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.
730
datasets available to search
ShareScore release 0.9.0
Dataset results
730 results for “biochemicals”
Species and foliar biochemical trait maps for Blackhawk Island, Wisconsin May-October 2018
<p class="MsoNormal">Foliar biochemical traits are important indicators of ecosystem functioning and health that are impractical to characterize at large spatial and temporal scales using traditional measurements. However, comprehensive inventories of foliar traits are important for understanding ecosystem responses to anthropogenic and natural disturbances, as inputs into ecosystem process models, and for quantifying spatial variation in functional diversity. Imaging spectroscopy has been demonstrated as a valuable tool for developing maps of ecologically important foliar traits at large scales, but its application to mapping foliar traits over the course of the growing season has been limited. We collected high-resolution imaging spectroscopy data over Blackhawk Island, Wisconsin, USA at eight time points during the 2018 growing season (May – October). Using partial least squares regression (PLSR) we developed predictive models applicable to all dates to produce canopy-level maps of eight traits related to ecophysiological function: chlorophyll content, leaf mass per area and concentrations of calcium, nitrogen, phosphorus, potassium, phenolics and lignin. The accuracy of our models varied across traits (R<sup>2</sup>: 0.25-0.86); traits with well-defined absorption features were retrieved with high accuracy including chlorophyll (R<sup>2</sup>: 0.86; %RMSE: 11.0) and total phenolics (R<sup>2</sup>: 0.86; %RMSE: 11.0). We also assessed how well our models estimated biochemistry on novel species and new dates using a cross-validation analysis. Chlorophyll and total phenolics were well estimated across withheld dates and species, whereas calcium was estimated poorly on both withheld species (R<sup>2</sup>: 0.08) and dates (R<sup>2</sup>: 0.07). Our canopy-level maps of macronutrients (N, P and K) showed general trends of decreasing concentration over the course of the year, reflecting dilution by carbon-rich compounds during the growing season and resorption during senescence.</p>
Virtual screening on SARS-CoV-2 Nsp14_testing of 5474 hits in a Nsp14 biochemical assay and an A549-hACE2 SARS-CoV-2 infected cells
<p> </p> <p>This report describes the most relevant results of virtually screening the Janssen Pharmaceutica compound collection for potential activity against SARS-CoV-2 Nsp14 and confirmation of potential hits in a SARS-CoV-2 Nsp10/Nsp14 biochemical exonuclease assay and in a A549-hACE2 cell-based anti-SARS-CoV-2 assay.</p>
Dataset I related to publication: Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96
<p>MD simulation data (sotorasib and adagrasib in complex with NRAS(WT) and KRAS(WT)) related to publication: "Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96". </p> <p>ACS Chem. Biol. 2024, XXXX, XXX, XXX-XXX</p> <p>https://pubs.acs.org/doi/10.1021/acschembio.4c00315</p> <ul> <li>The .zip files contain raw Desmond simulation trajectories of KRAS(WT) or NRAS(WT) in complex with adagrasib or sotorasib [5 replicas; each 5 us] <ul> <li>(-out.cms files and trajectories).</li> </ul> </li> </ul> <p>Related datasets: 10.5281/zenodo.10781452</p>
Dataset II related to publication: Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96
<p>MD simulation data (KRAS(G12C) and KRAS(G12C/Y96D)) related to publication: "Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96". </p> <p>ACS Chem. Biol. 2024, XXXX, XXX, XXX-XXX</p> <p>https://pubs.acs.org/doi/10.1021/acschembio.4c00315</p> <ul> <li>The .zip files contain raw Desmond simulation trajectories of KRAS(G12C) and KRAS(G12C/Y96D) with apo SII-P or in complex with sotorasib [5 replicas; each 4 us] <ul> <li>(-out.cms files and trajectories).</li> </ul> </li> </ul> <p>Related datasets: 10.5281/zenodo.10812233 </p>
Figure 5 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 5. Gene family and time-calibrated evolution analyses of 28 litostomateans and two spirotricheans. Ages are given as million years ago (Mya). Calibrated nodes are indicated with a star. Numbers asser "+" and "-" represent the expanded or contracted gene families in each branch* respectively. MRCA* the most recent common ancestor; H* subclass Haptoria; R* subclass Rhynchostomatia; º* subclass ºrichostomatia; CZ* Cenozoic; MZ* Mesozoic; NP* Neoproterozoic; PZ* Palaeozoic.
