Find research datasets worth reusing

opencc-by-4.0Nov 2022View details →

Dataset of Prediction of Self-efficacy in Recognizing Deepfake based on Personality Traits

Dataset of Prediction of Self-efficacy in Recognizing Deepfake based on HEXACO Personality Traits

opencc-by-4.0Nov 2022View details →

Supplementary materials for "SPAG9 expression predicts a good prognosis in patients with clear cell renal cell carcinoma: A bioinformatics integrative analysis"

Supplementary materials for "SPAG9 expression predicts a good prognosis in patients with clear cell renal cell carcinoma: A bioinformatics integrative analysis".

opencc-by-4.0Nov 2022View details →

Saved model and preprocessed data for "CRMnet:a deep learning model for predicting gene expression from large regulatory sequence datasets"

Saved TUNet model and preprocessed training data for "CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets" To load the trained model: <pre><code class="language-python">import tensorflow as tf tf.keras.models.load_model("path to the model folder")</code></pre> for more information please find our repository: https://github.com/jiayuwen/CRMnet

Data from: Bedrock geological map predictions for Phanerozoic fossil occurrences

This is the supplementary data repository of the Paleobiology paper titled Bedrock Geological Map Predictions for Phanerozoic Fossil Occurrences. Geographically-explicit, taxonomically resolved fossil occurrences are necessary for reconstructing macroevolutionary patterns and for testing a wide range of hypotheses in the Earth and life sciences. Heterogeneity in the spatial and temporal distribution of fossil occurrences in the Paleobiology Database (PBDB) is attributable to several different factors, including turnover among biological communities, socioeconomic disparities in the intensity of paleontological research, and geological controls on the distribution and fossil yield of sedimentary deposits. Here we use the intersection of global geologic map data from Macrostrat and fossil collections in the PBDB to assess the extent to which the potentially fossil-bearing, surface-expressed sedimentary record has yielded fossil occurrences. We find a significant and moderately strong positive correlation between geologic map area and the number of fossil occurrences. This correlation is consistent regardless of map unit age and binning protocol, except at period level; the Neogene and Quaternary have non-marine map units covering large areas and yielding fewer occurrences than expected. The sedimentary record of North America and Europe yields significantly more fossil occurrences per sedimentary area than similarly-aged deposits in most of the rest of the world. However, geographic differences in area and age of sedimentary deposits lead to regionally different expectations for fossil occurrences. Using the sampling of surface-expressed sedimentary units in North America and Europe as a predictor for what might be recoverable from the surface-expressed sedimentary deposits of other regions, we find that the rest of the globe is approximately 45% as well sampled in the PBDB. Using age and area of bedrock and sampling in North America and Europe as a basis for prediction, we estimate that over 639 thousand occurrences from outside of these regions would need to be added to the PBDB to achieve global geological parity in sampling. In general, new terrestrial fossil occurrences are expected to have the greatest impact on macroevolutionary patterns.

Landscape structure, predictability of forest regeneration trajectories, and recovery rate on secondary forests

Abandonment of agricultural lands promotes the global expansion of secondary forests, which are critical for preserving biodiversity and ecosystem functions and services. Such roles largely depend, however, on two essential successional attributes, trajectory and recovery rate, which are expected to depend on landscape-scale forest cover in non- linear ways. This dataset is the synthesis outcome of 22 independent databases from studies of woody plant species recovery as part of the research project entitled "Impacts of landscape structure on secondary tropical forest regeneration". This work aimed to understand the effect of landscape-level disturbance on forest regeneration, specifically through the predictability of trajectories and the recovery rate of these forests. Using a multiscale approach and a large vegetation dataset (843 plots, 3511 tree species) from 22 secondary forest chronosequences distributed across the Neotropics, we show that successional trajectories of woody plant species richness, stem density, and basal area are less predictable in landscapes (4-km radius) with intermediate (40-60%) forest cover than in landscapes with high (>60%) forest cover. This supports theory suggesting that high spatial and environmental heterogeneity in intermediately deforested landscapes can increase the variation in key ecological factors for forest recovery (e.g. seed dispersal, seedling recruitment), increasing the uncertainty of successional trajectories. Regarding the recovery rate, only the species richness is positively related to forest cover in relatively small (1-km radius) landscapes. These findings highlight the importance of using a spatially-explicit landscape approach in restoration initiatives and suggest that these initiatives can be more effective in more forested landscapes, especially if implemented across spatial extents of 1-4 km radius.

