Find research datasets worth reusing

opencc-by-sa-4.0Oct 2017View details →

Data from sonar beam pattern measurements and modeling of free-flying Rousettus aegyptiacus

Concurrent broadband microphone and high-speed video camera recordings of free-flying Egyptian fruit bat (Rousettus aegyptiacus). The experiment was designed to record and reconstruct broadband, two-dimensional (azimuth and elevation in a bat-centered coordinate system) beam pattern from this interesting lingual-echolocating bat species. These bats produce alternating left- and right-pointing clicks as pairs during navigation and target localization, with very short inter-click intervals (~20 msec) within each pair.

openother-openSep 2017View details →

Nahal HaArava Data and Models V1.0.1

Data and models for Nahal HaArava project

opencc-by-nc-4.0Oct 2017View details →

Data from: Inferring causalities in landscape genetics: An extension of Wright's causal modeling to distance matrices

Data files from Inferring causalities in landscape genetics: An extension of Wright's causal modeling to distance matrices.

opencc-by-4.0Nov 2017View details →

Data for "Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison"

All data recorded for reflectance and e-folding depth measurements associated with "Optical properties of sea ice doped with black carbon – an experimental and radiative-transfer modelling comparison"

Incremental Model Transformations with Triple Graph Grammars for Multi-version Models and Multi-version Pattern Matching Evaluation Data

Java abstract syntax graphs for two software development projects in non-recreating multi-version model encoding.

Data for Streamflow Prediction: Comparison of SWAT vs. Random Forest Models in Diverse Catchments

This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time series prediction across diverse catchments, and compares its results against SWAT predictions. We found strong evidence of RF's better performance by adding historical flows and time-lags for meteorological values over using only actual meteorological values. On a daily scale, RF demonstrated robust performance (Nash–Sutcliffe efficiency [NSE] > 0.5), whereas SWAT generally yielded unsatisfactory results (NSE < 0.5) and tended to overestimate daily streamflow by up to 27% (PBIAS). However, SWAT provided better monthly predictions, particularly in catchments with irregular flow patterns. Although both models faced challenges in predicting peak flows in snow-influenced catchments, RF outperformed SWAT in an arid catchment. RF also exhibited a notable advantage over SWAT in terms of computational efficiency. Overall, RF is a good choice for daily predictions with limited data, whereas SWAT is preferable for monthly predictions and understanding hydrological processes in depth. This repository contains the input data used for building the RF and SWAT models and the files describing the modeling results. The corresponding Zenodo code repository is available at <a href="../doi/10.5281/zenodo.11064973" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.11064973</a>.

opencc-by-4.0Aug 2023View details →

Model data for " Topography Influence on Changes in Spatial Distribution of Eddy Kinetic Energy in the Southern Ocean"

This dataset is for the paper " Topography influence on Changes in Spatial Distribution of Eddy Kinetic Energy in the Southern Ocean"

Data publication: Simulation of macroscopic boundary value problems using phenomenological material model for steel fiber reinforced high performance concrete (HPC)

This data set contains all necessary inputs for the Simulation of macroscopic boundary value problems using phenomenological material model, including boundary conditions, material parameters and numerical results. The discretization is realized in terms of the finite element method.

MAED Data Note: Selected socio-economic and technical demand modelling data for Sierra Leone.

This MAED Data Note contains historic annual data on electricity demand (2018 to 2023) segregated by sector (industry, services, households). Additionally, historic social and economic data on population (total, growth, urban-rural split, household size), GDP (total, sectoral split, growth) and electrification are included within this data note. This historic data can be used to create base year energy intensitities used as a foundation for energy demand modelling using the Model for the Analysis of Energy Demand (MAED) simulation software. Two illustrative scenarios are developed alongside a baseline demand projection from 2018 to 2050.  This work was supported by the Climate Compatible Growth Programme (#CCG) of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies.

opencc-by-4.0Jan 2024View details →

Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor"

Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor". For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.

openukcrownMay 2024View details →

Model Data and Diagnostics used for the Lake Victoria Process Analysis

Model data and derived diagnostics used in the Lake Victoria analysis, from Unified Model output. © Crown Copyright, Met Office

opencc-by-nc-4.0May 2024View details →

Exploring the Search Space of Neural Network Combinations obtained with Efficient Model Stitching - Results Data

Open the record for dataset details and reuse information.

