Find research datasets worth reusing

Data from: Effects of input data sources on species distribution model predictions across species with different distributional ranges

Species distribution models (SDMs) are a popular tool in theoretical and quantitative ecology, and constitute the most widely used modelling framework in global change science and biodiversity conservation. As main data sources, SDMs require georeferenced biodiversity observations as a response or dependent variable (e.g. species occurrence, species richness, etc) and geographic layers of environmental information as predictors or independent variables (e.g. climate, land cover, vegetation indices derived from remote sensing, etc). However, although SDMs have become one of the most important quantitative tools for addressing regular and timely biodiversity assessments worldwide, these techniques are still subject to different sources of uncertainty that have been unequally assessed. Thus, despite uncertainty related to niche-based or distribution-based models has been addressed at different stages in the modelling process, an analysis of the effect of uncertainty coming from alternative data sources on the predictive ability of SDMs is still limited. Citizen-collected species occurrence data (e.g. eBird) are often used for fitting SDMs when data from standardized and expert-supported surveys (e.g. Atlases) are unavailable. On the other hand, macroclimate variables are much more commonly used as predictors in SDMs than other sources of information coming from remote sensing data. We assessed the effects of using different data sources (in both response and predictor variables) on SDM performance across a wide range of bird species with contrasting distributional ranges in the Iberian Peninsula (Portugal and Spain). To do that, a SDM ensemble-forecasting approach was implemented by using bird data from two different data sources: the semi-structured eBird project and standardized Atlases. We fitted SDMs with three predictor types: macroclimate, remotely sensed ecosystem functional attributes (EFAs) from vegetation indices, and their combination. Species were grouped in four range size classes. We also used different evaluation metrics to better assess the uncertainty of model predictions. We then applied generalized linear mixed-effects models to test the effect on model performance of input data source across distributional range sizes while accounting for different accuracy metrics. Pairwise comparisons between range projections were used to assess their spatial similarity. Our models demonstrated the usefulness and complementarity of different input data sources when modelling species distribution across different distributional ranges. Citizen science and remote sensing data contribute to update the knowledge of the distribution of the most threatened bird species by increasing the model accuracy. These findings highlight the need to integrate different data sources to improve the model predictions at regional scale. Our framework also underlines that model uncertainty should be examined more exhaustively at early stages of the modelling process. To perfom and replicate this study, this dataset provides all needed files (as tables) to fit SDMs: i) the Iberian bird species occurrences at 10km UTM square as a response or dependent variable; ii) the geographic layers of environmental information at 10km UTM square for the Iberian Peninsula as predictors or independent variables, such as climate data, ecosystem functioning attributes (EFAs) and the combined climate and EFA data. The dataset is provided by four *.csv files named as: 1) The_Iberian_bird_species_occurrences_dataset_10km.csv 2) CHELSA_bioclimate_variables_IP10km.csv 3) MODIS_EVI-based_EFAs_IP10km.csv 4) Combined_bioclimate_EFA_dataset_IP10km.csv For a more detailed description of the main dataset and each of these subdatasets, please refer to the attached README file. Keywords: bird atlas, eBird data, ecosystem functional attributes (EFAs), Iberian Peninsula, IUCN categories, Model accuracy, MODIS EVI, narrow-ranged species, remote sensing, species distribution models (SDMs), widespread species

opencc-by-4.0Feb 2022View details →

Input features and benchmark data sets for protein complex prediction and E. coli proteome application by AF2Complex

Benchmark data sets of AF2Complex, input features for application to E. coli proteome, and predicted structural models of E. coli Ccm I as described in Predicting direct physical interactions in multimeric proteins with deep learning Mu Gao, Davi Nakajima An, Jerry M. Parks, Jeffrey Skolnick <ol> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/af2complex_bench.tar.gz">af2complex_bench.tar.gz</a>:  Benchmark data sets CP17, Dimer1193 and Oligomer562, including input features for AF2Complex/AF-Multimer, both paired and unpaired MSAs, as well as sequences, experimental structures, and results presented in the AF2Complex work (~90GB de-compressed size)</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_Ccm_I.tar.gz">ecoli_Ccm_I.tar.gz</a>: Computational models of the E. coli Ccm I system</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_set.tar.gz">ecoli_sets.tar.gz</a>: Lists of benchmark sets of positive and negative PPIs from E. coli</li> <li><a href="https://zenodo.org/api/files/087ae188-6f7a-4a71-a586-bbc7bdcc6843/ecoli_af_fea.tar.gz">ecoli_af_fea.tar.gz: </a>Pre-generated input features of E. coli proteome for protein complex prediction and modeling by AF2Complex (4,429 proteins, ~800 GB de-compressed size). This data set can be used with AF2Complex to probe the interactions of any combinations among the 4,429 proteins of E. coli.</li> </ol>

opencc-by-4.0Feb 2022View details →

data for "Horizontal connectivity in V1 : Prediction of coherence in contour and motion integration'