Exploring links between climatic predictability and the evolution of within- and transgenerational plasticity

In variable environments, phenotypic plasticity can increase fitness by providing tight environment-phenotype matching. However, adaptive plasticity is expected to evolve only when the future selective environment can be predicted based on the prevailing conditions. That is, the juvenile environment should be predictive of the adult environment (within-generation plasticity) or the parental environment should be predictive of the offspring environment (transgenerational plasticity). Moreover, environmental predictability can also shape transient responses such as stress responses in an adaptive direction. Here, we test links between environmental predictability and the evolution of adaptive plasticity by combining time series analyses and a common garden experiment using temperature as a stressor in a temperate butterfly (Melitaea cinxia). Time series analyses revealed that across-season fluctuations in temperature over 48 years are overall predictable. However, within the growing season, temperature fluctuations showed high heterogeneity across years with low autocorrelations and the timing of temperature peaks was asynchronous. Most life-history traits showed strong within-generation plasticity for temperature and traits such as body size and growth rate broke the temperature-size rule. Evidence for transgenerational plasticity, however, was weak and detected for only two traits each in an adaptive and non-adaptive direction. We suggest that the low predictability of temperature fluctuations within the growing season likely disfavours the evolution of adaptive transgenerational plasticity but instead favours strong within-generation plasticity.

Space resource utilization of dominant species integrates abundance- and functional-based processes for better predictions of plant diversity dynamics

Sustainable ecosystem management relies on our ability to predict changes in plant diversity and to understand the underlying mechanisms. Empirical evidence demonstrates that abundance- and functional-based processes simultaneously explain the loss of plant diversity in response to human activities. Recently, a novel indicator based on percent cover (CoverD) and maximum height (HeightD) of the dominant plant species – Space Resource Utilization (SRUD) – has proven to give robust and better predictions of plant diversity dynamics than community biomass. Whether the superior predictive ability of SRUD is due to its capacity to simultaneously capture abundance- and functional-based processes remains unknown. Here, we tested this hypothesis by quantifying mechanistic links between changes in SRUD and biodiversity in response to nutrients and herbivores. Furthermore, we assessed the relative contribution of dominant, intermediate, and rare species to reduced density of individuals by combining null model analysis with field experiments. We found that SRUD successfully captured changes in ground-level light availability and changes in the number of individuals to predict plant diversity dynamics, and each of CoverD and HeightD partly and independently contributed to both processes. Comparative results from null model analysis and field experiments confirmed that individual losses of dominant, intermediate, and rare species followed non-random processes. Specifically, compared with random loss process, rare species lost proportionally more individuals and thus disproportionately contributed to species loss, while dominant and intermediate species lost less. Our results demonstrate that SRUD captures both abundance- and functional-based processes thus explaining why SRUD provides more accurate predictions of changes in species diversity. Given that rare species can play an important role in shaping community structure, resisting against invasion, impacting higher trophic levels, and providing multiple ecosystem functions, reducing the SRU of dominant species could alleviate the risk of exclusion of rare species by mitigating abundance- and functional-based competition processes.

WMO Polar Prediction Project: Year Of Polar Prediction Animation

This 5 minute animated video The Year of Polar Prediction (YOPP) - from research to improved environmental safety. This video explains the background, methods and aims of the Year of Polar Prediction. www.polarprediction.net Thank you to the translators: Marta Terrado (Barcelona Supercomputing Center) for the Spanish Subtitles, Luisa Cristini (Alfred Wegener Institute) for the Italian Subtitles, Juhyeong Han (World Meteorological Organization) for the Korean Subtitles, Matthieu Chevallier (Météo France) for the French Subtitles.

Plant virus SNP prediction artificial dataset Performance Study

Recent developments in high-throughput sequencing (HTS) technologies and bioinformatics have drastically changed research on viral pathogens, especially for virus discovery and monitoring. Indeed, proper monitoring of the viral population requires information on the different isolates circulating in the studied area. For this purpose, HTS technologies have greatly facilitated the generation of new genomes of the detected viruses and their comparison. Nevertheless, the bioinformatics analyses allowing the reconstruction of genomes and the detection of Single Nucleotide Polymorphisms (SNPs) can potentially create bias, although it has not been widely addressed so far.   Therefore, more knowledge is required on the limitation and possibility of predicting SNPs based on HTS-generated sequence datasets. To address this issue, we compared the ability of 14 plant virology laboratories, each employing a different bioinformatics pipeline, to detect 21 variants of pepino mosaic virus (PepMV) through large-scale Performance Testing (PT) using three artificially designed datasets. The bioinformatics analyses were divided into three key steps: reads pre-processing (quality trimming, merging …), virus identification (assembly, alignment, mapping …) and variant calling. Each step was evaluated independently through an original, step-by-step PT design with iteration between participants.    Overall, this work underlines key parameters in SNP detection and proposes recommendations for reliable variant calling for plant viruses. The identification of the closest reference, mapping parameters and manual validation of the prediction were the most impactful analysis step for the success or failure of the predictions. Strategies to improve SNPs prediction are also discussed.

