Find research datasets worth reusing

opencc-by-nc-4.0Feb 2023View details →

Theoretical prediction and shape-controlled synthesis of two-dimensional semiconductive Ni3TeO6

Check the reference: Fernández-Catalá, J.; Kistanov, A., A.; Bai, Y.; Singh, H.; Cao, W. npj 2D Mater. Appl. 2023, 7, 1, 48

openother-openFeb 2023View details →

Precise predictions and new insights for atomic ionization from the Migdal effect

Probabilities for atomic ionisation via the Migdal effect using Dirac-Hartee-Fock wavefunctions. Dataset accompanies the publication P. Cox, M. J. Dolan, C. McCabe, H. M. Quiney, Precise predictions and new insights for atomic ionization from the Migdal effect, Phys. Rev. D (2023).

opencc-by-4.0Jun 2014View details →

Fig. 2 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa

Fig. 2. Modified Red-top™ fly trap used in surveys.

opencc-by-4.0Jun 2014View details →

Fig. 4 in Predicting the geographic distribution of Lucilia sericata and Lucilia cuprina (Diptera: Calliphoridae) in South Africa

Fig. 4. Distribution of human population in South Africa in 2011 in terms of density.

opencc-zeroFeb 2023View details →

Plant functional traits predict heterogeneous distributional shifts in response to climate change

Climate change is causing the rapid redistribution of vegetation as plant species move to track their climatic optima. Despite a global trend of upward movement in latitude and elevation, there is extensive heterogeneity among species and locations, with few emerging generalizations. Greater generalization may be achieved by considering multidimensional changes in species' distributions as well as incorporating ecologically relevant functional traits into studies of range shifts. To better understand how recent changes in climate are influencing the elevational distribution of plant species and how species' functional traits mediate distributional changes, we resampled a 2,438-meter elevation transect spanning a distance of 16 kilometers which encompasses desert scrub, pinyon-juniper woodland, chaparral, and coniferous forest plant communities. Over the last 42 years, total perennial cover and species' average cover increased at lower elevations and decreased at higher elevations while the average elevational leading-edge increased 116 m and the elevational rear edge decreased 84 m. Notably, these changes were mediated by species' functional traits, where species exhibiting more conservative traits (lower SLA, greater δ13C, larger seed mass) and taller height shifted upward in their leading-edge range limit, average elevation, and trailing edge range limit, while declining in abundance at the median and trailing edge of their range. Species possessing more acquisitive traits (higher SLA, lower δ13C, smaller seed mass) and shorter height shifted downward and increased in abundance at their trailing edge, with increases in their total range size. Our results provide clear evidence that heterogeneous range dynamics under recent climate change can be generalized by considering ecologically relevant plant functional traits, and how they respond to localized climate exposure. Further, by documenting changes across a steep ecological gradient comprising a large aridity gradient, we show divergent patterns for plants occupying contrasting positions along the global spectrum of plant form and function, which provides critical insight into how trait-mediated changes under increasing aridity will impact ecosystem functioning.

opencc-zeroFeb 2023View details →

Mercury isotope clocks predict coastal residency and migration timing of hammerhead sharks

1. The management of migratory taxa relies on the knowledge of their movements. Among them, ontogenetic habitat shift, from nurseries to adult habitats, is a behavioral trait shared across marine taxa allowing resource partitioning between life stages and reducing predation risk. As this movement is consistent over time, characterizing its timing is critical to implement efficient management plans, notably in coastal areas to mitigate the impact of fisheries on juvenile stocks. 2. In the Mexican Pacific, habitat use of the smooth hammerhead shark (Sphyrna zygaena) is poorly described, while the species is heavily harvested. Given the large uncertainties associated with the timing of out-migration from coastal nursery grounds to offshore waters prior to reproductive maturity, a more precise assessment of smooth hammerhead shark movements is needed. 3. Photochemical degradation of mercury imparts mass-independent isotope fractionation (Δ199Hg) which can be used to discriminate between neonate coastal shallow habitats and the offshore deep foraging patterns of late juveniles. Here, we present the application of muscle Δ199Hg as molecular clocks to predict the timing of ontogenetic habitat shifts by smooth hammerhead sharks, based on their isotopic compositions at the initial and arrival habitats and on muscle isotopic turnover rate. 4. We observed decreases in Δ199Hg values with shark body length, reflecting increasing reliance on offshore mesopelagic prey with age. Coastal residency estimates indicated that smooth hammerhead sharks utilize coastal resources for two years prior to offshore migration, suggesting a prolonged residency in these ecosystems. Policy implications: This study demonstrates how mercury stable isotopes and isotopic clocks can be implemented as a complementary tool for stock management by predicting the timing of animal migration—a key aspect in the conservation of marine taxa. In the Mexican Pacific, fishing pressure on shark species occurs in coastal habitats depleting juvenile stocks. Consequently, management decision support tools are imperative for effectively maintaining early life stage population levels over time. The finding that smooth hammerhead sharks extensively rely on highly fished habitats for two years after parturition supports the relevance of establishing a size limit in coastal fisheries and demonstrates how the current temporal shark fishing closure could lack efficiency for the species.

