Find research datasets worth reusing

opennotspecifiedDec 2020View details →

Figure 2 in A high-resolution growth series of Tyrannosaurus rex obtained from multiple lines of evidence

Figure 2 Ontogram of Tyrannosaurus rex showing growth stages, synontomorphies, individual variation, individual specimens, and chronological ages. Arrowhead points to the most mature specimen and the direction of the entire ontogenetic axis; that is, the least mature specimen is at the lower left whereas the most mature specimen is at the upper right. Asterisk indicates the type specimen. Individual variation occurs as progressions until young adulthood, where reversals are first seen. The maximum amount of change occurs at growth stages 5 and 6, which corresponds to the transition from a long and low skull and jaws to a deep and stout skull frame; this event, marked by the concentration of an extreme number of changes, is evidence that the ontogeny of T. rex is metamorphic (sensu Rose & Reiss, 1993). Each circle represents a numbered growth stage; these numbers do not correspond to those seen in Fig. 12. The star at growth stage 7 marks the ~3,000 kg threshold that separates T. rex from its closest, but smaller, relatives. Color key: red, small juveniles; orange, large juveniles; yellow, subadults; green, young adults; blue, adults; violet, senescent adults. See text for definition of growth categories. Skulls are to scale; AMNH FARB 5027 is scaled to a premaxilla to quadrate length of 1.3 m. Full-size DOI: 10.7717/peerj.9192/fig-2

opencc-zeroFeb 2022View details →

Novel genomic insights into body size evolution in cetaceans and a resolution of Peto's Paradox

Cetaceans (whales, dolphins, and porpoises) have undergone a radical transformation from the typical terrestrial mammalian body plan to a streamlined one while exhibited dramatic inter-specific size ranges. However, the molecular mechanisms underlying the diversifying evolution of cetacean body size are largely unknown. Here, by using genome and phenotypic data from 22 cetaceans, we seek to investigate the genome-wide gene-phenotype correlation and to explore the genetic basis under the high diversity of body size in cetaceans. Results of the functional enrichment showed that body size-related genes in cetaceans were enriched in pathways associated with immunity, cell growth, and metabolism, suggesting their potential roles in the diversifying evolution of body size in cetaceans. A series of genes was also found coevolution with body size that are mainly involved in immune surveillance, tumor suppression function, and development of 'cheater' tumors. This in turn suggests that the genes play a role in tumor control and thus resolve Peto's paradox, a finding that the expansion in body size and thereby cell number does not correlate with increases in cancer incidence in larger whales. The present study could provide novel insights into the evolution of great body size variation in cetaceans.

opencc-by-4.0Jul 2021View details →

Data for "On the resolution-dependence of cloud fraction in radiative-convective equilibrium"

Cloud-resolving model output and analysis scripts for the paper "On the resolution-dependence of anvil cloud fraction and precipitation efficiency in radiative-convective equilibrium"

opencc-by-4.0Feb 2022View details →

Low Resolution Thermal Imaging Dataset

The dataset contains low resolution thermal images corresponding to various sign language digits represented by hand and captured using the Omron D6T thermal camera with a resolution of 32x32.

opencc-by-4.0Feb 2022View details →

Dataset 1 for: Multi-eGO: an in-silico lens to look into protein aggregation kinetics at atomic resolution

Dataset Molecular dynamics simulation trajectories of TTR peptide monomers and aggregation kinetics: <ul> <li>Amber99sb_disp: TTR monomer in explicit solvent using amber99disp force field.</li> <li>multi-GO-monomer: TTR monomer simulation using the multi-GO force field.</li> <li>multi-eGO-monomer: TTR monomer simulation using the multi-eGO ensemble force field.</li> <li>multi-eGO-XXmM-Y: aggregation kinetics simulations of TTR using the multi-eGO force field at XXmM concentration replicate Y.</li> <li>.ipynb files: analysis script employed for the aggregation kinetics simulations.</li> <li>TTR aggregation kinetics movies.</li> </ul>

opencc-zeroFeb 2022View details →

Mapping breeding bird species richness at management-relevant resolutions across the United States

