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725 results for “Recommendation”
Dataset of the study "Exploring the Notion of Risk in Reviewer Recommendation"
<p><strong>Note: Please find the dockerized version of this replication package in the following link:</strong></p> <p><a href="https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255">https://figshare.com/articles/dataset/Replication_Package_of_the_study_Exploring_the_Notion_of_Risk_in_Reviewer_Recommendation_/20673255</a></p> <p> </p> <p>This repository contains the necessary data for replicating the necessary information to replicate the study of "Exploring the Notion of Risk in Reviewer Recommendation." This code extends the RelationalGit package (https://github.com/CESEL/RelationalGit) from the study of E. Mirsaeedi and P. C. Rigby [1] and adds some functionality that is needed to incorporate the concept of the fix-inducing likelihood of a project.</p> <p>In addition to our dataset, this repository also have the supporting materials for our study. The supporting materials are in the "ICSME_online_materials_ICSME.pdf" and contains the following items:</p> <ul> <li>Table 1 contains the detail of the dataset and some related statistics for each of the studied projects. </li> <li>Table 2 have risk measures that were used in our defect prediction model. We use Commit Guru Tool to extracts the data from the GitHub repositories and then use this data to train our defect prediction model.</li> <li>Figure 1 illustrates the distribution of predicted defect probability of different projects. This distribution shows how defect probability of different periods are similar to the adjacent periods. </li> </ul> <p><strong>References:</strong></p> <p>[1] E. Mirsaeedi and P. C. Rigby, ‘Mitigating turnover with code review recommendation: Balancing expertise, workload, and knowledge distribution’, στο <em>Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering</em>, 2020.</p> <p> </p>
NOAA NCCOS Assessment: Priority Areas Recommended for Shallow Coral Reef Management in the South Florida Coast from 2021-04-26 to 2021-05-21
<p>The National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process, and online application (Buja and Christensen 2019) to identify mapping needs along the south Florida coast to support shallow coral reef management by NOAA’s Coral Reef Conservation Program (CRCP). Eighteen participants from local federal, state, academic, and other institutions entered their priorities in an online participatory Geographic Information System (pGIS). Participants used virtual coins to denote their priorities in 10.4 km<sup>2</sup> hexagonal grid cells overlaid on the study area. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important, what data types were needed, and data collection methodologies using a pre-set list of options. Results were compiled, summarized, and mapped to identify high priority areas, reasons for those priorities, and information needs. Identifying these high priority areas provide a critical spatial framework for prioritizing mapping efforts in shallow coral reef ecosystems in south Florida.</p> <p>The overall goal of the project was to systematically gather and quantify suggestions for mapping needs to support management of shallow coral reef ecosystems along the coast of south Florida. This dataset supports these goals by compiling input from a diversity of regional experts on their recommended priorities for mapping data collection.</p> <p>An advisory group was established which included individuals from NOAA CRCP and NOAA Fisheries. This advisory team customized the pGIS process specifically to meet the needs of CRCP and local coral reef manager priorities. In the online pGIS, the study area was divided into 1761 hexagonal grid cells 10.4 km<sup>2</sup> in size. Existing relevant spatial datasets (<em>e.g.</em>, bathymetry, Sanctuary Protection Areas, etc.) were provided as a digital atlas to help participants understand information and data gaps within the project area and to identify locations they wanted to prioritize for future data collections. The pGIS was used by 18 participants to convey their recommendations. Each participant was provided with 530 virtual coins to place into grid cells that they wished to prioritize. They were instructed to place more coins in grid cells that were higher priorities. A maximum of 53 coins could be placed into an individual grid cell by each respondent. Respondents also reported why these locations were important by selecting a minimum of one, and a maximum of two, management uses from the following list: endangered species