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.
4
datasets available to search
ShareScore release 0.9.0
Dataset results
4 results for “knowledge syntheses”
Knowledge syntheses in medical education: A bibliometric analysis
<p>This is the raw data for the manuscript "Knowledge syntheses in medical education: A bibliometric analysis" by Maggio, Costello, Norton, Driessen, and Artino. The data contain the citations, including the DOI, PMID, and all author locations for all 963 knowledge syntheses included in the study. This study has also been published in a peer reviewed journal: </p> <p>Maggio LA, Costello JA, Norton C, Driessen EW, Artino Jr AR. <a href="https://link.springer.com/article/10.1007/s40037-020-00626-9">Knowledge syntheses in medical education: A bibliometric analysis</a>. <em>Perspectives on Medical Education</em>. 2020 Oct 22:1-9.</p> <p> </p> <p>The abstract for the study is as follows:</p> <p><strong>Purpose</strong> This bibliometric analysis maps the landscape of knowledge syntheses in medical education. It provides scholars with a roadmap for understanding where the field has been and where it might go in the future. In particular, this analysis details the venues in which knowledge syntheses are published, the types of syntheses conducted, citation rates they produce, and altmetric attention they garner.</p> <p><strong>Method</strong> In 2020, the authors conducted a bibliometric analysis of knowledge syntheses published in 14 core medical education journals from 1999 to 2019. To characterize the studies, metadata was extracted from Pubmed, Web of Science, Altmetrics Explorer, and Unpaywall.</p> <p><strong>Results</strong> The authors analyzed 963 knowledge syntheses representing 3.1% of total articles published (n=30,597). On average, 45.9 knowledge syntheses were published annually (SD=35.85, Median=33), and there was an overall 2,620% increase in the number of knowledge syntheses published from 1999 to 2019. The journals each published, on average, a total of 68.8 knowledge syntheses (SD=67.2, Median=41) with <em>Medical Education</em> publishing the most (n=189; 19%). Twenty-one knowledge synthesis types were identified; the most prevalent types were systematic reviews (n=341; 35.4%) and scoping reviews (n=88; 9.1%). Knowledge syntheses were cited an average of 53.80 times (SD=107.12, Median=19) and received a mean Altmetric Attention Score of 14.12 (SD=37.59, Median=6).</p> <p><strong>Conclusions</strong> There has been considerable growth in knowledge syntheses in medical education over the past 20 years, contributing to medical education’s evidence base. Beyond this increase in volume, researchers have introduced methodological diversity in these publications, and the community has taken to social media to share knowledge syntheses. Implications for the field, including the impact of synthesis types and their relationship to knowledge translation, are discussed.</p>
What is the current extent of our systematic knowledge? A case study in synthesizing phylogenetic data for building large trees to advance phylogenetic research in Orobanchaceae
To date, no comprehensive phylogenetic analyses have been conducted in Orobanchaceae that include both a wide generic sampling and a large sampling of species. Here, we utilize a recently developed set of tools for synthesizing publicly available data, and apply these to assess where the gaps in our phylogenetic knowledge exist in the angiosperm clade Orobanchaceae. We then use the resulting timetree to investigate diversification dynamics in this clade of mostly parasitic plants. We used the PyPHLAWD pipeline and RAxML to assemble a supermatrix of >900 species and construct a comprehensive phylogenetic hypothesis of Orobanchaceae. Divergence times were estimated with penalized likelihood from a 'congruified' set of secondary calibrations, and diversification dynamics were investigated in both trait-independent and trait-dependent (parasitic habit) contexts with BAMM and HiSSE. We sampled 39.8% of described species from 80 of 108 genera, representing all eight primary clades of Orobanchaceae. Relationships and divergence time estimates were similar to previous, clade-specific studies; however, eight genera were recovered as non-monophyletic, and will require focused systematic attention. Ours is first Orobanchaceae wide study to use model based approach to assess diversification dynamics in the parasitic lineage. Our results reveal elevated diversification rates associated with hemiparasitic habit, and holoparasitic habit was revealed to be an absorbing state, meaning that lineages within Orobanchaceae tend towards a reduction in photosynthetic towards hemiparasitism and finally holoparasitism. Using a synthetic phylogenetic hypothesis in conjunction with a unified taxonomic framework, we are able to understand where our taxonomic and phylogenetic knowledge is incomplete or conflicting, and we predict that this approach will aid in identifying where to focus future systematic efforts in any clade of interest. For Orobanchaceae, our phylogeny reflects the most recent taxonomy, and provides a new, comprehensive temporal framework for the clade that can serve as a stepping-stone for future macroevolutionary studies.
Data from: Toward synthesizing our knowledge of morphology: using ontologies and machine reasoning to extract presence/absence evolutionary phenotypes across studies
The reality of larger and larger molecular databases and the need to integrate data scalably have presented a major challenge for the use of phenotypic data. Morphology is currently primarily described in discrete publications, entrenched in noncomputer readable text, and requires enormous investments of time and resources to integrate across large numbers of taxa and studies. Here we present a new methodology, using ontology-based reasoning systems working with the Phenoscape Knowledgebase (KB; kb.phenoscape.org), to automatically integrate large amounts of evolutionary character state descriptions into a synthetic character matrix of neomorphic (presence/absence) data. Using the KB, which includes more than 55 studies of sarcopterygian taxa, we generated a synthetic supermatrix of 639 variable characters scored for 1051 taxa, resulting in over 145,000 populated cells. Of these characters, over 76% were made variable through the addition of inferred presence/absence states derived by machine reasoning over the formal semantics of the source ontologies. Inferred data reduced the missing data in the variable character-subset from 98.5% to 78.2%. Machine reasoning also enables the isolation of conflicts in the data, that is, cells where both presence and absence are indicated; reports regarding conflicting data provenance can be generated automatically. Further, reasoning enables quantification and new visualizations of the data, here for example, allowing identification of character space that has been undersampled across the fin-to-limb transition. The approach and methods demonstrated here to compute synthetic presence/absence supermatrices are applicable to any taxonomic and phenotypic slice across the tree of life, providing the data are semantically annotated. Because such data can also be linked to model organism genetics through computational scoring of phenotypic similarity, they open a rich set of future research questions into phenotype-to-genome relationships.
Data from: Toward synthesizing our knowledge of morphology: using ontologies and machine reasoning to extract presence/absence evolutionary phenotypes across studies
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.