Human activities alter ecosystems everywhere, causing rapid biodiversity loss and biotic homogenization. These losses necessitate coordinated conservation actions guided by biodiversity and species distribution spatial data that cover large areas yet have fine-enough resolution to be management-relevant (i.e., ≤ 5 km). However, most biodiversity products are too coarse for management or are only available for small areas. Furthermore, many maps generated for biodiversity assessment and conservation do not explicitly quantify the inherent tradeoff between resolution and accuracy when predicting biodiversity patterns. Our goals were to 1) generate predictive models of overall breeding bird species richness and species richness of different guilds based on nine functional or life history-based traits across the conterminous US at three resolutions (0.5, 2.5, and 5 km), and 2) quantify the tradeoff between resolution and accuracy, and hence relevance for management, of the resulting biodiversity maps. We summarized eighteen years of North American Breeding Bird Survey data (1992-2019) and modeled species richness using random forests, including 66 predictor variables (describing climate, vegetation, geomorphology, and anthropogenic conditions), 20 of which we newly derived. Among the three spatial resolutions, the percent variance explained ranged from 27% to 60% (median = 54%; mean = 57%) for overall species richness and 12% to 87% (median = 61%; mean = 58%) for our different guilds. Overall species richness and guild-specific species richness were best explained at 5-km resolution using approximately 24 predictor variables based on percent variance explained, symmetric mean absolute percentage error, and root mean squared error values. However, our 2.5-km resolution maps were almost as accurate and provided more spatially detailed information, which is why we recommend them for most management applications. Our results represent the first consistent, occurrence-based, and nationwide maps of breeding bird richness with a thorough accuracy assessment that are also spatially detailed enough to inform local management decisions. More broadly, our findings highlight the importance of explicitly considering tradeoffs between resolution and accuracy to create management-relevant biodiversity products for large areas.

opencc-by-4.0Feb 2022View details →

Differential analysis of gene regulation at transcript resolution by RNA-Seqcount table

Expression profiling by high throughput sequencing

opencc-by-4.0Dec 2021View details →

Lipidomics and metabolomics datasets for "Adverse effects of arsenic uptake in rice metabolome and lipidome revealed by untargeted liquid chromatography coupled to mass spectrometry (LC-MS) and regions of interest multivariate curve resolution"