Maps of predicted carbon dioxide and methane fluxes from waterbodies in the Yukon-Kuskokwim Delta, Alaska

In the Arctic, waterbodies are abundant, and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO2 and CH4 fluxes across the Yukon-Kuskokwim (YK) Delta of south-west AK. These datasets include carbon dioxide and methane dissolved concentrations and diffusive fluxes from a research watershed in the central YK Delta.

Testing the predictability of morphological evolution in contrasting thermal environments

Gaining the ability to predict population responses to climate change is a pressing concern. Using a 'natural experiment', we show that testing for divergent evolution in wild populations from contrasting thermal environments provides a powerful approach, and likely an enhanced predictive power for responses to climate change. Specifically, we used a unique study system in Iceland, where freshwater populations of threespine sticklebacks (Gasterosteus aculeatus) are found in waters warmed by geothermal activity, adjacent to populations in ambient-temperature water. We focused on morphological traits across six pairs from warm and cold habitats. We found that fish from warm habitats tended to have a deeper mid-body, a sub-terminally orientated jaw, steeper craniofacial profile, and deeper caudal region relative to fish from cold habitats. Our common garden experiment showed that most of these differences were heritable. Population age did not appear to influence the magnitude or type of thermal divergence, but similar types of divergence between thermal habitats were more prevalent across allopatric than sympatric population pairs. These findings suggest that morphological divergence in response to thermal habitat, despite being relatively complex and multivariate, are predictable to a degree<a name="_Hlk345127"></a>. Our data also suggests that the potential for migration of individuals between different thermal habitats may enhance non-parallel evolution and reduce our ability to predict responses to climate change.

opencc-by-4.0Jul 2022View details →

Air quality data from the article "Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine-learning-based prediction and assessment"

This dataset consists of 26 files. The descriptions of the files are as follows: <ul> <li>aqi_TY.csv, pm25_TY.csv, pm10_TY.csv, so2_TY.csv, no2_TY.csv and o3_TY.csv are the observed values of AQI and concentrations of PM2.5, PM10, SO2, NO2 and O3 of 36 monitoring stations used in model establish stage on TY days. The time range is June 2014 to December 2020.</li> <li>aqi_NTY.csv, pm25_NTY.csv, pm10_NTY.csv, so2_NTY.csv, no2_NTY.csv and o3_NTY.csv are the observed values of AQI and concentrations of PM2.5, PM10, SO2, NO2 and O3 of 36 monitoring stations used in model establish stage on NTY days. The time range is June 2014 to December 2020.</li> <li>station_info.csv is the detailed information of the 36 monitoring stations used in model establish stage, including station number, city, longitude and latitude.</li> <li>aqi_TY_testing.csv, pm25_TY_testing.csv, pm10_TY_testing.csv, so2_TY_testing.csv, no2_TY_testing.csv and o3_TY_testing.csv are the observed values of AQI and concentrations of PM2.5, PM10, SO2, NO2 and O3 of 3 monitoring stations used for testing the model on TY days. The time range is June 2014 to December 2020.</li> <li>aqi_NTY_testing.csv, pm25_NTY_testing.csv, pm10_NTY_testing.csv, so2_NTY_testing.csv, no2_NTY_testing.csv and o3_NTY_testing.csv are the observed values of AQI and concentrations of PM2.5, PM10, SO2, NO2 and O3 of 3 monitoring stations used for testing the model on NTY days. The time range is June 2014 to December 2020.</li> <li>sta_testing.csv is the detailed information of the 3 monitoring stations used for testing the model, including station number, city, longitude and latitude.</li> </ul>

Data from: A mix of old British and modern European breeds: Genomic prediction of breed composition of smallholder pigs in Uganda