management (e.g.,), habitat restoration, monitoring, coastal vulnerability planning, watershed management, fisheries management, consultations and permitting, emergency response, and spatial protection and management. Respondents also reported what data types were needed in priority cells. A minimum of one, to a maximum of two choices were selected from the following list: habitat map/characterization, shoreline characterization, ground truthing (e.g. photos and videos collected using ROVs or AUVs), elevation (e.g. bathymetry and topography), backscatter and intensity (e.g. surfaces used to delineate between hard and soft substrate), 2D map product (e.g. static images used to visualize bottom type, presence/absence of taxa), georectified photomosaics (e.g. 3D products created from structure for motion), and water column (e.g. for fish biomass detection). Respondents also reported what method of data collection was desired in each priority cell. Only one response was required and were selected from the following list: satellite, lidar, multibeam echosounder, split beam echosounder, side-scan sonar, photogrammetry, drop-camera, and uncrewed systems. Coin values were summarized and mapped to identify high priority areas, reasons for those priorities, and information needs. This ESRI shapefile contains the 10.4 km<sup>2</sup> grid cells used in this prioritization and their associated coin values overall, as well as by management use, data product, and mapping methodology. Other summary values include the number of participants, number of participating groups, number of management uses, and number of data products. Also included is a ranking of each grid cell based on the total number of coins, management uses, and agencies allocating coins in the respective cell. For a complete description of the process and analysis see: Kraus et al., 2022.</p> <p> </p>
R code and associated data for: A review of riverine ecosystem service quantification: research gaps and recommendations
<p>This publication contains the R code and associated data used in the Journal of Applied Ecology publication entitled "A review of riverine ecosystem service quantification: research gaps and recommendations". </p>
Data for "Re-weighing the 5% tagging recommendation: assessing the potential impacts of tags on the behavior and body condition of bats"
<p>Database as tab-delimited (.csv) associated with the publication: </p> <p>Meierhofer M.B., et al. (2024) Re-weighing the 5% tagging recommendation: assessing the potential impacts of tags on the behavior and body condition of bats. <em>Mammal Review.</em></p> <p>Please refer to the main publication for a detailed description. An explanation of the database is available in the Metadata file uploaded alongside the database. R code to reproduce the analysis pipeline is available on GitHub:</p> <p>https://github.com/melissameierhofer/Meta-5-Rule.git</p>
Figure 2 in Recreational watercraft decontamination: can current recommendations reduce aquatic invasive species spread?
Figure 2. Water temperature and exposure duration resulting in 100% mortality. The regression line shows the relationship between water temperature and exposure duration among all AIS types collectively. Dashed lines represent the 95% confidence bands.
What does Google recommend when you want to compare insurance offerings? – A method and empirical study considering Google's top search results
<p>This dataset is part of a publication and shows Google's search results for German search queries on insurance comparison offerings.</p> <p>Relevant search queries were extracted from a commercial search engine log file consisting of more than 640,000 different search queries. From the log, we extracted a variety of query formulations for the same topic, i.e., queries containing the same word or phrase. The selection was based on pre-defined keywords in the context of insurance comparisons. The queries from the log file were automatically selected by combining the terms "*insurance*" and "*comparison*" (including left as well as right truncation). Examples of such inquiries are "car insurance comparison", "occupational disability insurance comparison", "liability insurance in comparison". This procedure identified a total of 121 different search queries. Scraping of the results took place between 08.05. - 09.05.2018.The adress data were extracted by using a text classification algorithm and a crawler to find the contact data on a website.</p> <p>It is a tab-separated file with the following attributes:</p> <p>ID: Unique row identifier</p> <p>ID Query: Unique search query identifier</p> <p>Query: German search query </p> <p>Position: Result position to the search query </p> <p>URL: URL of the search result </p> <p>Host: Host of the search result </p> <p>Company: Name of the company on the website</p> <p>Street: Street in the address on the website </p> <p>Zipcode: Street in the address on the website </p> <p>Location: Location in the address on the website </p> <p>District: District in the address on the website </p> <p>State: State in the address on the website </p> <p>Country: Country in the address on the website </p>