Files description Raw files for lipidomics and metabolics studies on the impact of arsenic exposure on rice growth. File details on the worksheets lipids_files.xlsx and metabolomics_files.xlsx Files have been organized as follows: Lipidomics <blockquote> 1) Control samples: lip_controls.rar 2) Watering low As exposure: lip_water_1.rar 3) Watering high As exposure: lip_water_1000.rar 4) Soil low As exposure: lip_soil_5.rar 5) Soil high As exposure: lip_soil_50.rar 6) QC samples: lip_qcs.rar </blockquote> Metabolomics (positive ionization mode) <blockquote> 1) Control samples: met_pos_controls.rar 2) Watering low As exposure: met_pos_water_1.rar 3) Watering high As exposure: met_pos_water_1000.rar 4) Soil low As exposure: met_pos_soil_5.rar 5) Soil high As exposure: met_pos_soil_50.rar 6) QC samples: met_pos_qcs.rar </blockquote> Metabolomics (negative ionization mode) <blockquote> 1) Control samples: met_neg_controls.rar 2) Watering low As exposure: met_neg_water_1.rar 3) Watering high As exposure: met_neg_water_1000.rar 4) Soil low As exposure: met_neg_soil_5.rar 5) Soil high As exposure: met_neg_soil_50.rar 6) QC samples: met_neg_qcs.rar   </blockquote>   Experimental details <blockquote> Arsenic Exposure Arsenic was supplied through two main routes: watering with contaminated water or soil containing arsenic. In addition, this new study includes metabolomic as well as lipidomic analysis, in order to have a more global overview of arsenic exposure. For the watering treatment, during the first 11 days, rice was irrigated with Milli-Q water. From that day until harvesting, plants were watered with 1 and 1000 μM of As (V) for the two concentration levels of exposure, and with Milli-Q water for control samples. The lowest concentration was established at 1 μM as it is the limit of the acceptable arsenic concentration in water by European legislation. The upper concentration was set at 1000 μM, a threshold established to ensure that the experiment was performed under sub-lethal arsenic concentration for the plant, based on previous studies. For the soil treatment, two containers were prepared with 1 kg of soil two days before planting. Soil from the container was exposed to two arsenic concentration levels (5 and 50 mg L-1). Once sowing, rice was irrigated the whole growth period with a solution containing 0.001 μM of As (V). The lowest arsenic limit in this treatment was set at 5 mg L-1 as a maximum value of common arsenic leaches without toxic characteristics, although background soil content of arsenic varies between one and 40 ppm according to the US food and drug administration (FDA) report. The highest arsenic limit was established to 50 mg L-1, as a considerably high arsenic content in the soil, slightly above the maximum frequently encountered levels. Lipidomic Analysis The lipidomic analysis was performed using a Waters Acquity UPLC system (Waters Corporation, MA, USA), connected to a Waters LCT Premier orthogonal accelerated time of flight mass spectrometer (Waters), operated in both positive and negative electrospray (ESI) ionization modes. Full scan spectra were acquired from 50 to 1500 Da. The chromatographic column employed was a Kinetex C8 (100 x 2.1 mm, 1.7 μm) (Phenomenex) under the following conditions (already used in [47]): temperature at 30˚C, injection volume at 10 μL, and flow rate at 0.3 mL min-1. Mobile phases selected were (A) MeOH 1mM ammonium formate, and (B) H2O 2mM ammonium formate, both at 0.2% formic acid. The gradient started at 80% A, increased to 90% A in 3 min, from 3 to 6 min remained at 90% A, changed to 99 % A until minute 15, remained constant 1 min, and returned to initial conditions until minute 20. Metabolomic analysis The metabolomic analysis was performed using a Waters Acquity UPLC system connected to a Q-Exactive (Thermo Fisher Scientific, Hemel Hempstead, UK) equipped with a quadrupole-Orbitrap mass analyzer. Electrospray (ESI) was used as an ionization source in both positive and negative ion modes. Full scan mass range was set from m/z 90 to 1000, and all ion fragmentation (AIF) was performed with normalized collision energy (NCE) of 35 eV. The column employed was an HILIC TSK gel amide-80 column (250 x 2.0 mm i.d., 5 μm) provided by Tosoh Bioscience (Tokyo, Japan), under the following experimental conditions (already employed in [45]): flow rate at 0.15 mL min-1, at room temperature, and 5 μL injection volume. Mobile phases were (A) AcN, and (B) 5 mM ammonium acetate, adjusted at pH 5.5 with acetic acid. The gradient employed was: starting conditions at 25% B, then increased until 30% B in 8 min; a 60% B was reached at 10 min, held for 2 min more and then back to 25% B until minute 14 min; lastly, a re-equilibration step was added and from 14 to 20 min at 25% B. </blockquote>   Funding: This research was funded by the Spanish Ministry of Science and Innovation (MCI, Grant CTQ2017-82598-P) and Severo Ochoa Project CEX2018-000794-S (funded by MCIN/AEI/ 10.13039/501100011033), and supported from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). MPC was funded by a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP).

opencc-by-4.0Mar 2022View details →

TROPOMI SIF high resolution data at 0.005° for CONUS as estimated by the convolutional neural network SIFnet

We develop a Convolutional Neural Network, named SIFnet, that increases the spatial resolution of SIF from the TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations together with a broad range high resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis. Temporal coverage: April 2018 until March 2021, 16 day time steps Data for other regions can be requested and produced by the authors. Please refer for further information to:  Gensheimer, J., Turner, A. J., Köhler, P., Frankenberg, C., & Chen, J. (2022). A Convolutional Neural Network for Spatial Downscaling of Satellite-Based Solar-Induced Chlorophyll Fluorescence (SIFnet). A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet). Biogeosciences, 19(6), 1777-1793. DOI: https://doi.org/10.5194/bg-19-1777-2022

opencc-zeroDec 2022View details →

Worldclim 2.1 versus Worldclim 1.4: climatic niche and grid resolution affect between-version mismatches in habitat suitability models predictions across Europe