Pig herds in Africa comprise genotypes ranging from local ecotypes to commercial breeds. Many animals are composites of these two types and the best levels of crossbreeding for particular production systems are largely unknown. These pigs are managed without structured breeding programs and inbreeding is potentially limiting. The objective of this study was to quantify ancestry contributions and inbreeding levels in a population of smallholder pigs in Uganda. The study was set in the districts of Hoima and Kamuli in Uganda and involved 422 pigs. Pig hair samples were taken from adult and growing pigs in the framework of a longitudinal study investigating productivity and profitability of smallholder pig production. The samples were genotyped using the porcine GeneSeek Genomic Profiler (GGP) 50K SNP Chip. The SNP data was analyzed to infer breed ancestry and autozygosity of the Uganda pigs. The results showed that exotic breeds (modern European and old British) contributed an average of 22.8% with a range of 2–50% while "local" blood contributed 69.2% (36.9–95.2%) to the ancestry of the pigs. Runs of homozygosity (ROH) greater than 2 megabase (Mb) quantified the average genomic inbreeding coefficient of the pigs as 0.043. The scarcity of long ROH indicated low recent inbreeding. We conclude that the genomic background of the pig population in the study is a mix of old British and modern pig ancestries. Best levels of admixture for smallholder pigs are yet to be determined, by linking genotypes and phenotypic records.

Dataset for "Does fluid structure encode predictions of glassy dynamics?"

This folder contains data in support of "Does fluid structure encode predictions of glassy dynamics?", T. M. Obadiya and D. M. Sussman, arXiv preprint arXiv:2211.00604, (2022). It has three subfolders (described below); all data has been stored in Python's pickle format using protocol version 4, with the pickled files structured as dictionaries. ## "Trajectories/" folder Each file in this folder is the raw saved output of a molecular dynamics simulation of an 80:20 Kob-Andersen mixture. To generate the trajectories of particles in this mixture, we first equilibrated a system with random initial conditions for 5000 tau at a temperature of T=0.45. We used the final configuration of this as an initial seed for our other simulations: a snapshot was loaded as the initial configuration for our other simulations, each of which was allowed to equilibrate for 1000 tau at its target temperature. The simulations were done in an NVT ensemble with the coupling constant of the thermostat set to 10 tau. The simulations were evolved using a timestep of dt = 0.001 tau, with frames saved every 1 tau. Each file name contains the temperature at which the simulation was run, and all simulations are of a set of N=4096 particles. The pickle dictionary for each file contains three elements. "Box_size" contains an array of three elements describing the cubic box simulation box's size along the x, y, and z axes. "Particle types" has a list of 4096 integers with 0 corresponding to a particle of type A and 1 corresponding to a particle of type B. "Positions" contains the particle positions for every frame in the saved trajectory. ## "Training_data_V1/" folder This folder contains the training data for five training temperatures used in the above paper. The paper considered two different training sets (defined by using either phop or cumulative squared displacement as the dynamical label), and the training sets corresponding to this choice of dynamical label are in the corresponding subfolders. Within each subfolder, the file name indicates the temperature at which the data were obtained. The pickle dictionary for each file contains two elements. "X_train_rawdata" for every particle in the training set, this contains a list of 100 local structural features (corresponding to the AA and AB local radial distribution functions described in the paper above -- all particles in the training set are particles of type A); the "rawdata" part of the name indicates that these features have not been standardized. "Y_train" is the corresponding dynamical label, with 1 indicating a large value of dynamical label and a -1 indicating a small value of the dynamical label. ## "Training_data_V2/" folder This folder contains similar training data for the same five training temperatures, but does not make any assumptions about how to define the local structural features. The pickle dictionaries have the same names as in the Training_data_V1, and the "Y_train" element has the same structure. Here, though, "X_train_rawdata" for each element contains a list of 4-vectors that correspond to the positions of all particles within 5 sigma of the particle whose dynamical label is being considered. The first 3 elements of each 4-vector give the relative separation between the target particle and its neighboring particle, and the 4th element is the particle type of the neighboring particle (0 for particles of type A, and 1 for particles of type B).