Dataset from Maith, O., Baladron, J., Einhäuser, W., & Hamker, F. H. (2023). Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model. Submitted to iScience.

This dataset contains all analyzed data from the study "Maith, O., Baladron, J., Einhäuser, W., & Hamker, F. H. (2023). Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model. Submitted to iScience.". It includes the behavioral data of 20 human participants (folder "psychExp") and of simulations of a neuro-computational basal ganglia model (folder "simulations") of the study. To replicate the results of the study, the dataset can be analyzed using the code provided separately under the following identifier: https://doi.org/10.5281/zenodo.6555886. The dataset is organized in the directory structure required for this purpose. For the human participants, only preprocessed eye-tracking and general behavioral data (.mat files) and the final analyzed behavioral data (output files) generated with the script "get_vps_outputs.m" (folder psychExp/..../3_srcAna/) are available. For more information about preprocessing steps as well as raw data of the eye-tracking experiment, please contact us by email (click <a href="https://www.tu-chemnitz.de/urz/mail/adrx.html?1-d29sZmdhbmcuZWluaGFldXNlci10cmV5ZXJAcGh5c2lrLg==">here</a>).

Data and codes of "Odor descriptive ratings can predict some odor-color associations in different color features of hue or lightness"

This project included data and codes used in our original article titled "Odor descriptive ratings can predict some odor-color associations in different color features of hue or lightness", written by Kaori Tamura and Tsuyoshi Okamoto. Please see https://gitlab.com/tamurak415/olfqr

opencc-zeroMar 2023View details →

Data for: Advances and critical assessment of machine learning techniques for prediction of docking scores

Semi-flexible docking was performed using AutoDock Vina 1.2.2 software on the SARS-CoV-2 main protease Mpro (PDB ID: 6WQF). Two data sets are provided in the xyz format containing the AutoDock Vina docking scores. These files were used as input and/or reference in the machine learning models using TensorFlow, XGBoost, and SchNetPack to study their docking scores prediction capability. The first data set originally contained 60,411 in-vivo labeled compounds selected for the training of ML models. The second data set,denoted as in-vitro-only, originally contained 175,696 compounds active or assumed to be active at 10 μM or less in a direct binding assay. These sets were downloaded on the 10th of December 2021 from the ZINC15 database. Four compounds in the in-vivo set and 12 in the in-vitro-only set were left out of consideration due to presence of Si atoms. Compounds with no charges assigned in mol2 files were excluded as well (523 compounds in the in-vivo and 1,666 in the in-vitro-only set). Gasteiger charges were reassigned to the remaining compounds using OpenBabel. In addition, four in-vitro-only compounds with docking scores greater than 1 kcal/mol have been rejected. The provided in-vivo and the in-vitro-only sets contain 59,884 (in-vivo.xyz) and 174,014 (in-vitro-only.xyz) compounds, respectively. Compounds in both sets contain the following elements: H, C, N, O, F, P, S, Cl, Br, and I. The in-vivo compound set was used as the primary data set for the training of the ML models in the referencing study. The file in-vivo-splits-data.csv contains the exact composition of all (random) 80-5-15 train-validation-test splits used in the study, labeled I, II, III, IV, and V. Eight additional random subsets in each of the in-vivo 80-5-15 splits were created to monitor the training process convergence. These subsets were constructed in such a manner, that each subset contains all compounds from the previous subset (starting with the 10-5-15 subset) and was enlarged by one eighth of the entire (80-5-15) train set of a given split. These subsets are further referred to as in_vivo_10_(I, II, ..., V), in_vivo_20_(I, II, ..., V),..., in_vivo_80_(I, II, ... V).

opencc-zeroMar 2023View details →

Data for: Nest material preferences in wild hazel dormice Muscardinus avellanarius: Testing predictions from optimal foraging theory

