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.
802
datasets available to search
ShareScore release 0.9.0
Dataset results
802 results for “Commercialization”
Fig. 1 in Glyphosate commercial formulation effects on preoptic area and hypothalamus of Cardinal Neon Paracheirodon axelrodi (Characiformes: Characidae)
Fig. 1. Cross-section of preoptic area (AP) and posterior diencephalic region (PDR) of Paracheirodon axelrodi showing its different neuronal nuclei. a. Panoramic view of AP showing different neuronal nuclei. b. Panoramic view of PDR, including HT neuronal nuclei. Technique: high resolution optical microscopy (HROM) Bar 100 μm.
VFA T1 mapping | RTHawk (open) vs Siemens (commercial)
<p>The preliminary variable flip angle (VFA) T1 mapping data acquired using:<br> 1. Fully-open (<a href="https://bit.ly/qMRPullseq">https://bit.ly/qMRPullseq</a>) 3D spoiled gradient-echo based RTHawk application<br> 2. Siemens stock sequence 3D FLASH </p> <p>The analysis (fitting and comparison) can be executed online: <a href="http://bit.ly/qmr_vfat1">http://bit.ly/qmr_vfat1</a> </p> <p>GitHub repository for the code: <a href="https://github.com/agahkarakuzu/ismrm20">https://github.com/agahkarakuzu/ismrm20</a></p>
Appendix List of samples of deep frozen frog legs with purchase date, collection number, haplotype number, taxonomic identification, tibia length (TL) and estimated snout vent length (SVL). in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France
Appendix List of samples of deep frozen frog legs with purchase date, collection number, haplotype number, taxonomic identification, tibia length (TL) and estimated snout vent length (SVL).
Fig. 2. Minimum spanning network depicting relationships among 16S in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France
Fig. 2. Minimum spanning network depicting relationships among 16S haplotypes of Fejervarya cancrivora (Gravenhorst, 1829). The size of each circle is proportional to the haplotype frequency and the lengths of the connecting lines are proportional to the number of mutations. Colors refer to distinct regions (Indonesia: Java, Sumatra, Bali, Kalimantan, Bangka; Malaysia; Taiwan) and commercialized frogs of unknown origin are in black.
Fig. 3. Histograms. A in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France
Fig. 3. Histograms. A. Snout vent length (in mm) in adult Fejervarya cancrivora (Gravenhorst, 1829) from samples collected for scientific purposes (Boulenger 1920) and collection specimens as mentioned in Material and methods. B. Snout vent length estimated from tibia length of genetically identified frog legs from French supermarkets (specimen list, see Appendix).
Fig. 1 in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France
Fig. 1. Phylogeny of Indonesian species of Fejervarya and Limnonectes recovered by the Bayesian analysis (GTR + I + G model). Hoplobatrachus rugulosus (Wiegmann, 1834) and Occidozyga laevis (Günther, 1858) were used as outgroups. Numbers on nodes represent Bayesian posterior probabilities, * indicates a value higher than 0.98. Only values higher than 0.75 are represented. h01 to h18 indicate the 18 haplotypes from frozen frog legs recovered in this study.
Figure 2 in Description of two new Quadrastichus (Hymenoptera: Eulophidae) reared from Litchiomyia chinensis (Diptera: Cecidomyiidae) on commercial lychee (Litchi chinensis; Sapindaceae) in Taiwan
Figure 2. Quadrastichus lasallei, female: a, anterolateral head; b, dorsal mesosoma; c, propodeum; g, lateral flagellum; h, lateral clava. Q. lasallei, male: d, lateral flagellum; e, lateral clava; f, lateral scape.
Figure 1 in Description of two new Quadrastichus (Hymenoptera: Eulophidae) reared from Litchiomyia chinensis (Diptera: Cecidomyiidae) on commercial lychee (Litchi chinensis; Sapindaceae) in Taiwan
Figure 1. (a) Quadrastichus johnlasallei, lateral habitus, female. (b) Quadrastichus lasallei, lateral habitus, female.