Data associated with the article : "Horizontal connectivity in V1 : Prediction of coherence in contour and motion integration", Plos One

Data from: Multivariate selection mediated by aridity predicts divergence of drought resistant traits along natural aridity gradients of an invasive weed

Geographic variation in the environment underpins selection for local adaptation and evolutionary divergence among populations. Because many environmental conditions vary across species' ranges, identifying the specific environmental variables underlying local adaptation is profoundly challenging. We tested whether natural selection mediated by aridity predicts clinal divergence among invasive populations of capeweed (Arctotheca calendula) that established and spread across southern Australia during the last two centuries. Using common garden experiments with two environmental treatments (wet and dry) that mimic aridity conditions across capeweed's invasive range, we estimated clinal divergence and effects of aridity on fitness and multivariate phenotypic selection in populations sampled along aridity gradients in Australia. We show that: (i) capeweed populations have relatively high fitness in aridity environments similar to their sampling locations; (ii) the magnitude and direction of selection strongly differs between wet and dry treatments, with drought stress increasing the strength of selection; and (iii) differences in directional selection between wet and dry treatments predict patterns of clinal divergence across the aridity gradient, particularly for traits affecting biomass, flowering phenology and putative antioxidant expression. Our results suggest that aridity-mediated selection contributes to trait diversification among invasive capeweed populations, possibly facilitating the expansion of capeweed across southern Australia.

Data from: Demography predicts genetic effective size in a desert stream fish community

Demographic data collected during long-term diversity monitoring and short-term ecological surveys were used to calculate summary statistics to compare with estimates of genetic effective size for nine species occurring in the Gila River, New Mexico, USA. Diversity survey data collected by New Mexico Department of Game and Fish from 1988 to 2010 were converted to presence/absence data to create detection histories for each of the nine focal species. Simple occupancy models of detection histories implemented using program PRESENCE were used to estimate the annual probability of sites becoming extirpated. A second data source from affiliated short-term ecological studies provided numbers of individuals captured and area sampled and was used to estimate mean density of adult individuals across sample sites. These data were also used to calculated an index of commonness that incorporates both relative abundance and study wide occupancy. Microsatellite genotypes from a previous study and Dryad submission were used to estimate effective number of breeders and genetic effective size (estimates included in this submission) for each species and contemporary migration rates. Migration rates were estimated using the Bayesian assignment model implemented in program BIMr. Outputs from all independent model runs and code to process output files are included with this data submission.

opencc-by-4.0Mar 2020View details →

MeDeMo - a dependency model for DNA methylation-aware transcription factor binding predictions

The uploaded fasta files contain extended reference genomes for three cell lines HepG2, GM12878, K562 (ENCODE) and two primary liver hepatocyte samples from the german epigenomics consortium (DEEP). The extended reference genomes contain information on DNA methylation in a CpG context. They can be used as input for MeDeMo, a tool to infer transcription factor binding sites incorporating not only sequence specificity but also DNA methylation. MeDeMo is available online at: <a href="http://www.jstacs.de/index.php/MeDeMo">http://www.jstacs.de/index.php/MeDeMo</a>. We considered the files ENCFF279HCL and ENCFF835NTC for GM12878, ENCFF867JRG and ENCFF721JMB for K562 as well as ENCFF064GJQ and ENCFF369YQW for HepG2. From DEEP, we considered samples 41_Hf01 and 41_Hf03 which are available through the International Human Epigenomics Consortium (IHEC). In addition, we provide all models trained using the mentioned data sets as well models for and motifs from genome wide predictions.

opencc-by-4.0Jan 2022View details →

Prediction and realisation of high mobility and degenerate p-type conductivity in CaCuP thin films Dataset

Experimental and computational datasets for this publication, including README files. To unzip, in the command line paste the following command: <blockquote> tar -xzvf cacup_data_repository_v2.tar.gz </blockquote> And repeat the command: <blockquote> tar -xzvf <file.tar.gz> </blockquote> for each necessary tar file. For any issues with accessibility, please email joe.willis.15@ucl.ac.uk.

