Find research datasets worth reusing

Large-scale Comparison of Fe and Ru Polyolefin C–H Activation Catalysts

We performed a large-scale density functional theory comparison of polyolefin C–H hydroxylation trends across over 200 Fe and Ru catalysts that are identical except for their metal centers for the radical-rebound conversion of propane to propanol. This ZIP contains optimized structures of all reactive intermediates of the Fe and Ru catalysts in the final dataset; approximate transition state structures from the calculated reaction coordinates; raw electronic energies and reaction energies for all Fe and Ru catalysts in the final dataset; electronic properties of all intermediates of the Fe and Ru catalysts in the final dataset; Fe and Ru catalysts that were discarded during DFT (ZIP)

opencc-zeroNov 2023View details →

Data from: Fine-scale genetic structure in the orchid Gymnadenia conopsea is not associated with local density of flowering plants

Premise: Density-dependent pollinator visitation can lead to density-dependent mating patterns and within-population genetic structure. In Gymnadenia conopsea, individuals in low-density patches receive more self-pollen than individuals in high-density patches, suggesting higher relatedness at low density. Ongoing fragmentation is also expected to cause more local matings, potentially leading to biparental inbreeding depression. Methods: To evaluate whether relatedness decreases with local density, we analysed 1315 SNP loci in 113 individuals within two large populations. We quantified within-population genetic structure in one of the populations, recorded potential habitat barriers, and visualized gene flow using estimated effective migration surfaces (EEMS). We further estimated the magnitude of biparental inbreeding depression that would result from matings restricted to within 5 m. Results: There was no significant relationship between local density and relatedness in any population. We detected significant fine-scale genetic structure consistent with isolation-by-distance, with positive kinship coefficients at distances below 10 m. Kinship coefficients were low, and predicted biparental inbreeding depression resulting from matings within the closest 5 m was a modest 1–3%. EEMS suggested that rocks and bushes may act as barriers to gene flow within a population. Conclusions: The results suggest that increased self-pollen deposition in sparse patches does not necessarily cause higher selfing rates, or that inbreeding depression results in low establishment success of inbred individuals. The modest relatedness suggests that biparental inbreeding depression is unlikely to be an immediate problem following fragmentation of large populations. The results further indicate that habitat structure may contribute to governing fine-scale genetic structure in G. conopsea.

opencc-by-4.0Jan 2022View details →

TSSB-3M: A massive scale dataset of single statement bugs

Datasets created for the paper "TSSB-3M: Mining single statement bugs at massive scale".Access to single statement bug fixes at massive scale is not only important for exploring how developers introduce bugs in code and fix them but it is also a valuable ressource for research in data-driven bug detection and automatic repair. Therefore, we are releasing multiple large-scale collections of single statement bug fixes mined from over 500K public Python repositories.To facilitate future research, we are releasing three datasets:TSSB-3M: A dataset of over 3 million isolated single statement bug fixes. Each bug fix is related to a commit in a public Python that does not change more than a single statement.SSB-9M: A dataset of over 9 million single statement bug fixes. Each fix modifies at least a single statement to fix a bug. However, the related code changes might incorporate changes to other files.SSC-28M: A dataset of over 28 million general single statement changes. We are releasing this dataset with the intention to faciliate research in software evolution. Therefore, a code change might not necessarily relate to a bug fix.In addition, we are releasing two cleaned versions by applying a more aggressive deduplication scheme:CTSSB-1M: A cleaned version of TSSB-3M containing nearly 1 million isolated single statement bug fixes. Each bug fix is related to a commit in a public Python that does not change more than a single statement.CSSB-2.6M: A cleaned version of SSB-9M containing over 2.6 million single statement bug fixes. Each fix modifies at least a single statement to fix a bug. However, the related code changes might incorporate changes to other files.Because of concerns regarding the licensing of code, we do not release the original source code related to the single statement code changes. However, our datasets provide enough information to load the original code from the source project. All dataset entries are saved in a compressed jsonlines format. Each individual entry provides access to the following information:Commit details:<ul><li>project: Name of the git project where the commit occurred.</li><li>project_url: URL of project containing the commit</li><li>commit_sha: commit SHA of the code change</li><li>parent_sha: commit SHA of the parent commit</li><li>file_path: File path of the changed source file</li><li>diff: Universal diff describing the change made during the commit</li><li>before: Python statement before commit</li><li>after: Python statement after commit (addresses the same line)</li></ul>Commit analysis:<ul><li>likely_bug: true if the commit message indicates that the commit is a bug fix. This is heuristically determined.</li><li>comodified: true if the commit modifies more than one statement in a single file (formatting and comments are ignored).</li><li>in_function: true if the changed statement appears inside a Python function</li><li>sstub_pattern: the name of the single statement change pattern the commit can be classified for (if any). Default: SINGLE_STMT</li><li>edit_script: A sequence of AST operation to transform the code before the commit to the code after the commit (includes Insert, Update, Move and Delete operations).</li></ul>