The influence of climate on the distribution of taxa has been extensively investigated in the last two decades through Habitat Suitability Models (HSMs). In this context, the Worldclim database represents an invaluable data source as it provides worldwide climate surfaces for both historical and future time horizons. Thousands of HSMs-based papers have been published taking advantage of Worldclim 1.4, the first online version of this repository. In 2017, Worldclim 2.1 was released. Here, we evaluated spatially explicit prediction mismatch at continental scale, focusing on Europe, between HSMs fitted using climate surfaces from the two Worldclim versions (between-version differences). To this aim, we simulated occurrence probability and presence-absence across Europe of four virtual species (VS) with differing climate-occurrence relationships. For each VS, we fitted HSMs upon uncorrelated bioclimatic variables derived from each Worldclim version at three grid resolutions. For each factor combination, HSMs attaining sufficient discrimination performance on spatially independent test data were projected across Europe under current conditions and various future scenarios, and importance scores of the single variables were computed. HSMs failed in accurately retrieving the simulated climate-occurrence relationships for the climate-tolerant VS and the one occurring under a narrow combination of climatic conditions. Under current climate, noticeable between-version prediction mismatch emerged across most of Europe for these two VSs, whose simulated suitability mainly depended upon diurnal or yearly variability in temperature; differently, between-version differences were more clustered toward areas showing extreme values, like mountainous massifs or southern regions, for VSs responding to average temperature and precipitation trends. Under future climate, the chosen emission scenarios and Global Climate Models did not evidently influence between-version prediction discrepancies, while grid resolution synergistically interacted with VSs' niche characteristics in determining extent of such differences. Our findings could help in re-evaluating previous biodiversity-related works relying on geographical predictions from Worldclim-based HSMs.

opencc-by-4.0Mar 2022View details →

Data set for FSE 2022 Submission Program Merge Conflict Resolution via Neural Transformers

Data set for FSE 2022 Submission Program Merge Conflict Resolution via Neural Transformers

opennotspecifiedApr 2022View details →

FIGURE 3. High-resolution X in Systematic revision of Afrogecko ansorgii (Boulenger, 1907) (Sauria: Gekkonidae) from western Angola

FIGURE 3. High-resolution X-ray computed tomographies of Bauerius ansorgii (PEM R23911). Views in (A) dorsal and (B) lateral aspects of body. Detailed CT-scan of (C) pelvic and (D) pectoral girdle in dorsal and ventral view, respectively.

opennotspecifiedApr 2022View details →

FIGURE 2. High-resolution X in Systematic revision of Afrogecko ansorgii (Boulenger, 1907) (Sauria: Gekkonidae) from western Angola

FIGURE 2. High-resolution X-ray computed tomographs of skull of Bauerius ansorgii (PEM R23912). Detailed views in (A) lateral, (B) medial, (C) dorsal and (D) ventral.

opencc-by-4.0Mar 2022View details →

E3SM high resolution simulations

This dataset is E3SM simulation output with different hozitional and vertical resolutions.

Comparability of skeletal fibulae surfaces generated by different source scanning (dual-energy CT scan vs. high resolution laser scanning) and 3D geometric morphometric validation

SI_Appendix 1. Matrix of Cartesian coordinates of analyzed specimens. SI_Appendix 2. Sample list and acquisition methods.

opencc-zeroApr 2022View details →

Bioclimatic, soil, terrain, distance to Native American settlement, and historical tree taxon relative abundance data at 8-km resolution for the northeastern United States