Data: Flower visiting insects of kiwifruit within New Zealand commercial orchard blocks sampled over two years in the Bay of Plenty, New Zealand
<p>These data are total counts of individual bee and non–bee insects observed visiting the flowers of kiwifruit (<em>Actinidia chinensis</em> var.deliciosa) (‘Hayward’) vines in three commercial orchards located in the Bay of Plenty Region of New Zealand (37° 46' 56" S; 176° 19' 10" E). Each block was located on a different farm and each separated by a distance of at least two kilometres and surveyed twice in two consecutive years. A total of 1181 insects were observed, 741 in the 2014 season and 460 in the 2015 season. Insects from four orders were recorded. The most abundant species were honey bees <em>Apis mellifera</em> (n= 1068; 90.4%), flower longhorn beetles <em>Zorion guttigerum</em> (n= 52; 4.4%), the native bee <em>Lasioglossum</em> <em>sordidum</em>/c<em>ognatum</em> (n= 12; 1.0%) and the hover fly <em>Melanostoma fasciatum</em> (n= 11; 0.9%) Others insects represented 3.2% of individuals observed (n=38). We present a table of counts of the insects observed.</p>
Figure 4 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 4. Scanning Electron Microscopy (SEM) images of the upper-surface of leaves sprayed with water (control), Silical®, Postar®, and Ultrafit® to visualize the diversity of trichome types (glandular: GT and non-glandular: NGT) and densities for tomato cultivars K-186 F1 and 023 F1.
Figure 2 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 2. Population of movable stages of Tetranychus urticae on tomato leaves over old 3–13 weeks after transplanting for two cultivars treated with commercial stimulants during the 2017 and 2018 summer seasons. Ultrafat * applied by adding to soil below the plants. Columns with the same letter represent means that are not significantly different according to Tukey's multiple range test (p <0.05). Vertical bars represent ± standard error of the mean (n = 36).
Figure 1 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 1. Population of movable stages of Tetranychus urticae on tomato leaves of several plant ages for cultivars K186 F1 and 023 F1 treated with some commercial stimulants during the 2017 (A) and 2018 (B) summer seasons. Population at three weeks after transplanting was immediately before treatment. Columns with the same letter represent means that are not significantly different according to Tukey's multiple range test (p <0.05). Vertical bars represent ± standard error of the mean (n = 21).
Figure 3 in Efficiency of some commercial stimulants in inducing tomato resistance to Tetranychus urticae (Acari: Tetranychidae)
Figure 3. Scanning Electron Microscopy (SEM) images of the lower-surface of leaves sprayed with water (control), Silical®, Postar®, and Ultrafit® to visualize the diversity of trichome types (glandular: GT and non-glandular: NGT) and densities for tomato cultivars K-186 F1 and 023 F1.
Figure 1 in Effects of commercial oils on the camel tick, Hyalomma dromedarii (Acari: Ixodidae) and their enzyme activities
Figure 1. Mortality percentages of Hyalomma dromedarii semi-engorged females treated with different concentrations of four oils at five successive days after treatment – A. Rosemary; B. Garlic; C. Neem; D. Cyperus. a, b, … etc. indicate significant differences between concentrations (%) of each oil for each day according to Tukey test (P <0.001).
Figure 2 in Characterization of Eugenia uniflora accessions: a native species with great commercial potential in America
Figure 2. Collection sites of the 40 Surinam Cherry accessions in the state of Rio Grande do Sul with the cluster according to genetic similarity obtained with RAPD markers. Group I =; Group II =; Group III =).
Figure 1 in Characterization of Eugenia uniflora accessions: a native species with great commercial potential in America
Figure 1. Dendrogram of genetic similarity between the 40 Surinam Cherry, obtained from RAPD markers. The line indicates the 84% cut-off point based on the average similarity between populations.