#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems
<p>Music recommender systems can offer users personalized and contextualized recommendation and are therefore important for music information retrieval. An increasing number of datasets have been compiled to facilitate research on different topics, such as content-based, context-based or next-song recommendation. However, these topics are usually addressed separately using different datasets, due to the lack of a unified dataset that contains a large variety of feature types such as item features, user contexts, and timestamps. To address this issue, we propose a large-scale benchmark dataset called #nowplaying-RS, which contains 11.6 million music listening events (LEs) of 139K users and 346K tracks collected from Twitter. The dataset comes with a rich set of item content features and user context features, and the timestamps of the LEs. Moreover, some of the user context features imply the cultural origin of the users, and some others—like hashtags—give clues to the emotional state of a user underlying an LE. In this paper, we provide some statistics to give insight into the dataset, and some directions in which the dataset can be used for making music recommendation. We also provide standardized training and test sets for experimentation, and some baseline results obtained by using factorization machines.</p> <p>The dataset contains three files:</p> <ul> <li>user_track_hashtag_timestamp.csv contains basic information about each listening event. For each listening event, we provide an id, the user_id, track_id, hashtag, created_at </li> <li>context_content_features.csv: contains all context and content features. For each listening event, we provide the id of the event, user_id, track_id, artist_id, content features regarding the track mentioned in the event (instrumentalness, liveness, speechiness, danceability, valence, loudness, tempo, acousticness, energy, mode, key) and context features regarding the listening event (coordinates (as geoJSON), place (as geoJSON), geo (as geoJSON), tweet_language, created_at, user_lang, time_zone, entities contained in the tweet).</li> <li>sentiment_values.csv contains sentiment information for hashtags. It contains the hashtag itself and the sentiment values gathered via four different sentiment dictionaries: AFINN, Opinion Lexicon, Sentistrength Lexicon and vader. For each of these dictionaries we list the minimum, maximum, sum and average of all sentiments of the tokens of the hashtag (if available, else we list empty values). However, as most hashtags only consist of a single token, these values are equal in most cases. Please note that the lexica are rather diverse and therefore, are able to resolve very different terms against a score. Hence, the resulting csv is rather sparse. The file contains the following comma-separated values: <hashtag, vader_min, vader_max, vader_sum,vader_avg, afinn_min, afinn_max, afinn_sum, afinn_avg, ol_min, ol_max, ol_sum, ol_avg, ss_min, ss_max, ss_sum, ss_avg >, where we abbreviate all scores gathered over the Opinion Lexicon with the prefix 'ol'. Similarly, 'ss' stands for SentiStrength. </li> </ul> <p>Please also find the training and test-splits for the dataset in this repo. Also, prototypical implementations of a context-aware recommender system based on the dataset can be found at <a href="https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM">https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM</a>.</p> <p>If you make use of this dataset, please cite the following paper where we describe and experiment with the dataset:</p> <p>@inproceedings{smc18,<br> title = {#nowplaying-RS: A New Benchmark Dataset for Building Context-Aware Music Recommender Systems},<br> author = {Asmita Poddar and Eva Zangerle and Yi-Hsuan Yang},<br> url = {http://mac.citi.sinica.edu.tw/~yang/pub/poddar18smc.pdf},<br> year = {2018},<br> date = {2018-07-04},<br> booktitle = {Proceedings of the 15th Sound & Music Computing Conference},<br> address = {Limassol, Cyprus},<br> note = {code at https://github.com/asmitapoddar/nowplaying-RS-Music-Reco-FM},<br> tppubtype = {inproceedings}<br> }</p>