opencc-by-4.0Nov 2022View details →

Pairing Remote Sensing and Clustering in Landscape Hydrology for Large-Scale Changes Identification. Applications to the Subarctic Watershed of the George River (Nunavik, Canada). Dataset and Code.

For remote and vast northern watersheds, hydrological data are often sparse and incomplete. Landscape hydrology provides useful approaches for the indirect assessment of the hydrological characteristics of watersheds through analysis of landscape properties. In this study, we used unsupervised Geographic Object-Based Image Analysis (GeOBIA) paired with the Fuzzy C-Means (FCM) clustering algorithm to produce seven high-resolution territorial classifications of key remotely sensed hydro-geomorphic metrics for the 1985-2019 time-period, each spanning five years. Our study site is the George River watershed (GRW), a 42,000 km2 watershed located in Nunavik, northern Quebec (Canada). The subwatersheds within the GRW, used as the objects of the GeOBIA, were classified as a function of their hydrological similarities. Classification results for the period 2015-2019 showed that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient, which indicates broad-scale differences in hydrological regimes and water balances across the GRW. Six classifications were computed for the period 1985-2014 to investigate past changes in hydrological regime. The seven-classification time series showed a homogenization of subwatershed types associated to increases in vegetation productivity and in water content in soil and vegetation, mostly concentrated in the northern half of the GRW, which were the major changes occurring in the land cover metrics of the GRW. An increase in vegetation productivity likely contributed to an augmentation in evapotranspiration and may be a primary driver of fundamental shifts in the GRW water balance, potentially explaining a measured decline of about 1 % (&sim; 0.16 km3y−1) in the George River’s discharge since the mid-1970s. Permafrost degradation over the study period also likely affected the hydrological regime and water balance of the GRW. However, the shifts in permafrost extent and active layer thickness remain difficult to detect using remote sensing based approaches, particularly in areas of discontinuous and sporadic permafrost.

Namelist files and settings for multi-year km-scale nextGEMS Cycle 3 simulations with IFS-FESOM/NEMO

This dataset contains the used atmosphere, land, wave, and ocean model namelist files and settings for the multi-year km-scale nextGEMS Cycle 3 simulations with IFS-FESOM (TCo2559 atmosphere at 4.4km resolution + 5km ocean; TCo1279 atmosphere at 9km resolution + 5km ocean) and IFS-NEMO (TCo1279 atmosphere at 9km resolution, 0.25 degree ocean) as described in the studyRackow, Pedruzo-Bagazgoitia, Becker, Milinski, Sandu et al. : Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5/NEMOV3.4This work was supported by the European Union's Horizon 2020 collaborative project NextGEMS (grant number 101003470). For more infos about the nextGEMS project, see https://nextgems-h2020.eu (last access: 29.11.2023)

Influences of Satellite Sensor and Scale on Derivation of Ecosystem Functional Types and Diversity