Figure 6 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 6. Heatmap showing the bias of stop codon usage among 28 litostomatean ciliates. H* subclass Haptoria; R* subclass Rhynchostomatia; º* subclass ºrichostomatia.
Figure 3. Phylogenomic tree estimated from a in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 3. Phylogenomic tree estimated from a concatenated dataset of 1680 orthogroups of 28 litostomateans and two spirotricheans
Figure 3. Phylogenomic tree estimated from a in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 3. Phylogenomic tree estimated from a concatenated dataset of 1680 orthogroups of 28 litostomateans and two spirotricheans by maximum likelihood (ML) and Bayesian inference (BI) methods. Sequences from the present study are in bold. Ŋe numbers at the nodes are the bootstrap values of ML out of 1000 pseudoreplicates and the posterior probability of Bayesian analysis* respectively. Ŋe black dots represent full support values both in the ML and in the BI trees. º* subclass ºrichostomatia; H* subclass Haptoria; R* subclass Rhynchostomatia.
Figure 2 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 2. Comparative analysis of 28 litostomatean ciliates. A* UpSet plot of shared orthogroups among the three litostomatean subclasses. B* GO enrichment analysis of conserved orthogroups in Litostomatea. C* heatmap showing the number of shared genes among 28 litostomatean ciliates. º* subclass ºrichostomatia; H* subclass Haptoria; R* subclass Rhynchostomatia.
Figure 7 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 7. Ŋe length distribution (A–C) and motif (C–E) of the introns detected in Monodinium sp.* Myriokaryon sp.* and Apodileptus visscheri.
Figure 1 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 1. Maps showing the location of the sampling sites and photomicrographs showing the in vivo morphology of the 14 litostomatean ciliates for which omics' data were newly obtained.
Figure 4 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 4. Carbohydrate-Active Enzymes Database (CAZy) annotation results of 28 litostomatean ciliates. A* comparison of the number of CAZymes in 28 litostomatean ciliates. B* CAZy function classification diagrams of three newly sequenced litostomateans (Didinium sp.1* Myriokaryon sp.* and Apodileptus visscheri). º* subclass ºrichostomatia; H* subclass Haptoria; R* subclass Rhynchostomatia.
Figure 2 in Comparative genomics reveals the evolutionary history of the unicellular eukaryote class Litostomatea and its adaptive evolution based on biochemical metabolic capacity
Figure 2. Comparative analysis of 28 litostomatean ciliates. A* UpSet plot of shared orthogroups among the three litostomatean subclasses. B*
IMMUNO-BIOCHEMICAL AND HISTOLOGICAL CHARACTERISTICS OF UTERINE LEIOMYOMA
<p> </p> <p><span>Лейомиома матки является распространенным гинекологическим заболеванием, которое поражает женщин различных возрастных групп, особенно в репродуктивном возрасте. Для изучения клинических характеристик и патофизиологических механизмов этого заболевания было проведено комплексное клиническое исследование с участием 120 женщин с диагнозом лейомиома матки в возрасте от 20 до 49 лет. Большинство участников составили женщины в возрасте от 40 до 49 лет (90 человек), среди которых значительное преобладание имели женщины узбекской национальности (106 женщин, 88,3%). Анализ уровня образования и рода занятий показал более высокую распространенность данной патологии среди женщин с средним специальным образованием (71,6%) и домохозяек (51,6%). Исследование также выявило сложный акушерский анамнез у участниц, из которых 14,2% сообщили о самопроизвольных выкидышах, 17,5% имели неразвивающуюся беременность, и 2,5% имели историю внематочной беременности. Основные жалобы у пациенток включали гиперполименорею (51,6%), боли в области таза и альгоменорею (36,7%), нарушения менструального цикла (59,2%), диспареунию (27,5%) и бесплодие (22,5%). Статистический анализ данных проводился с использованием как параметрических, так и непараметрических методов, выявив значительные клинические и лабораторные изменения. Например, у пациенток с лейомиомой матки были обнаружены повышенные уровни провоспалительных цитокинов, таких как IL-6 и TNF-α, а также факторов роста, таких как TGF-β2 и VEGF, что указывает на активные воспалительные и пролиферативные процессы.</span></p> <p><strong><span>Ключевые слова:</span></strong><span> лейомиома матки, самопроизвольные выкидыши, внематочные беременности, гиперполи менорея, боли в области таза, алгоменорея, нарушения менструального цикла, диспареуния, цитокины.</span></p>