Floral shape predicts bee-parasite transmission potential

The spread of parasites is one of the primary drivers of population decline of both managed and wild bees. Several bee parasites are transmitted by the shared use of flowers, turning floral resources into potential disease hotspots. However, we know little about how floral morphology and floral species identity affect different steps of the transmission process. Here, we used the gut parasite Crithidia bombi and its primary host, bumble bees (Bombus spp.), to examine whether floral traits or species identity better predict three basic steps of parasite transmission on flowers: feces deposition on flowers, survival of the parasite on flowers, and acquisition by a new host. We also identified which traits and/or species were most strongly associated with each step in the transmission process. We found that both trait- and species-based models fit the data on deposition of feces and survival of C. bombi on flowers, but that species-based models provided a better fit than trait-based ones. However, trait-based models were better at predicting the acquisition of C. bombi on flowers. While different species tended to support higher fecal deposition or parasite survival, we found that floral shape provided explanatory power for each of the transmission steps. When we assessed overall transmission potential, floral shape had the largest explanatory effect, with wider, shorter flowers promoting higher transmission. Taken together, our results highlight the importance of flower species identity and floral traits in disease transmission dynamics of bee parasites, and floral shape as an important predictor of overall transmission potential. Identifying traits associated with transmission potential may help us create seed mix that presents lower parasite transmission risk for bees for use in pollinator habitat.

opencc-by-4.0Feb 2022View details →

STATA do file for "Economic shocks predict increases in child wasting prevalence" in Nature Communications

This is a STATA v16 do file for the study "Economic shocks predict increases in child wasting prevalence" published in Nature Communications in 2022. To access the study data - which is not open access - please email Dr. Derek Headey at d.headey@cgiar.org

opencc-by-4.0Dec 2021View details →

Data of manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" submitted to Solar Energy

This is the data corresponding to manuscript "Forecasting day-ahead 1-minute irradiance variability from Numerical Weather Predictions" by Kreuwel et al., 2022, submitted to Solar Energy.   The file `basic_stats.tar.gz` contains a broad set of standard statistics of surface meteorology and vertical profiles. The file `sw_flux_dn_xy.tar.gz` contains spatial cross sections of downwelling shortwave radiation.

Low-cost prediction of molecular and transition state partition functions via machine learning

This dataset contains the vibrational, rotational, translational, and electronic partition functions for 35,883 organic chemistry molecular structures taken from the Grambow et. al dataset [1]. It was used to train ML deep neural networks to predict unknown transition state partition functions as well as partition functions for known molecular structures [2] The partition functions were computed at temperatures in the range T= [50, 2000] K with the rigid rotor, rigid body, harmonic oscillator approximations. Reactions involve no more than 7 C, N, or O atoms. Frequencies for the vibrational partition functions were taken from [1] where they were computed with DFT at the ωB97X-D3/def2-TZVP level of theory. For the rotational partition function, symmetry numbers were obtained by evaluating proper and improper invariant rotations of the structures. We note that structures involving two molecules were not separated: vibrational frequencies and symmetry numbers were computed for the aggregate structure. For each reaction, partition functions were calculated at 50 temperatures sampled uniformly from the inverse temperature range 1/T = [1/2000, 1/50] K-1. This corresponds to 11,961 reactions, 35,883 total structures, and 1,794,150 total partition function examples. The file Partition_Functions.tar.gz contains directories entitled “rxnXXXXXX” where XXXXXX is a reaction number identifier. Each contain three files “rXXXXXX.csv”, “pXXXXXX.csv”, “tsXXXXXX.csv” corresponding to data from the reactant (“r”), product (“p”), and transition state (“ts”) for reaction XXXXXX. Note that the directory structure and the reaction identifiers are the same as used in the original structure dataset by Grambow et al. and the corresponding structures can easily be extracted from that dataset. Each comma separated value (csv) file contains 50 rows and the following columns: <table> <tbody> <tr> <td>  Column label </td> <td>  Values </td> </tr> <tr> <td>  T [K] </td> <td>  Temperature </td> </tr> <tr> <td>  qpart_ele [unitless] </td> <td>  Electronic partition function </td> </tr> <tr> <td>  qpart_trans [unitless] </td> <td>  Translational partition function </td> </tr> <tr> <td>  qpart_vib [unitless] </td> <td>  Vibrational partition function </td> </tr> <tr> <td>  qpart_rot [unitless] </td> <td>  Rotational partition function </td> </tr> <tr> <td>  qpart [unitless] </td> <td>  Partition function </td> </tr> <tr> <td>  log_qpart_trans [unitless]  </td> <td>  Natural logarithm of translational partition function </td> </tr> <tr> <td>  log_qpart_rot [unitless] </td> <td>  Natural logarithm of rotational partition function </td> </tr> <tr> <td>  log_qpart_vib [unitless] </td> <td>  Natural logarithm of vibration partition function </td> </tr> <tr> <td>  log_qpart [unitless] </td> <td>  Natural logarithm of partition function </td> </tr> </tbody> </table>   [1]      C. A. Grambow, L. Pattanaik, and W. H. Green, “Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry,” Sci. Data, 7:1–8, 2020. [2]      Komp, E. Valleau, S. “Low-cost prediction of molecular and transition state partition functions via machine learning”, arXiv:, 2022.

