Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
24
datasets available to search
ShareScore release 0.9.0
Dataset results
24 results for “research topics”
Figure 1 from: Irawan D, Priyambodho A, Rachmi C, Wibowo D, Fahmi A (2016) Bibliometric study to assist research topic selection: a case from research design on Jakarta's groundwater (part 1). Research Ideas and Outcomes 2: e9841. https://doi.org/10.3897/rio.2.e9841
Figure 1 - Density map of the papers based on the key words (Red: dense paper population, green: less dense). Dataset is available as Suppl. material 1 (in Irawan et al. 2016).
Science Education Research Topic Modeling Dataset
<p>This dataset contains scraped and processed text from roughly 100 years of articles published in the Wiley journal <em>Science Education </em>(formerly <em>General Science Quarterly</em>). This text has been cleaned and filtered in preparation for analysis using natural language processing techniques, particularly topic modeling with <a href="https://dl.acm.org/doi/10.5555/944919.944937">latent Dirichlet allocation</a> (LDA). We also include a Jupyter Notebook illustrating how one can use LDA to analyze this dataset and extract latent topics from it, as well as analyze the rise and fall of those topics over the history of the journal.</p> <p>The articles were downloaded and scraped in December of 2019. Only non-duplicate articles with a listed author (according to the <a href="https://www.crossref.org/">CrossRef metadata</a> database) were included, and due to missing data and text recognition issues we excluded all articles published prior to 1922. This resulted in 5577 articles in total being included in the dataset. The text of these articles was then cleaned in the following way:</p> <ul> <li>We removed duplicated text from each article: prior to 1969, articles in the journal were published in a magazine format in which the end of one article and the beginning of the next would share the same page, so we developed an automated detection of article beginnings and endings that was able to remove any duplicate text.</li> <li>We removed the reference sections of the articles, as well headings (in all caps) such as “ABSTRACT”.</li> <li>We reunited any partial words that were separated due to line breaks, text recognition issues, or British vs. American spellings (for example converting “per cent” to “percent”) </li> <li>We removed all numbers, symbols, special characters, and punctuation, and lowercased all words.</li> <li>We removed all <em>stop words</em>, which are words without any semantic meaning on their own—“the”, “in,” “if”, “and”, “but”, etc.—and all single-letter words.</li> <li>We lemmatized all words, with the added step of including a part-of-speech tagger so our algorithm would only aggregate and lemmatize words from the same part of speech (e.g., nouns vs. verbs).</li> <li>We detected and create <em>bi-grams</em>, sets of words that frequently co-occur and carry additional meaning together. These words were combined with an underscore: for example, “problem_solving” and “high_school”.</li> </ul> <p>After filtering, each document was then turned into a list of individual words (or tokens) which were then collected and saved (using the python pickle format) into the file scied_words_bigrams_V5.pkl.</p> <p>In addition to this file, we have also included the following files:</p> <ol> <li>SciEd_paper_names_weights.pkl: A file containing limited metadata (title, author, year published, and DOI) for each of the papers, in the same order as they appear within the main datafile. This file also includes the weights assigned by an LDA model used to analyze the data</li> <li>Science Education LDA Notebook.ipynb: A notebook file that replicates our LDA analysis, with a written explanation of all of the steps and suggestions on how to explore the results.</li> <li>Supporting files for the notebook. These include the requirements, the README, a helper script with functions for plotting that were too long to include in the notebook, and two HTML graphs that are embedded into the notebook. </li> </ol> <p>This dataset is shared under the terms of the <a href="https://olabout.wiley.com/WileyCDA/Section/id-826542.html">Wiley Text and Data Mining Agreement,</a> which allows users to share text and data mining output for non-commercial research purposes. Any questions or comments can be directed to Tor Ole Odden, t.o.odden@fys.uio.no.</p>
Exploring the Landscape of Controlled Environment Agriculture Research: A Systematic Scoping Review of Current Trends and Topics
<p>No description provided.</p>
Longitudinal bibliometric investigation of trending orthodontic research topics, study designs and outcomes: are we reporting patient-focused outcomes?
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.