology industry in particular, it is essential to understand its impact on the work practices of IT professionals, and the implications for computing students and curricula. This research project builds on work initiated jointly, in Sweden, New Zealand and Scotland, investigating concerns about the increasing impacts of Artificial Intelligence in IT Sector workplaces for employee work engagement [11,13,1] and the implications for tertiary study, assessment and curricula in computing [4, 8, 10, 9].  “Work engagement”, has been defined as the positive inner state where employees are fully present and engaged in their work, and is closely linked to motivation, learning, productivity, and accountability [11, 13]. Within the context of (Generative) AI at work, IT professionals have been noted as early adopters of AI [10, 1]. Their involvement in implementing and utilising AI technologies can provide valuable insights into the interplay between AI and work engagement.  The implications for students are significant as future IT professionals, who must acquire and enhance competencies to adapt and thrive in digital workplaces.   2           Goals of the Working Group By exploring the relationship between work engagement and learning, this study aims to shed light on the dynamics that drive employee engagement and its connection to the professional development of competencies. The previous study has interviewed IT professionals with the following research questions (RQ):   RQ1: How does AI influence work engagement for IT professionals? RQ2: How does AI affect the socio-technical work dynamics for IT professionals? RQ3: What are the implications of integrating AI on the acquisition and enhancement of professional competencies and the learning processes of IT professionals?   3           Methodology This working group aims to analyse the corpus of interview data collected from multiple countries to better understand the implications for computing students, tertiary computing education curricula and assessment of the new professional competencies emerging from this work. This study informed by the literature on work engagement, automation and motivation for IT professionals [11, 13], will use a combination of multi-vocal literature review [7] and qualitative research methods [2, 5], including thematic analysis of the interviews, to investigate the state of the practice in and challenges IT Professionals face within their local/global work contexts. The literature on professional competencies in computing [4, 3, 6] will be drawn upon to characterise the new needs identified in this analysis.  Further implications for computing curricula design and assessment will be developed from this analysis. REFERENCES [1]    ACM Technology Policy Council. 2023. Principles for the development, deployment, and use of generative AI technologies, ACM New York. [2]    Braun, V. and Clarke, V. 2021. One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative research in psychology, 18 (3). 328-352. [3]    Clear, A., Clear, T., Vichare, A., Charles, T., Frezza, S., Gutica, M., Lunt, B., Maiorana, F., Pears, A. and Pitt, F. 2020. Designing Computer Science  Competency Statements: A Process and Curriculum Model for the 21st Century in Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education, ACM, New York. [4]    Clear, A., Parrish, A. and CC2020 Task Force. 2020. Computing Curricula 2020 - CC2020 - Paradigms for Future Computing Curricula ACM and IEEE-CS eds. A Computing Curricula Series Report ACM, New York. [5]    Cruzes, D.S. and Dyba, T. 2011. Recommended steps for thematic synthesis in software engineering. in 2011 international symposium on empirical software engineering and measurement, IEEE, 2011, 275-284. [6]    Frezza, S., Clear, T. and Clear, A. 2020. Unpacking Dispositions in the CC2020 Computing Curriculum Overview Report in 2020 IEEE Frontiers in Education Conference (FIE), IEEE, Uppsala, Sweden. [7]    Garousi, V., Felderer, M., & Mäntylä, M. V. 2019. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. Information and Software Technology, 106. 101-121 [8]    Jacques, L. 2023. Teaching CS-101 at the Dawn of ChatGPT. ACM Inroads, 14 (2). 40-46. [9]    Liffiton, M., Sheese, B., Savelka, J. and Denny, P. 2023. CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes. arXiv preprint arXiv:2308.06921. [10]  Prather, J., Denny, P., Leinonen, J., Becker, B.A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T. and Luxton-Reilly, A. 2023. The robots are here: Navigating the generative ai revolution in computing education. arXiv preprint arXiv:2310.00658. [11]  Roto, V., Palanque, P. and Karvonen, H., 2019. Engaging automation at work–a literature review. in Human Work Interaction Design. Designing Engaging Automation: 5th IFIP WG 13.6 Working Conference, HWID 2018, Espoo, Finland, August 20-21, 2018, Revised Selected Papers 5, Springer, 158-172. [12]  SFIA Foundation. 2023. SFIA skills aligned to EU ICT Profiles, SFIA Institute, London. [13]  Sharp, H., Baddoo, N., Beecham, S., Hall, T. and Robinson, H. 2009. Models of motivation in software engineering. Information and software technology, 51 (1). 219-233.

opencc-by-4.0Dec 2021View details →

Supporting data for the AI education publication statistics in "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates"

Supporting data for the AI education publication statistics presented in the paper "An Experience Report of Executive-Level Artificial Intelligence Education in the United Arab Emirates" to be published at the Twelfth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-22). The data was used to plot the figure showing the cumulative number of publications from 1976 to 2020 relating to AI education.