Researchers have debated impacts of past Native American land use on forests including upon tree species composition in northeastern United States (US), with estimates of impacts ranging from local to regional extent. This study examines tree relative abundances in the northeastern US (approx. 420,000 km2) to assess whether Native Americans influenced geographic distributions of 18 tree taxa prior to Euro-American settlement. We used boosted regression trees to model abundance patterns and to assess the importance of distance-based proxies of Native American land use versus environmental variables. We trained models that included and excluded distance-based proxies. Abundance estimates from original land survey records (1650-1850 CE) were acquired for taxa at 8 km spatial resolution, and related to Native American settlement locations (1500-1800 CE) and 27 environmental variables. When evaluated upon test data, regional-scale models of relative abundance that included distance-based proxies performed only slightly better than models that excluded them, with mean improvements in RMSE of 0.1 percentage points. Models suggest that Native American land use modestly altered the relative abundance of taxa locally, extending no more than 50 km from settlement. Models also suggest slight increases near settlement of a few percentage points in relative abundance for fire-tolerant and/or dietary taxa (e.g. oak, hickory, and pine), and for early-successional taxa (e.g. ash). Past Native American land use had no detectable effect on forest composition across a regional extent, but increased the abundance of fire-tolerant, shade-intolerant, and nut-producing trees locally. The Excel-format (.xlsx) dataset here provides the training and testing data for the BRT models in this publication. It contains relative abundances of the 18 tree taxa expressed as a fraction of total number of trees. It also provides bioclimatic (e.g. annual temperature, annual preciptation), soil (e.g. pH, percent sand), terrain (e.g. slope), and Native American (e.g. distance to settlement) variables developed from multiple sources. A column also indicates whether an observation was part of the training data or the test data. See the publication and its Supporting Information for full details on the acquisition and processing of these data, as well as the original providers of the data. If using these data, please cite both this dataset's corresponding article, as well as the original providers of the data. Also provided are ascii-format (.asc) gridded data layers that were used for making spatial predictions of taxon relative abundances from BRT models. The original creators/providers of the relative abundance, bioclimatic, soil, terrain, and Native American data or layers used for creating the gridded data in this study are: Abel, T. (2016). The Iroquoian occupations of Northern New York: A summary of current research. Ontario Archaeology, 96, 65–75. CIFAS. (2017). Map Of First Nations in New Brunswick. Comitas Institute for Anthropological Study. <a href="http://cifas.us/first-nations-maps/">http://cifas.us/first-nations-maps/</a> Grumet, R. S. (1995). Historic Contact: Indian People and Colonists in Today's Northeastern United States in the Sixteenth through Eighteenth Centuries. University of Oklahoma Press. Jordan, K. A. (2013). Incorporation and colonization: Postcolumbian Iroquois satellite communities and processes of indigenous autonomy. American Anthropologist, 115(1), 29–43. Milner, G. R., & Chaplin, G. (2010). Eastern North American population at ca. A.D. 1500. American Antiquity, 75(4), 707–726. NASA. (2000). SRTM 90m Digital Elevation Database v4.1. <a href="https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/">https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/</a> O'Donnell, M. S., & Ignizio, D. A. (2012). Bioclimatic Predictors for Supporting Ecological Applications in the Conterminous United States (Data Series 691; p. 10). U.S. Geological Survey. <a href="https://www.sciencebase.gov/catalog/item/4fe0f9f4e4b05d4ed81d9392">https://www.sciencebase.gov/catalog/item/4fe0f9f4e4b05d4ed81d9392</a> Paciorek, C. J., Goring, S. J., Thurman, A. L., Cogbill, C. V., Williams, J. W., Mladenoff, D. J., Peters, J. A., Zhu, J., & McLachlan, J. S. (2016). Statistically-estimated tree composition for the northeastern United States at the time of Euro-American settlement. PLoS ONE, 11(2), e0150087. <a href="https://doi.org/10.1371/journal.pone.0150087">https://doi.org/10.1371/journal.pone.0150087</a> Peters, M. P., Iverson, L. R., Prasad, A. M., & Matthews, S. N. (2013). Integrating Fine-scale Soil Data into Species Distribution Models: Preparing Soil Survey Geographic (SSURGO) Data from Multiple Counties (General Technical Report NRS-122; p. 70). U.S. Forest Service. <a href="https://www.fs.usda.gov/treesearch/pubs/45308">https://www.fs.usda.gov/treesearch/pubs/45308</a>

Dataset 3 for: Multi-eGO: an in-silico lens to look into protein aggregation kinetics at atomic resolution

Dataset Multi-GO Molecular dynamics simulation trajectories of TTR peptide and aggregation kinetics; Multi-eGO oligomer structures and trajectories: <ul> <li>multi-GO-XXmM: aggregation kinetics simulations of TTR using the multi-eGO force field at XXmM concentration.</li> <li>TTR structures and trajectories of oligomers from dimers to decamers.</li> </ul>

High-resolution imaging and manipulation of endogenous AMPA receptor surface mobility during synaptic plasticity and learning

Data set for the MS

Ozone Super Resolution Video Supplement

Video Supplement for: "Downscaling Atmospheric Chemistry Simulations with Physically Consistent Deep Learning," by Andrew Geiss, Sam J. Silva, and Joseph C. Hardin, (2022)