Obtaining nesting material presents an optimal foraging problem, collection of materials incurs a cost in terms of risk of predation and energy spent, and individuals must balance these costs with the benefits of using that material in the nest. The hazel dormouse, Muscardinus avellanarius is an endangered British mammal in which both sexes build nests. However, whether material used in their construction follows the predictions of optimal foraging theory is unknown. Here, we analyse the use of nesting materials in forty two breeding nests from six locations in Southwest England. Nests were characterised in terms of which plants were used, the relative amount of each plant, and how far away the nearest source was. We find that dormice exhibit a preference for plants closer to the nest, but that the distance they are prepared to travel depends on the plant species. Dormice travelled further to collect honeysuckle Lonicera periclymenum, oak Quercus robur, and beech Fagus sylvatica than any other plants. Distance did not affect the relative amount used, although the proportion of honeysuckle in nests was highest, and more effort was expended collecting honeysuckle, beech, bramble Rubus fruticosus and oak compared to other plants. Our results suggest that not all aspects of optimal foraging theory apply to nest material collection. However, optimal foraging theory is a useful model to examine nest material collection, providing testable predictions. As found previously honeysuckle is important as a nesting material, and should be taken account when assessing suitability of sites for dormice.

SPIRIT Checklist & Model Consent for 'Predicting Acute and Post-Recovery Outcomes in Cerebral Malaria and Other Comas by Optical Coherence Tomography (OCT in CM) – A protocol for an observational cohort study of Malawian children'

This dataset contains the SPIRIT checklist (adapted to a observational trial) and model consent forms for the OCT in CM study protocol. The protocol will be submitted as a paper to Wellcome Open Research.

Genomically predicted theoretical protein mass database for mass spectrometry (GPMsDB) evaluation datasets

These are datasets obtained for the evaluation of GPMsDB (genomically predicted protein mass database) and its toolkits (GPMsDB-tk/GPMsDB-dbtk). The following datasets are deposited. <ul> <li>The genome sequences of the strains newly sequenced and added using GPMsDB-dbtk (genomes_added.zip)</li> <li>MALDI-TOF-MS peak lists obtained from reference bacterial and archaeal strains (MALDI_peaklists.zip)</li> <li>16S rRNA gene sequences of the faecal isolates (mice_isolates_16S_nanopore.zip)</li> <li>Metagenome-assembled genomes from mouse faeces (mice_MAGs.zip)</li> </ul>

opencc-by-4.0Jan 2023View details →

Defectors: A Large Scale Python Dataset for Defect Prediction

Defect prediction has been a major research problem in the software engineering domain for the last five decades. In recent years, large deep-learning models have shifted the performance of software engineering tasks to new limits and are gaining usage in defect prediction. However, these defect prediction models are often limited by the quality of their datasets, which are not large or diverse enough. In this paper, we present Defectors, a large dataset for both line-level and just-in-time defect prediction. Defectors consist of $\approx$ 213K source code files ($\approx$ 93K defective and $\approx$ 120K defect-free files) from 25 popular python projects from various domains and organizations. These projects come from a diverse set of domains including machine learning, automation, and internet-of-things. Such a scale and diversity make Defectors a suitable dataset for deep learning models, especially transformer models that require large and diverse datasets to effectively generalize defect-inducing patterns to predict future defects. <table> <caption>Dataset Description</caption> <tbody> <tr> <td>File Name</td> <td>Description</td> </tr> <tr> <td>defectors.zip</td> <td>The original Dataset. Find its description in Section II of the paper.</td> </tr> <tr> <td>bug_inducing_commits.zip</td> <td>Each yaml file contains a map of bugfix commits to bug-inducing commits.</td> </tr> <tr> <td>filtered_bug_inducing_commits.yaml</td> <td>A map in structure {repo_name: {bug_inducing_commits: [list of python files in the commit]}}. This file only contains the bug-inducing commits that match the filtering criteria from Section III.D.</td> </tr> <tr> <td>repo_links.yaml</td> <td>Links to the repositories we used to construct the dataset.</td> </tr> </tbody> </table>