opencc-by-4.0Oct 2021View details →

The Piraeus AIS Dataset for Large-scale Maritime Data Analytics

AIS data collected by the University of Piraeus' AIS receiver   Abstract The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9th, 2017 to December 26th, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus (Greece), one of the busiest ports in Europe and worldwide. The dataset consists of over 244 million AIS records, an average of more than 10,000 records per hour, which makes it an ideal input for large-scale mobility data processing and analytics purposes.   Dataset related to the following publication <blockquote> Andreas Tritsarolis, Yannis Kontoulis, Yannis Theodoridis, The Piraeus AIS dataset for large-scale maritime data analytics, Data in Brief, Volume 40, 2022, 107782, ISSN 2352-3409, <a href="https://doi.org/10.1016/j.dib.2021.107782">https://doi.org/10.1016/j.dib.2021.107782</a>. </blockquote>   Files Description <ul> </ul> <ul> <li>ais_static: CSV flat files containing vessels' static information and their corresponding types</li> </ul> <ul> <li>geodata: ESRI Shapefiles containing several geographic-related data (e.g. harbours, islands, etc.)</li> </ul> <ul> <li>noaa_weather: ESRI Shapefiles containing weather forecast from GRIB files (as provided by NOAA)</li> </ul> <ul> <li>unipi_ais_dynamic: CSV flat files containing AIS kinematic information </li> </ul> <ul> <li>unipi_ais_dynamic_synopses: CSV flat files containing metadata (i.e. synopses) regarding vessels' AIS positions</li> </ul>   Privacy Statement For privacy-related queries, please contact the authors

opencc-zeroNov 2023View details →

Global AI, ML, Data salaries

Dataset de Salarios en el Ámbito de Inteligencia Artificial y Ciencia de DatosEste conjunto de datos recopila información salarial de diversos puestos de trabajo vinculados a inteligencia artificial y a la ciencia de datos en todo el mundo. Es una adaptación del conjunto original salaries que pertenece a ai-jobs.net.Se ha ampliado con nuevas variables para ofrecer una visión más completa de las condiciones laborales. Las variables originales son:<ul><li>work_year: Año en que se pagó el salario (Categórica)</li><li>experience_level: Nivel de experiencia (EN: Junior, MI: Medio, SE: Senior, EX: Director/Ejecutivo) (Categórica)</li><li>employment_type: Tipo de contrato (PT: Tiempo parcial, FT: Tiempo completo, CT: Contrato de servicio, FL: Freelance) (Categórica)</li><li>job_title: Puesto de trabajo (Categórica)</li><li>salary: Salario bruto anual (Cuantitativa)</li><li>salary_currency: Divisa del salario bruto (Categórica)</li><li>salary_in_usd: Salario en USD (Cuantitativa)</li><li>employee_residence: País de residencia del empleado (ISO3166) (Categórica)</li><li>remote_ratio: Cantidad de teletrabajo (0: Presencial, 50: Híbrido, 100: Remoto) (Categórica)</li><li>company_location: País de la empresa (ISO3166) (Categórica)</li><li>company_size: Tamaño de la empresa (S: Menos de 50 empleados, M: Entre 50 y 250 empleados, L: Más de 250 empleados) (Categórica)</li></ul>Las nuevas variables incluidas son:<ul><li>company_continent: Continente donde se encuentra la empresa (Categórica)</li><li>employee_continent: Continente donde reside el empleado (Categórica)</li><li>company_continent_region: Región continental donde está ubicada la empresa (Categórica)</li><li>employee_continent_region: Región continental donde reside el empleado (Categórica)</li><li>salary_rounded_in_eur: Salario en euros redondeado (calculado con la media de cada año) (Cuantitativa)</li><li>salary_k_in_eur: Salario en miles de euros (Cuantitativa)</li><li>salary_type: Tipo de salario (VL: Very Low, L: Low, M: Medium, H: High, VH: Very High) (Categórica)</li><li>same_country: ¿Residencia y lugar de trabajo en el mismo país? (Categórica)</li></ul>

opencc-by-4.0Oct 2019View details →

Multi-Source Distributed System Data for AI-powered Analytics

Abstract: In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems. The major contributions have been materialized in the form of novel algorithms. Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms. Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance.   Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research. Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation. General Information: This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset. You may find details of this dataset from the original paper: Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics".  If you use the data, implementation, or any details of the paper, please cite!   BIBTEX: _________________________________________ <pre>@inproceedings{nedelkoski2020multi, title={Multi-source Distributed System Data for AI-Powered Analytics}, author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej}, booktitle={European Conference on Service-Oriented and Cloud Computing}, pages={161--176}, year={2020}, organization={Springer} } </pre> ___________________________ The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The sequential_data is generated via executing workload of sequential user requests. The concurrent_data is generated via executing workload of concurrent user requests. The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format. Important: The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data. Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: <a href="https://github.com/SashoNedelkoski/multi-source-observability-dataset/">https://github.com/SashoNedelkoski/multi-source-observability-dataset/</a>

opencc-by-4.0Jul 2023View details →

Hidden and total fishing activity hotspots in the Mediterranean Sea between 2017 and 2022 estimated from AIS data