Data used in: Social status does not predict in-camp integration among egalitarian hunter-gatherer men

In the last few decades, there has been much research regarding the importance of social prestige in shaping the social structure of small-scale societies. While recent studies show that social prestige may have important health consequences, little is known about the extent to which prestige translates into actual in-person interactions and proximity, even though the level of integration into such real-life social networks has been shown to have important health consequences. Here, we determine the extent to which two different domains of social prestige, popularity (being perceived as a friend by others) and hunting reputation (being perceived as a good hunter), translate into GPS-derived in- and out-of-camp proximity networks in a group of egalitarian hunter-gatherer men, the Hadza. We show that popularity and hunting reputation differ in the extent to which they are translated into time spent physically close to each other. Moreover, our findings suggest that in-camp proximity networks, which are commonly applied in studies of small-scale societies, do not show the full picture of Hadza men's social preferences. While men are in camp, neither popularity nor hunting reputation is associated with being central in the proximity network; however, when out of camp, Hadza men who are popular are more integrated in the proximity networks while men with better hunting reputations are less integrated. Overall, our findings suggest that, in order to fully understand social preferences among hunter-gatherers, both in-camp and out-of-camp proximity networks should be considered.

TestDataset for Oneat networks for detection and prediction of dividing Hela cells for different imaging modalities

Here we present the tif files to be used to test the Oneat networks for detection and prediction of division events for Hela cells imaged under different imaging modalities and presented originally here: https://zenodo.org/record/6139958#.Yjcjl3rMJD8

Analysis of biodiversity data suggests that mammal species are hidden in predictable places

Research in the biological sciences is hampered by the Linnean shortfall, which describes the number of hidden species that are suspected of existing without formal species description. Using machine learning and species delimitation methods, we built a predictive model that incorporates some 5.0 × 105 data points for 117 species traits, 3.3 × 106 occurrence records, and 9.1 × 105 gene sequences from 4,310 recognized species of mammals. Delimitation results suggest that there are hundreds of undescribed species in class Mammalia. Predictive modeling indicates that most of these hid- den species will be found in small-bodied taxa with large ranges characterized by high variability in temperature and precipitation. As demonstrated by a quantitative analysis of the literature, such taxa have long been the focus of taxonomic research. This analysis supports taxonomic hypotheses regarding where undescribed diversity is likely to be found and highlights the need for investment in taxonomic research to overcome the Linnean shortfall.

Predicted alteration of vertebrate communities in response to climate-induced elevational shifts

Aim Climate change is driving species to migrate to novel areas as current environments become unsuitable. As a result, species distributions have shifted uphill in montane ecosystems globally, leading to projections of severe impacts on upland specialist species. Heterogenous dispersal rate among shifting species could result in complex changes to community assemblages. For example, interspecific differences in dispersal ability could lead to the disruption, or creation, of species interactions and processes within communities, likely amplifying the impact of climate change on ecosystems. Here, we provide a comprehensive assessment of the impacts of climate change on communities and ecosystems by developing a novel spatially explicit approach focussing on changes in local populations. Location The Australian Wet Tropics. Method We used a spatially explicit approach to simulate the elevational shift of 7,613 community assemblages (defined at the patch level) along the elevational gradient, using empirical information about the distribution of 202 vertebrate species. We analysed changes in community structures and species co-occurrence derived from the elevational shift as a proxy for potential changes in species interactions. Results Our results showed a consistent pattern of increasing temporal β-dissimilarity between community assemblages along the elevational gradient resulting from local species extinctions. The local extinction rate was especially remarkable at high elevations, suggesting potential mass local extinctions of upland species unable to shift to the isolated mountaintops. Furthermore, the increasing local extinction rate with elevation resulted in a marked decline in species co-occurrence towards mountaintops. Main conclusions Our study highlights the escalating impact of climate change on community assemblages in response to climate-induced elevational shifts in species' ranges, providing a classic example of the "escalator to extinction". Future predictions of the impacts of climate change on ecosystems will benefit from improvements in understanding species interactions and species potential to adapt to a changing environment.