opencc-zeroMar 2023View details →

Plant water use strategies predict restoration success across degraded drylands

<ul> <li>Plant strategies for coping with water limitation are likely to mediate restoration outcomes in degraded dryland ecosystems. Tradeoffs in traits related to water acquisition and use can intensify in more arid environments, making their effects on dryland restoration success even more salient. However, isolating the effects of drought responses from those of other environmental factors, as well as identifying the specific drought resistance traits that influence restoration success, can be difficult.</li> <li>In the present study, we couple a controlled dry-down experiment with a cross-site restoration field trial of out-planted seedlings (RestoreNet) using a suite of dryland herbaceous plant species from the same seed sources. We quantified interspecific variation in physiological responses to drought, specifically reductions in stomatal conductance (gs) and stem water potential (SWP), by comparing well-watered control plants to those experiencing decreasing soil moisture.</li> <li>Drought responses of SWP and gs varied independently among species, but both were related to survival in the cross-site restoration field trial when effect sizes were aggregated across all sites. Responses were consistent with acquisitive water use strategies resulting in greater success, where species with greater declines in SWP or weaker declines in gs under drought had greater survival. The correlation between SWP drought response and survival also intensified in sites with lower accumulated precipitation following restoration.</li> <li>Differences among functional groups revealed two different paths to restoration success: forbs that maintain high gs and narrow safety margins to maximize exploitation of moisture pulses before going into drought dormancy, or C4 grasses that maintain efficient water uptake in drying soils while risking cavitation. C3 grass species varied between these two strategies.</li> <li> Synthesis and applications: Taken together, the results of this study and others conducted at RestoreNet sites indicate that while a diversity of physiological responses to drought may exist in dryland plant communities, successfully restoring herbaceous species through out-planting in degraded conditions is likely to be achieved with species that maximize water uptake via one of two strategies, with tolerance of low SWP being particularly important in the most arid settings.</li> </ul>

Protein biomarkers for early predictions of hypoxic ischemic encephalopathy

Perinatal asphyxia is a temporary interruption of oxygen availability that results in neonatal morbidity and mortality. The exact burden of perinatal asphyxia is unknown due to the lack of valid and accepted diagnostic criteria applicable to resource-limited settings. After sepsis, it is the second most important cause of neonatal death. Though there are tests available that can indicate whether an infant is suffering from birth asphyxia or not, they are not the gold standard. There is a dire need for early detection biomarkers for birth asphyxia so that the outcome of birth asphyxia can be avoided. Urinary and serum proteomics has rapidly developed and standard collection and experiment protocols are now available. With the help of the ‘omics’ approach, we can speed up biomarker discovery and widely use it to facilitate diagnostic and therapeutic developments for many diseases. Some of the promising biomarkers found in urine and serum are APOC3, ORM1, SOD1, FABP1, and AZGP1.

Machine Learning Techniques Application for Installation Torque Prediction of Helical Piles

This database contains torque observations in the installation of helical piles used as a foundation in an infrastructure construction for power transmission towers. In this way, this database trains ML models for torque prediction.

DATA: Language experience predicts music processing in a half-million speakers of fifty-four languages

This repository contains the data for the paper 'Language experience predicts music processing in a half-million speakers of fifty-four languages' The code that accompanies this data can be found on GitHub - https://github.com/themusiclab/language-experience-music

Expert-annotated dataset for band gap prediction

Here we present the dataset for "Toward Accurate Interpretable Predictions of Materials Properties within Transformer Language Models" (<a href="https://doi.org/10.48550/arXiv.2303.12188">arXiv:2303.12188</a>). The dataset_annotated.json file is organized as follows: <pre><code class="language-python">{ "JARVIS-DFT id": { "text": "text description of material generated within the Robocrystallographer library", "tokens": "sequence of tokens generated by the MatBERT tokenizer", "rationales": "rationales proposed by a domain expert", "label": "label predicted by the MatBERT model", }, ...: ..., }</code></pre>

openmit-licenseFeb 2023View details →

Run of digital pathology tissue/tumor prediction workflow

This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a> representation of an execution of the tissue/tumor prediction workflow for digital pathology from <a href="https://github.com/crs4/deephealth-pipelines/tree/c54840df08742e3aa454394e0e74d15fbd640f07">crs4/deephealth-pipelines</a>. It follows the <a href="https://w3id.org/ro/wfrun/provenance/0.1">Provenance Run Crate</a> profile. The workflow has been run with <a href="https://github.com/common-workflow-language/cwltool/tree/3.1.20230213100550">cwltool</a>, using the --provenance option to generate a <a href="https://doi.org/10.1093/gigascience/giz095">CWLProv</a> RO bundle, and then converted to an RO-Crate using <a href="https://github.com/ResearchObject/runcrate/tree/755fb7f0a8ba6fc238a2cb7a3218175644eb78b5">runcrate</a>. The input dataset is <a href="https://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/Mirax2-Fluorescence-2.zip">Mirax2-Fluorescence-2</a> by Yves Sucaet, from the <a href="https://openslide.cs.cmu.edu/download/openslide-testdata/Mirax/">MIRAX test data</a>.