Water Quality Classes - Recommended Water Quality Based on Guideline and Typical Wastewater Qualities
<p>This dataset compiles water quality standards for different end-uses based on most prominent guidelines. The value "-1" signifies no limit specified or no data available. The dataset also contains a list of typical wastewater qualities for several types of wastewater to be reused. The dataset contains the following two document:</p> <ul> <li>Water Quality Classes - Typical Wastewater Qualities and Recommended Water Quality Based on international Guidelines(Dataset) - PDF</li> </ul>
Thermocouple inhomogeneity data underpinning the recommendations given in Euramet Calibration Guide No. 8 Version 3.0
<p>Thermoelectric inhomogeneity data from diverse sources, compiled to inform the advice given in Euramet Calibration Guide No. 8 concerning the uncertainty to assume from inhomogeneity.</p> <p>Filename indicates file type; data is the magnitude of the inhomogeneity, in units of °C.</p> <p>More details can be found in the publication Meas. Sci. Technol. 29 (2018) 067002, https://doi.org/10.1088/1361-6501/aabaa3.</p> <p> </p>
Figure 1 in Development of experimental mesocosms for cicada nymphs Graptopsaltria nigrofuscata: methodology and research recommendations
Figure 1. Photographs of the mesocosm experiment. (A) a final instar nymph of Graptopsaltria nigrofuscata cicada in a mesocosm cage. (B) An empty burrow made by a cicada nymph. The nymph might feed on larch root at the interior of burrow. Photographs were taken at the end of mesocosm experiment (7 July).
Nearly six decades of grazing research published by the Grassland Society of Southern Africa: Trends, recommendations, and gaps
<p>The dataset contains data about articles pulled from a search in Scopus and Google Scholar from the African Journal of Range and Forage Science between 1966 and 2023 using the search terms "grazing" AND"management"; "communal" AND "grazing". The associated R code contains code for natural language processing. </p> <p>The dataset is supplementary to the published journal article: <span>10.2989/10220119.2024.2397952</span></p>
Recommendations for reporting equivalent black carbon (eBC) mass concentrations based on long-term pan-European in-situ observations
<p>A reliable determination of equivalent black carbon (eBC) mass concentrations derived from filter absorption photometers (FAPs) measurements depends on the appropriate quantification of the mass absorption cross-section (MAC) for converting the absorption coefficient (babs) to eBC. This study investigates the spatial–temporal variability of the MAC obtained from simultaneous elemental carbon (EC) and babs measurements performed at 22 sites. We compared different methodologies for retrieving eBC integrating different options for calculating MAC including: locally derived, median value calculated from 22 sites, and site-specific rolling MAC. The eBC concentrations that underwent correction using these methods were identified as LeBC (local MAC), MeBC (median MAC), and ReBC (Rolling MAC) respectively. Pronounced differences (up to more than 50 %) were observed between eBC as directly provided by FAPs (NeBC; Nominal instrumental MAC) and ReBC due to the differences observed between the experimental and nominal MAC values. The median MAC was 7.8 ± 3.4 m2 g-1 from 12 aethalometers at 880 nm, and 10.6 ± 4.7 m2 g-1 from 10 MAAPs at 637 nm. The experimental MAC showed significant site and seasonal dependencies, with heterogeneous patterns between summer and winter in different regions. In addition, long-term trend analysis revealed statistically significant (s.s.) decreasing trends in EC. Interestingly, we showed that the corresponding corrected eBC trends are not independent of the way eBC is calculated due to the variability of MAC. NeBC and EC decreasing trends were consistent at sites with no significant trend in experimental MAC. Conversely, where MAC showed s.s. trend, the NeBC and EC trends were not consistent while ReBC concentration followed the same pattern as EC. These results underscore the importance of accounting for MAC variations when deriving eBC measurements from FAPs and emphasize the necessity of incorporating EC observations to constrain the uncertainty associated with eBC.</p>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Public and Stakeholder Engagement
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Ethics and Ethical Governance
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
Datasets from the KDD 2021 article "A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"