The Commercial Potential of Science
<div> <div> <div> <div> </div> </div> </div> <div> <p>[<strong>c</strong><strong>oming soon: commercial and scientific potential predictions for over 30 million articles, published worldwide</strong>]</p> <p> </p> <p>This dataset introduces a novel index designed to predict the commercial potential of scientific articles. The index captures the probability that an article will be used by firms for the development of marketable products or processes. In addition to commercial potential, the dataset also introduces an index to predict scientific potential—the likelihood that an article will be relevant for the advance of science, regardless its commercial application. </p> <p>The indices are crucial for researchers focused on understanding 1) the production of science with commercial potential and 2) the pathway from academic research to market innovations and the factors that influence the commercial viability of scientific discoveries.</p> <p> </p> <p><strong>Citation Information:</strong> If you use this dataset, please cite the article: “Masclans-Armengol, R., Hasan, S., & Cohen, W. M. (2024). Measuring the Commercial Potential of Science. NBER working paper”</p> <p> </p> <p><strong>Components of the Dataset: </strong>The dataset encompasses indices for over 5.2 million articles that meet the following criteria:</p> <ul> <li>Publication year: 2000 to 2020</li> <li>Published under 126 U.S. universities</li> <li>Articles in the applied and natural sciences and engineering fields</li> </ul> <p>Data is delivered via a single csv file. Each row contains information for a scientific article, with the following variables:</p> <ul> <li>‘<em>doi’</em>: Digital Object Identifier—unique article identifier that can be used to match to other data sources, such as OpenAlex, Dimensions, or Web of Science.</li> <li>‘<em>compot</em>’: commercial potential index.</li> <li>‘<em>scipot</em>‘: scientific potential index.</li> </ul> <p>To develop the commercial potential index, we employed SciBert (Beltagy et al., 2019), a Large Language Model for scientific understanding. We fine tune SciBert with deep neural networks to classify scientific articles based on their potential for commercial application. We trained 20 predictive models, one per year, using the text of an academic article’s abstract to generate ex-ante, out-of-sample, and out-of-training-time-period predictions of any given scientific article’s commercial potential.</p> <p>Following the same methodology, we compute the scientific potential index.</p> <p> </p> <p><strong>Licensing and Contact Information: </strong>The dataset and its components are distributed under a Creative Commons Attribution Non-Commercial license.</p> <p> </p> <p><strong>Acknowledgments</strong>: We thank The Technology Opportunity Lab at Duke University and the Kauffman Foundation for funding the creation of this dataset.</p> </div> </div>
Figs 2, 3 in Seasonal variations in ixodid tick populations on a commercial game farm in the Limpopo Province, South Africa
Figs 2, 3. Numbers of Rhipicephalus (Boophilus) decoloratus collected in wetter and drier months (2), and in warmer and cooler months (3).
Fig. 2. Haplotype network calculated from the E in Molecular assessment of commercial and laboratory stocks of Eisenia spp. (Oligochaeta: Lumbricidae) from South Africa
Fig. 2. Haplotype network calculated from the E. andrei COI haplotypes found in the South African earthworm groups investigated. The size of the circles is proportional to the number of earthworms sharing the same haplotype. The numbers on the branches indicate the positions of mutations on the COI sequences, mv1 represents a median vector (intermediate haplotypes, not found in this study).
Fig. 1 in Molecular assessment of commercial and laboratory stocks of Eisenia spp. (Oligochaeta: Lumbricidae) from South Africa
Fig. 1. Neighbour-joining tree based on the K2P method. Bootstrap support obtained for specific nodes are reported. Genbank accession numbers or BOLD process IDs are provided in brackets for the sequences downloaded from either Genbank or BOLD. Allolobophoridella eiseni and Microscolex phosphoreus were included as outgroups. Asterisk indicates dubious E. andrei sequences from BOLD.
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.