These are the datasets where were generated for paper "Influences of Satellite Sensor and Scale on Derivation of  Ecosystem Functional Types and Diversity"

Appendix A - The Implications of Handwritten Text Recognition for Accessing the Past at Scale

List of works identified through a Grounded Theory Method (GTM) of the current and near future implications of Handwritten Text Recognition (HTR) on the historical method and wider information environment. The findings of this data collection are provided in 'The Implications of Handwritten Text Recognition for Accessing the Past at Scale'

SI Movies for "Dynamics of dominance: maneuvers, contests, and assessment in the posture-scale movements of interacting zebrafish"

This is a repository of the Supplementary Movies from the paper "Dynamics of dominance: maneuvers, contests, and assessment in the posture-scale movements of interacting zebrafish".<h3>SM1</h3> This movie is a visualization of the tracking pipeline output, namely the 3D bodypoint positions of the two zebrafish over time. In this animation, we show the 3D locations of the head (circle), pec (square) and tail (cross) bodypoints of the winner (red) and loser (blue) throughout an entire experiment, the same experiment shown in Fig3(A) of the paper. The movie is downsampled from the 100fps recordings to 25fps to reduce file size. (1hr 23min 29sec)<h3>Notes on SM2-6</h3>In these movies we show two panels. The left panel shows an animation of the contestant 3D bodypoint positions. The right panel shows the XZ camera view of the tank with the XZ image coordinates of bodypoints drawn over the fish. The movies run at 100fps and are all 2 minutes long. The movies are each centered at a different time during the FishTank20200130_153857 recording, with one minute before, and one minute after, the chosen centered point.<h3>SM2</h3>This video is centred at the frame identified as the start of the first fight bout.<h3>SM3</h3>This video is centered at the frame identified as the middle of the first fight bout.<h3>SM4</h3>This video is centered at the frame identified as the end of the first fight bout.<h3>SM5</h3>This video is centered at the frame identified as the start of the second fight bout.<h3>SM6</h3>This video is centered at the frame identified as the end of the second fight bout.

opencc-by-4.0Jul 2023View details →

INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells

Open the record for dataset details and reuse information.

SDSS DR72 Large Scale Structure random catalog

This is a catalog of random points in the footprint of the Sloan Digitial Sky Survey (specifically the NYU-VAGC) used for defining the angular mask of the survey. This dataset was originally created/hosted by Michael Blanton at NYU as part of the Sloan Digital Sky Survey.

opencc-by-sa-4.0Dec 2023View details →

Monitoring of large-scale archaeological excavations using photogrammetric techniques - Nea Paphos case study

Photogrammetric data (images) for five contexts from a single archaeological trench from the Paphos Agora Project (Excavations conducted in Nea Paphos Archaeological Site by Jagiellonian Unversity in Krakow - under the direction of Prof. Ewdoksia Papuci-Władyka). Images for photogrammetric modelling of all contexts were taken in 2015 with the NIKON D7100 camera (24 Mpx resolution), with the number of images per model varying from 25 to 41. For photogrammetric processing, the Agisoft PhotoScan and Reality Capture were used. The data (images) were uploaded together with project files from Agisoft Metashape and RealityCapture, exported results (3D models, orthoimages and DEMs) and reference data - point clouds from terrestrial laser scanner measurements are available as supplementary material for the article in Journal of Archaeological Science: Reports. Also, data for one layer below and above, which do not include reference point clouds, have been added.

Data from: Latest Ordovician (Hirnantian) brachiopod faunal lists used for non-matric multidimensional scaling (NMDS) and network analyses

A total of 107 brachiopod genera of Hirnantian age among 42 localities worldwide are compiled into a binary dataset (Table S1; presence =1, absence = 0). The majority of the faunal lists was derived from the well-screened Hirnantian brachiopod faunal data of Rong et al. (2020). In this study, the Hirnantian faunal lists are updated for the following localities: Anticosti Island, eastern Canada; Edgewood region, American Mid-Continent; Mackenzie Mountains, northwestern Canada. Detailed discussions on these faunal update and references are provided in the main paper (section on Paleobiogeography of the Mackenzie Mountains Hirnantian fauna).