Data for Floor heating pre-on/off parameters based on Model Predictive Control feature extrapolation Paper in CLIMA2022 conference proceedings

This is a collection of time series results used to obtain all the results shown in the paper. The tags of the .csv or .mat files are self explanatory and easy to use.

Elevational and local climate variability predicts thermal breadth of mountain tropical tadpoles

The climate variability hypothesis posits that increased environmental thermal variation should promote species with broader thermal tolerance breadths, while stable environments should promote thermal specialists. This hypothesis has been tested on large spatial scales, such as latitude and elevation, but less so on smaller scales which reflect the experienced microclimate. Here, we estimated thermal tolerance limits of 75 species of amphibian tadpoles from an aseasonal tropical mountain range of the Ecuadorian Andes, distributed along a 3500 m elevational range, to test the climatic variability hypothesis at a large (elevation) and a small (microhabitat) scales. We show how species from less variable thermal habitats, such as lowlands and those restricted to streams, exhibit narrower thermal tolerance breadths than highland and pond-dwelling species respectively. Interestingly, while broader thermal tolerance breadths at large scales are driven by higher cold tolerance variation (heat-invariant hypothesis), at local scales they are driven by higher heat tolerance variation. This contrasting pattern may result from divergent selection on both thermal limits to face environmental thermal extremes at different scales. Specifically, within the same elevational window, exposure to extreme maximum temperatures could be avoided through habitat shifts from temporary ponds to permanent ponds or streams, while minimum peak temperatures remained invariable between habitats but steadily decreased with elevation. Therefore addressing the effects of habitat conversion is crucial for future research on resilience to climate change.

Basophils as a predictive hematological biomarker of canine visceral leishmaniasis in the treatment with miltefosine

BACKGROUND: Basophils are initiators of the Th2 response related to the progression of canine visceral leishmaniasis (CanL). Miltefosine has been presented as an alternative in treatment due to its leishmanicide and immunomodulatory effects. Many blood cells have been used as predictive biomarkers, but none of them focused on the immunomodulatory action of miltefosine in the different stages of the disease. OBJECTIVE: Identifying the predictive hematological biomarkers in dogs with visceral leishmaniasis treated with miltefosine. METHODS: The animals were divided into three groups: sick dogs; infected dogs, dogs exposed. The animals were submitted to differential blood cell count and CanL diagnosis, through DPP, ELISA and qPCR. The groups were monitored from the time zero (T0) before treatment, and the times twenty (T20) and thirty (T30) days after the beginning of  treatment using miltefosine at a dose of 1mL/10kg per day for 28 days. FINDINGS: Basophils showed an exponential increase in the infected and sick group with an increase of IgG, suggesting a Th2 response. In the exposed group there was reduction of basophils with increase in monocytes and IgG reduction, suggesting a Th1 response. CONCLUSIONS: We suggest basophils as a predictive biomarker of immunomodulation in the treatment of CanL with miltefosine. BACKGROUND: Basophils are initiators of the Th2 response related to the progression of canine visceral leishmaniasis (CanL). Miltefosine has been presented as an alternative in treatment due to its leishmanicide and immunomodulatory effects. Many blood cells have been used as predictive biomarkers, but none of them focused on the immunomodulatory action of miltefosine in the different stages of the disease. OBJECTIVE: Identifying the predictive hematological biomarkers in dogs with visceral leishmaniasis treated with miltefosine. METHODS: The animals were divided into three groups: sick dogs; infected dogs, dogs exposed. The animals were submitted to differential blood cell count and CanL diagnosis, through DPP, ELISA and qPCR. The groups were monitored from the time zero (T0) before treatment, and the times twenty (T20) and thirty (T30) days after the beginning of  treatment using miltefosine at a dose of 1mL/10kg per day for 28 days. FINDINGS: Basophils showed an exponential increase in the infected and sick group with an increase of IgG, suggesting a Th2 response. In the exposed group there was reduction of basophils with increase in monocytes and IgG reduction, suggesting a Th1 response. CONCLUSIONS: We suggest basophils as a predictive biomarker of immunomodulation in the treatment of CanL with miltefosine.

Protein vibrational frequencies dataset: Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks

Dataset for machine learning model, based on graph neural network, to predict protein natural frequencies using Graph Neural Networks.  Code: https://github.com/lamm-mit/ProteinMechanicsGNN Paper:  Rapid Prediction of Protein Natural Frequencies using Graph Neural Networks Kai Guo and Markus J. Buehler Digital Discovery, 2022, DOI: 10.1039/D1DD00007A