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5 results for “journal recommendation”
Pubmed Journal Recommendation System dataset
<p>Dataset for Journal recommendation, includes title, abstract, keywords, and journal.</p> <p>We extracted the journals and more information of:</p> <p>Jiasheng Sheng. (2022). PubMed-OA-Extraction-dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6330817.</p> <p>Dataset Components:</p> <ul> <li> <p><strong>data_pubmed_all:</strong> This dataset encompasses all articles, each containing the following columns: 'pubmed_id', 'title', 'keywords', 'journal', 'abstract', 'conclusions', 'methods', 'results', 'copyrights', 'doi', 'publication_date', 'authors', 'AKE_pubmed_id', 'AKE_pubmed_title', 'AKE_abstract', 'AKE_keywords', 'File_Name'.</p> </li> <li> <p><strong>data_pubmed:</strong> To focus on recent and relevant publications, we have filtered this dataset to include articles published within the last five years, from January 1, 2018, to December 13, 2022—the latest date in the dataset. Additionally, we have exclusively retained journals with more than 200 published articles, resulting in 262,870 articles from 469 different journals.</p> </li> <li> <p><strong>data_pubmed_train, data_pubmed_val, and data_pubmed_test:</strong> For machine learning and model development purposes, we have partitioned the 'data_pubmed' dataset into three subsets—training, validation, and test—using a random 60/20/20 split ratio. Notably, this division was performed on a per-journal basis, ensuring that each journal's articles are proportionally represented in the training (60%), validation (20%), and test (20%) sets. The resulting partitions consist of 157,540 articles in the training set, 52,571 articles in the validation set, and 52,759 articles in the test set.</p> </li> </ul>
A list with recommended journals by Russian VAK
<p>Processed list of recommended journals in social science and computer science by VAK (source of raw data: http://vak.ed.gov.ru/87, date: 19.04.2017).</p>
Journal recommendations for 4 abstracts
<p>Journal recommendations prepared on results from JANE and whichjournal.com based on 4 abstracts from the disciplines dentistry, psychology and aerosol chemistry.</p> <p>The factsheets with data for each journal should help to decide for the best journal.</p> <p>The data is provided as spreadsheet (xls) and factsheet (pdf).</p>
Research integrity in instructions for authors in Japanese medical journals using ICMJE recommendations: A descriptive literature study
<p>The goals of this project were to compare research integrity content in Instructions for Authors in ICMJE member journals with those in the English- and Japanese-language journals of the Japanese Association of Medical Sciences (JAMS).</p> <p>Therefore, this dataset contents include the following three categories: 1) journals' background information, 2) description numbers of research integrity topics, and 3) journal numbers that required ICMJE description forms in ICMJE member journals, English- and Japanese-language journals.</p>
Research integrity in instructions for authors in Japanese medical journals using ICMJE recommendations: A descriptive literature study
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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.