Large-scale facilitative effects of Vachellia caven

The importance of nurse plants structuring plant communities is well appreciated at local scales, yet the effect of a single nurse on large scales has been neglected in analyses. So far, studies only use environmental gradients within one type of ecosystem and tend to generalize the nurse effects. To assess how the effect of a single nurse species is modulated by different environmental settings, interactions between the shrub Vachellia caven and the surrounding plant communities were evaluated at 481 paired plots (outside vs underneath the plant crown), in 39 sites across two distribution ranges, the Mediterranean west and the mostly subtropical east of the Andes mountains (covering ca. 2x10 6 km2). Cover, abundance, and richness of perennial plants underneath and outside V. caven were used as response variables to estimate an index indicative of plant interactions (RII) and tested how this was affected by the rainfall gradient and distribution range. Overall, RII responses to rainfall gradients had low conditional R2 (~0.25) at this large-scale of analysis, but were significantly different between ranges: the RII followed a quadratic trend across the rainfall gradient in the western range, while this relationship was positive and close to linear at the eastern range. Then, by projecting the RII models (i.e., for abundance, cover, and richness) spatially through a consensus map, we show that most positive effects of V. caven are geographically found in dissimilar areas: the central part of Chile (western range) and across the Paraná river (eastern range). When local fine-scale predictors (i.e., annual herbs cover and height, and herbivores feces cover), were used to model each response variable at the plot level (underneath or outside V. caven) similar trends were kept as when considering only large-scale predictors. Synthesis: Here, we show that the effect of the same nurse species on neighbouring plant communities can be very different depending on ranges of distribution, stressing that its ecological function cannot be generalized and not only depends on local factors but also is large-scale context-dependent.

opencc-by-nc-4.0Dec 2022View details →

CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection

In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by the Artificial Intelligence Group at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS). The dataset aims to address the challenges encountered in landslide recognition. With the increase in landslide occurrences due to climate change and earthquakes, there is a growing need for a precise and comprehensive dataset to support fast and efficient landslide recognition. In contrast to existing datasets with dataset size, coverage, sensor type and resolution limitations, the CAS Landslide Dataset comprises 20,865 images, integrating satellite and unmanned aerial vehicle data from nine regions. To ensure reliability and applicability, we establish a robust methodology to evaluate the dataset quality. We propose the use of the Landslide Dataset as a benchmark for the construction of landslide identification models and to facilitate the development of deep learning techniques. Researchers can leverage this dataset to obtain enhanced prediction, monitoring, and analysis capabilities, thereby advancing automated landslide detection. If you use our data, please cite our work published in Scientific Data. Xu, Y., Ouyang, C., Xu, Q. et al. CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection. Sci Data 11, 12 (2024). https://doi.org/10.1038/s41597-023-02847-z

Unraveling the cavity-nesting network at large spatial scales: The biogeographic role of woodpeckers as ecosystem engineers

Aim: Cavities are usually a limiting resource for several forest-dwelling vertebrates, with effects that propagate through ecological networks. Although diverse assemblages of primary excavators (e.g., woodpeckers) are assumed to increase cavities, other forest resources can also limit populations of primary excavators and cavity users, thus undermining the ecological role of excavators over different scales. We aim to test the biogeographical-scale relationships between primary excavators and cavity users by distinguishing the contribution of forest characteristics. Location: Southern South America Methods: We used species distribution models, which combine bioclimatic and remote sensing derived variables, to map the richness of vertebrates composing the cavity network of temperate and Mediterranean forests of South America. Based on a resampling procedure for ensuring spatial independence, we fitted structural equation models to estimate causal relationships between forest characteristics and cavity-user vertebrates. Results: Secondary cavity users (obligated, habitat generalists and forest specialists) were positively and strongly influenced by the richness of primary excavators, while mammal richness was more influenced by tree richness. The richness of trees and Nothofagus tree species influenced positively the richness of primary excavators and secondary cavity users. Canopy height and net primary productivity affected positively secondary cavity users. Main conclusions: Our results confirm the role of primary excavators as ecosystem engineers but highlight the importance of considering large spatial scales when analyzing cavity-nesting networks. Biogeographical patterns of tree diversity and forest structure can be important drivers of cavity-nesting networks that remain hidden when studies are conducted over fine spatial scales.

ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries – Data and Models

This repository contains data and models used in the following paper.   Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V., & Zou, J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. In review.   The data and models are meant to be used with the <a href="https://github.com/swansonk14/admet_ai">ADMET-AI</a> code, which runs the ADMET-AI web server at <a href="https://admet.ai.greenstonebio.com/">admet.ai.greenstonebio.com</a>.   The data.zip file has the following structure. data   drugbank: Contains files with drugs from the <a href="https://go.drugbank.com/">DrugBank</a> that have received regulatory approval. drugbank_approved.csv contains the full set of approved drugs along with ADMET-AI predictions, while the other files contain subsets of these molecules used for testing the speed of ADMET prediction tools.   tdc_admet_all: Contains the data (.csv files) and RDKit features (.npz files) for all 41 single-task ADMET datasets from the <a href="https://tdcommons.ai/">Therapeutics Data Commons</a> (TDC).   tdc_admet_multitask: Contains the data (.csv files) and RDKit features (.npz files) for the two multi-task datasets (one regression and one classification) constructed by combining the tdc_admet_all datasets.   tdc_admet_all.csv: A CSV file containing all 41 ADMET datasets from tdc_admet_all. This can be used to easily look up all ADMET properties for a given molecule in the TDC.   tdc_admet_group: Contains the data (.csv files) and RDKit features (.npz files) for the 22 TDC ADMET Benchmark Group datasets with five splits per dataset.   tdc_admet_group_raw: Contains the raw data (.csv files) used to construct the five splits per dataset in tdc_admet_group.   The models.zip file has the following structure. Note that the ADMET-AI website and Python package use the multi-task Chemprop-RDKit models below. models   tdc_admet_all: Contains Chemprop and Chemprop-RDKit models trained on all 41 single-task TDC ADMET datasets.   tdc_admet_all_multitask: Contains Chemprop and Chemprop-RDKit models trained on the two multi-task TDC ADMET datasets (one regression and one classification).   tdc_admet_group: Contains Chemprop and Chemprop-RDKit models trained on the 22 TDC ADMET Benchmark Group datasets.

CoUDlabs_WP8_T831_AaU_001 Application of Large-Scale Particle Image Velocimetry (LSPIV) technique in Aalborg retention pond

This dataset includes the data obtained during the installation of a camera system to determine the surface velocities in a retention pond monitored by Aalborg University (AAU). The dataset consists of raw and processed images and surface velocity maps obtained during preliminary experiments when the retention tank was being filled and emptied. The objective is to assess the feasibility and usefulness of this type of measurement for the calibration of CFD models to optimize the operation of the installation. The data presented may help to improve and assess imaging techniques in real conditions in urban environments.The dataset is a result from the Joint Research Activity 3 (WP8, Improving Resilience and Sustainability in Urban Drainage solutions), Task 8.3.1. (Hydrodynamic design for stormwater detention ponds optimized for cost-efficient maintenance) within Co-UDlabs project, funded under the European Union's Horizon 2020 research and innovation program under grant agreement No 101008626.