Predictive Search Model of Flocking for Quadcopter Swarm in the Presence of Static and Dynamic Obstacles

The folder includes experimental data for the paper titled "Predictive Search Model of Flocking for Quadcopter Swarm in the Presence of Static and Dynamic Obstacles". In the paper, we present a Predictive Search Model (PSM) for flocking with Heading and Speed Shared (HSS) and Heading and Speed Unshared (HSU) prediction methods. We compare the performance of PSM with Potential Field Model (PFM) in the presence of static and dynamic obstacles in simulation. Also, we validate the performance of PSM with a quadcopter swarm indoors. The 'simulation experiments' folder includes simulation experiment data and MATLAB scripts that can simulate the experiments and provide plots for analysis. The 'quadcopter experiments' folder includes quadcopter experiment data and MATLAB scripts that can simulate the experiments and provide plots for analysis.

opencc-by-4.0Aug 2022View details →

Predicting and modeling protein-protein interactions in E. coli envelopome

Structural models and Supplementary data described in the reference: Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria. Mu Gao, Davi Nakajima An, and Jeffrey Skolnick. eLife, 2022. 11: p. e82885. List of files: <ul> <li>af2c_fea_220331.tar -- A tarball of input features of full E coli proteome to AF2Complex version 1.3.0. Feature files are pickled and gzipped, which AF2Complex v1.3 takes as input directly.</li> </ul> Results of an application to E coli envelopome on four query proteins from the outer membrane biogenesis pathway. <ul> <li>Supplementary Data.xlsx -- Virtual PPI screening results of PpiD, YfgM, SurA, and BamA.</li> <li>screening_top1_models.zip -- Compressed top 1 dimeric models of top hits from the PPI screening. Note that these models are unrelaxed.</li> <li>predicted structural models.zip -- Compressed structural models of supercomplexes formed in the OMP biogenesis pathway described in the reference.</li> </ul>

A direct comparison of ecological theories for predicting the relationship between plant traits and growth

Despite long-standing theory for classifying plant ecological strategies, limited data directly links organismal traits to whole-plant growth rates. We compared trait-growth relationships based on three prominent theories: growth analysis, Grime's competitive-stress tolerant-ruderal (CSR) triangle, and the leaf economics spectrum (LES). Under these schemes, growth is hypothesized to be predicted by traits related to relative biomass investment, leaf structure or gas exchange, respectively. We also considered traits not included in these theories, but that might provide potential alternative best predictors of growth. In phylogenetic analyses of 30 diverse milkweeds (Asclepias spp.) and 21 morphological and physiological traits, growth rate (total biomass produced per day) varied 50-fold and was best predicted by biomass allocation to leaves (as predicted by growth analysis) and the CSR traits of leaf size and leaf dry matter content. Total leaf area and plant height were also excellent predictors of whole-plant growth rate. Despite two LES traits correlating with growth (mass-based leaf nitrogen and area-based leaf phosphorus contents), these were in the opposite direction predicted by LES, such that higher N and P contents corresponded to slower growth. The remaining LES traits (e.g., leaf gas exchange) were not predictive of plant growth rates. Overall, differences in growth rate were driven more by whole-plant characteristics such as biomass fractions and total leaf area than individual leaf-level traits such as photosynthetic rate or specific leaf area. Our results are most consistent with classical growth analysis - combining leaf traits with whole-plant allocation to best predict growth. However, given that destructive biomass measures are often not feasible, applying easy-to-measure leaf traits associated with the CSR classification appear more predictive of whole plant growth than LES traits. Testing the generality of this result across additional taxa would further improve our ability to predict whole-plant growth from functional traits across scales.

Data for: A new paradigm for medium-range severe weather forecasts: Probabilistic random forest-based predictions

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4–8. RFs are trained with ~9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time-lagging. Validated RF models are tested with ~1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with diminishing skill thereafter. The RF-based forecasts exhibit tendencies to slightly underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time-lagging acts to expand the forecast areas, increasing resolution but decreasing overall skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range.

Data from: Predicting and measuring decision rules for social recognition in a Neotropical frog

Many animals use signals to recognize familiar individuals but risk mistakes because the signal properties of different individuals often overlap. Further, outcomes of correct and incorrect decisions yield different fitness payoffs, and animals incur these payoffs at different frequencies depending on interaction rates. To understand how signal variation, payoffs, and interaction rates shape recognition decision rules, we studied male golden rocket frogs, which recognize the calls of territory neighbors and are less aggressive to neighbors than to strangers. We first quantified patterns of individual variation in call properties and predicted optimal discrimination thresholds using signal variation. We then measured thresholds for discriminating between neighbors and strangers using a habituation-discrimination field playback experiment. Territorial males discriminated between calls differing by 9% to 12% in temporal properties, slightly higher than the predicted thresholds (5-10%). Finally, we used a signal detection theory model to explore payoff and interaction rate parameters and found that the empirical threshold matched those predicted under ecologically realistic assumptions of infrequent encounters with strangers and relatively costly missed detections of strangers. We demonstrate that receivers group continuous variation in vocalizations into discrete social categories and that signal detection theory can be applied to understand evolved decision rules.