<p>We publicly release the anonymized <em>song_embeddings.parquet user_embeddings.parquet user_features_test.parquet user_features_train.parquet user_features_validation.parquet</em> datasets, with each of the TT-SVD or UT-ALS versions of embeddings, from the music streaming platform Deezer, as described in the article "<em>A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps"</em> published in the proceedings of the 27TH ACM SIGKDD conference on knowledge discovery and data mining (<em>KDD 2021</em>). The paper is available <a href="https://arxiv.org/abs/2106.03819">here</a>.</p> <p>These datasets are used in the GitHub repository <a href="https://github.com/deezer/semi_perso_user_cold_start">deezer/semi_perso_user_cold_start</a> to reproduce experiments from the article.</p> <p>Please cite our paper if you use our code or data in your work.</p>
Determinants of HTA recommendations
<p>The data set contains macro and micro level variables collected from reimbursement reports on a panel of medicines assessed by HTA bodies between 2010 and 2019.</p>
Datasets to Evaluate Accuracy, Miscalibration and Popularity Lift in Recommendations
<p>This repository contains three datasets for evaluating accuracy, miscalibration and popularity lift in recommender systems. All datasets contain genre/category information in addition to different user group splits:</p> <ol> <li>Last.fm (lfm.zip), based on the LFM-1b dataset of JKU Linz (http://www.cp.jku.at/datasets/LFM-1b/)</li> <li>MovieLens (ml.zip), based on MovieLens-1M dataset (https://grouplens.org/datasets/movielens/1m/)</li> <li>MyAnimeList (anime.zip), based on the MyAnimeList dataset of Kaggle (https://www.kaggle.com/CooperUnion/anime-recommendations-database)</li> </ol> <p>'user_events_cats.txt' contains the users' rating/interaction data along with a list of genres/categories assigend to the rated items. The list of categories is given in 'categories.txt'. Additionally, assignments to three user groups that differ in their inclination to popular/mainstream items are provided: LowPop in 'low_main_users.txt', MedPop in 'med_main_users.txt', and HighPop in 'high_main_users.txt'.</p> <p>The format of the three user files are "user,mainstreaminess"</p> <p>The format of the user-events files are "user,item,preference,cats", where different categories are separated by '|'</p> <p>The format of the categories files are "category-name,index", where index refers to the category-id in the user-events files</p> <p>Example Python-code for analyzing the datasets as well as empirical results on calibration, popularity lift and accuracy can be found on GitHub: https://github.com/domkowald/FairRecSys</p>
Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium
<p>Dataset includes 2.5 hours real-world data, for one participant from each of the technical validation study cohorts: ongestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), healthy adult (HA), multiple sclerosis (MS), Parkinson’s (PD) and proximal femoral facture (PFF) linked to the manuscript entitled ‘Assessing real-world gait with digital technology? Validation, insights, and recommendations from the Mobilise-D consortium’. The attached documentation has 6 datasets and a README document.</p> <p>The MOBILISE-D project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 820820. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and the European Federation of Pharmaceutical Industries and Associations (EFPIA).</p> <p>Content on this publication reflects the author’s view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.</p>
Dataset: Strauss et al. 2023 Sustainable soil management measures: a synthesis of stakeholder recommendations
<p>The provided dataset contains the used information for the scientific publication</p> <p>Strauss, V., Paul, C., Dönmez, C., Löbmann, M., & Helming, K. (2023). Sustainable soil management measures: a synthesis of stakeholder recommendations. <em>Agronomy for Sustainable Development</em>, <em>43</em>(1), 17</p> <p> </p> <p>Due to the assessment language, parts of the dataset are in German.</p> <p>Specifically, it contains:</p> <p>- Stakeholder documents (welche ausgewertet wurden) & Einteilung in Stakeholder Groups<br> - Erfassung der Aussagen & Kategorisierung<br> - Auswertung zur Longlist<br> - Ergebnisse der Farmer Survey</p>
Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group
<p>Example datasets and code for the recommended implementation of Quantitative Susceptibility Mapping (QSM) in "Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group".</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.