Responses of marine diatom-dinoflagellate competition to multiple environmental drivers: abundance, elemental and biochemical aspects
<p>A key challenge in ecology and biogeochemistry is to quantitatively determine the effects of multiple environmental factors on the diatom-dinoflagellate community and the related changes in elemental and biochemical composition. Here, laboratory experiments were conducted to investigate the responses of a diatom-dinoflagellate community to the interactions between temperature, N and P concentrations and their ratios. In particular, we focus on quantitatively assessing the changes in elemental stoichiometry and lipid biomarkers associated with the shifts of community composition. The phytoplankton species used in the experiments were the diatom<em> Phaeodactylum tricornutum</em> and the dinoflagellate <em>Prorocentrum minimum</em>. Both monocultures and bicultures of the two species were exposed to a fully factorial combination of three temperatures (12, 18 and 24°C), three N:P supply ratios (molar ratios 10:1, 24:1 and 63:1) and three nutrient concentration levels in temperature-controlled cabinets. Algal cells were harvested at steady state in semi-continuous cultures. The following parameters were analyzed: Cell density, POC, PON, POP, FAs and sterols.</p>
F in Taxonomic revision of Amerus troisi (Berlese, 1883) (Acari, Oribatida, Ameridae) using morphological and biochemical characters
F. 6. Cladogram based on UPGMA clustering of the genetic distance data showing genetic relationships between populations.
F in Taxonomic revision of Amerus troisi (Berlese, 1883) (Acari, Oribatida, Ameridae) using morphological and biochemical characters
F. 5. (a) Amerus cuspidatus n.sp.: rostral outline from dorsal (upper row) and lateral view (lower row) in some specimens of the typical population. (b, c) Morphological comparison of prodorsal setae in in A. troisi (Berlese, 1883) (b) and A. cuspidatus n. sp. (c).
F in Taxonomic revision of Amerus troisi (Berlese, 1883) (Acari, Oribatida, Ameridae) using morphological and biochemical characters
F. 2. (a–d) Amerus troisi (Berlese, 1883): (a) ventral view of topotypical specimen (×100); (b) detail of gnathosoma (×375); (c) detail of the podosomal surface (×7700); (d) antero-lateral portion of the ventral shield (×300). (e–h) Amerus cuspidatus n. sp.: (e) ventral view of whole specimen (×120); (f) ventral view of detail of rostrum and rutellum (×1350); (g) lateral portion of the ventral shield showing cuticle and cerotegumental layer (×3000); (h) posterior portion of the ventral shield with the anal plates (×315).
F in Taxonomic revision of Amerus troisi (Berlese, 1883) (Acari, Oribatida, Ameridae) using morphological and biochemical characters
F. 7. Schematic geographic distribution of the two Amerus species. The outline refers to the new species, whereas the black spots show collecting sites of Amerus troisi. The question mark concerns the doubts about specimens from Tangier and quotations for southern Spain.
F in Taxonomic revision of Amerus troisi (Berlese, 1883) (Acari, Oribatida, Ameridae) using morphological and biochemical characters
F. 3. Amerus cuspidatus n. sp.: (a) dorsal view of whole specimen (×95); (b) dorsal view of rostrum (×1900); (c) lateral view of bothridial rim (×830); (d) detail of the cuticle covered by the cerotegumental layer (×7500); (e) detail of the humeral foramen (×2400); (f) latero-abdominal gland opening (×4150); (g) dorsal view of bothridial rim (×1700); (h) microsculpture of the lateral notogastral portion (×3200); (i) detail of a notogastral seta (×3750).
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.