Large-Scale Traveling Ionospheric Disturbances over the European sector during the geomagnetic storm on March 23-24, 2023: energy deposition in the source regions and the propagation characteristics

IMAGE 2D Ionospheric Equivalent Currents for 23 and 24 March 2023 (https://space.fmi.fi/image/).  We thank the institutes who maintain the IMAGE Magnetometer Array (<a href="https://space.fmi.fi/image/">https://space.fmi.fi/image/</a>): Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway), Finnish Meteorological Institute (Finland), Institute of Geophysics Polish Academy of Sciences (Poland), GFZ German Research Centre for Geosciences (Germany), Geological Survey of Sweden (Sweden), Swedish Institute of Space Physics (Sweden), Sodankylä Geophysical Observatory of the University of Oulu (Finland), DTU Technical University of Denmark (Denmark), and Science Institute of the University of Iceland (Iceland). The provisioning of data from AAL, GOT, HAS, NRA, VXJ, FKP, ROE, BFE, BOR, HOV, SCO, KUL, and NAQ is supported by the ESA contracts number 4000128139/19/D/CT as well as 4000138064/22/D/KS. The authors would like to thank Dr. Liisa Juusola for providing the IMAGE 2D Ionospheric Equivalent Currents data.

Contrasting responses of fine root biomass and traits to large-scale nitrogen and phosphorus addition in tropical forests in the Guiana shield

Fine roots mediate plant nutrient acquisition and growth. Depending on soil nutrient availability, plants can regulate fine root biomass and morphological traits to optimise nutrient acquisition. Little is known, however, about the importance of these parameters influencing forest functioning. In this study, we measured root responses to nutrient additions to gain a mechanistic understanding of plant adaptations to nutrient limitation in two tropical forests in French Guiana, differing two-fold in their soil nutrient statuses. We analysed the responses of root biomass, mean root diameter (RD), specific root length (SRL), specific root area (SRA), root tissue density (RTD) and carbon (C), nitrogen (N) and phosphorus (P) concentrations in roots down to 15 cm soil depth after three years of N and P additions. At the lower-fertility site Paracou, no changes in root biomass or morphological traits were detected with either N or P addition, although P concentrations in roots increased with P addition. In the higher fertility site, Nouragues, root biomass and P concentrations in roots increased with P addition, with no changes in morphological traits. In contrast, N addition shifted root traits from acquisitive to more conservative by increasing RTD. A significant interaction between N and P in Nouragues pointed to stronger responses to P addition in the absence of N. Our results suggest that the magnitude and direction of root biomass and trait expression were regulated by soil fertility, corroborated by the response to N or P additions. At low fertility sites, we found lower plasticity in root trait expression compared to more fertile conditions, where N and P additions caused stronger and antagonistic responses. Identifying the exact role of mechanisms affecting root nutrient uptake in Amazon forests growing in different soils will be crucial to foresee if and how rapid global changes can affect their carbon allocation.

Non-redundant metagenome-assembled genomes of activated sludge reactors at different disturbances and scales

Metagenome-assembled genomes (MAGs) are microbial genomes reconstructed from metagenomic data and can be assigned to known taxa or lead to uncovering novel ones. MAGs can provide insights into how microbes interact with the environment. Here, we performed genome-resolved metagenomics on sequencing data from four studies using sequencing batch reactors at microcosm (~25 mL) and mesocosm (~4 L) scales inoculated with sludge from full-scale wastewater treatment plants. These studies investigated how microbial communities in such plants respond to two environmental disturbances: the presence of toxic 3-chloroaniline and changes in organic loading rate. We report 839 non-redundant MAGs with at least 50% completeness and 10% contamination (MIMAG medium-quality criteria). From these, 399 are of putative high-quality, while sixty-seven meet the MIMAG high-quality criteria. MAGs in this catalogue represent the microbial communities in sixty-eight laboratory-scale reactors used for the disturbance experiments, and in the full-scale wastewater treatment plant which provided the source sludge. This dataset can aid meta-studies aimed at understanding the responses of microbial communities to disturbances, particularly as ecosystems confront rapid environmental changes.