Hidden and total fishing activity hotspots in the Mediterranean Sea between 2017 and 2022 estimated from AIS data

opencc-by-4.0Jul 2023View details →

Data for paper "The Two Faces of AI in Green Mobile Computing: A Literature Review"

This is the data associated with the literature review presented in the paper “The Two Faces of AI in Green Mobile Computing: A Literature Review” accepted at SEAA 2023.

opencc-by-4.0Dec 2019View details →

Artificial Intelligence (AI), in the Breast Cancer Screening Programme Questionnaire and Data

The goal of this study is to gain insight into the level of trust of women in the Netherlands, in the decisions made by radiologists with the support of different applications of Artificial Intelligence (AI), in the Breast Cancer Screening Programme. Gaining insight into your level of trust in the decisions made by radiologists and AI regarding whether you have breast cancer or not, is of high importance, as this will help to anticipate which steps can be taken by hospitals and software developers, in the near future. The survey consists of 10 introductory questions and 42 statements and takes approximately 10 minutes to complete.

opencc-by-4.0Nov 2023View details →

Generative AI in University Communication - Survey Data (June 2023)

Der Datensatz mit dem Titel "Generative KI in der Hochschulkommunikation - Umfragedaten (Juni 2023)" erfasst Informationen zur Einführung und Nutzung von generativer künstlicher Intelligenz (KI) im Kontext der Hochschulkommunikation. Die Umfrage, die im Juni 2023 unter 318 deutschen Hochschulen durchgeführt wurde, von denen 101 geantwortet haben, untersucht verschiedene Aspekte, darunter Bekanntheit und Wissen über verschiedene KI-Tools (z.B. ChatGPT), Diskussionen in Gremien, das Vorhandensein von Richtlinien für die Nutzung, das Vorhandensein von Arbeitsgruppen für generative KI, strategische Ziele und Initiativen, Schulungsangebote für generative KI-Tools und die wahrgenommene Bedeutung von generativen KI-Tools in der Hochschulkommunikation. Ziel des Datensatzes ist es, Einblicke in die aktuelle Landschaft und Praxis der Integration generativer KI im universitären Umfeld zu geben.The dataset, titled "Generative AI in University Communication - Survey Data (June 2023)," captures information related to the adoption and utilization of generative artificial intelligence (AI) in the context of university communication. This survey, conducted in June 2023 among 318 German universities of which 101 responded, explores various aspects, including awarenes and knowledge of various AI tools (e.g. ChatGPT), discussions in committees, the existence of guidelines for usage, the presence of working groups for generative AI, strategic goals and initiatives, training offerings for generative AI tools, and the perceived importance of generative AI tools in university communication. The dataset aims to provide insights into the current landscape and practices regarding the integration of generative AI within university settings. More information here: <a href="https://www.hof.uni-halle.de/projekte/hochki/">https://www.hof.uni-halle.de/projekte/hochki/</a>

opencc-by-4.0Dec 2023View details →

ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries – Data and Models

This repository contains data and models used in the following paper.   Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V., & Zou, J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. In review.   The data and models are meant to be used with the <a href="https://github.com/swansonk14/admet_ai">ADMET-AI</a> code, which runs the ADMET-AI web server at <a href="https://admet.ai.greenstonebio.com/">admet.ai.greenstonebio.com</a>.   The data.zip file has the following structure. data   drugbank: Contains files with drugs from the <a href="https://go.drugbank.com/">DrugBank</a> that have received regulatory approval. drugbank_approved.csv contains the full set of approved drugs along with ADMET-AI predictions, while the other files contain subsets of these molecules used for testing the speed of ADMET prediction tools.   tdc_admet_all: Contains the data (.csv files) and RDKit features (.npz files) for all 41 single-task ADMET datasets from the <a href="https://tdcommons.ai/">Therapeutics Data Commons</a> (TDC).   tdc_admet_multitask: Contains the data (.csv files) and RDKit features (.npz files) for the two multi-task datasets (one regression and one classification) constructed by combining the tdc_admet_all datasets.   tdc_admet_all.csv: A CSV file containing all 41 ADMET datasets from tdc_admet_all. This can be used to easily look up all ADMET properties for a given molecule in the TDC.   tdc_admet_group: Contains the data (.csv files) and RDKit features (.npz files) for the 22 TDC ADMET Benchmark Group datasets with five splits per dataset.   tdc_admet_group_raw: Contains the raw data (.csv files) used to construct the five splits per dataset in tdc_admet_group.   The models.zip file has the following structure. Note that the ADMET-AI website and Python package use the multi-task Chemprop-RDKit models below. models   tdc_admet_all: Contains Chemprop and Chemprop-RDKit models trained on all 41 single-task TDC ADMET datasets.   tdc_admet_all_multitask: Contains Chemprop and Chemprop-RDKit models trained on the two multi-task TDC ADMET datasets (one regression and one classification).   tdc_admet_group: Contains Chemprop and Chemprop-RDKit models trained on the 22 TDC ADMET Benchmark Group datasets.