Quantitative evidence for modelling electric vehicles - Supplementary Data

Please cite as: Malte Jansen, Rob Gross, and Iain Staffell. ‘Quantitative Evidence for Modelling Electric Vehicles’. Renewable and Sustainable Energy Reviews 199 (1 July 2024): 114524. <a href="https://doi.org/10.1016/j.rser.2024.114524">https://doi.org/10.1016/j.rser.2024.114524</a>. Abstract: Electric vehicles are now a major contributor to decarbonising the transport sector. Their rollout has accelerated rapidly since 2020, reaching a global fleet of 40 million in 2023. This  presents both problems and opportunities for electricity systems, with charging increasing peak loads, but also providing a large new source of flexibility to help manage increased shares of wind and solar generation, shift peak demand and improve network management. While EV flexibility is widely discussed, there is uncertainty surrounding the magnitude to which EVs could help electricity systems, and a distinct lack of quantitative evidence around adoption, charging behaviour and technical capabilities for load shifting. This study employs the rapid evidence assessment method to synthesise recent information. We find that studies expect that EVs could provide 1–11 GW of flexible capacity per million vehicles (median: 3.7 GW), with the ability to shift demand by 1.5–5 hours (median: 4 hours) and a price elasticity of –0.77 to –0.10 (median: –0.15).  Diurnal profiles of charging demand and availability for providing flexibility are aggregated across multiple studies. The results are relevant for energy modellers and show that the interaction between EVs and electricity systems can be generalised on a widely-applicable basis.

Rewired connectome of an SSCX model with inhibitory targeting based on trends found in MICrONS data

This is a rewired connectome of the internal synaptic connectivity of the model deposited under <a href="../records/7930275" target="_blank" rel="noopener">https://zenodo.org/records/7930275</a>. In the original model, local connectivity is based on axo-dendritic overlaps combined with a pruning rule, which together are known to be able to recreate biological trends in excitatory subnetworks. By comparison with the <a href="https://www.microns-explorer.org/cortical-mm3" target="_blank" rel="noopener">MICrONS</a> dataset we found that the inhibitory trends characterized by <a href="https://www.biorxiv.org/content/10.1101/2023.01.23.525290v3" target="_blank" rel="noopener">Schneider-Mizell et al. (2023)</a> can be reproduced with some additional, relatively simple pruning rules (see accompanying <a href="https://www.biorxiv.org/content/10.1101/2022.08.11.503144" target="_blank" rel="noopener">anatomy publication</a>), and created a rewired version of the connectome in <a href="https://github.com/AllenInstitute/sonata" target="_blank" rel="noopener">SONATA</a> format (edges.h5) that reproduces these trends. We refer to this as the Schneider-Mizell compatible SM-connectome in the accompanying <a href="https://www.biorxiv.org/content/10.1101/2023.05.17.541168" target="_blank" rel="noopener">physiology publication</a>. Note that for technical reasons, all afferent_segment_... and efferent_... synapse properties (which are not required for running simulations) were set to zero in the rewired pathways. [Update 08/05/2024 - v3]: Conductances of individual synapses were recalibrated to preserve pathway-specific reference PSP amplitudes. The afferent_section_type and efferent_section_type synapse properties were previously off by 1 and were fixed. Missing synaptic input compensation was recalibrated to obtain target firing rates and added to this release. [Update 28/02/2024 - v2]: Source m-type L1_NGC-SA included

opencc-by-4.0Dec 2023View details →

Data for "A unified model-based framework for doublet or multiplet detection in single-cell multiomics data"

This repository contains all the data necessary for replicating, interpreting, and extending the COMPOSITE multiplet detection results featured in our manuscript, 'A Unified Model-Based Framework for Doublet or Multiplet Detection in Single-Cell Multiomics Data'. The data are ready to be directly used as input for the COMPOSITE cloud-based application or the Python package 'sccomposite' to replicate the results.

opencdla-permissive-1.0Dec 2023View details →

WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models

############# <h1>WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models</h1> ############# Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis Affiliation: EPFL Date: January, 2024 Link to the article: <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6">https://link.springer.com/article/10.1007/s11263-024-02026-6</a> -------------------------------- WildCLIP is a fine-tuned CLIP model that allows to retrieve camera-trap events with natural language from the Snapshot Serengeti dataset. This project intends to demonstrate how vision-language models may assist the annotation process of camera-trap datasets. Here we provide the processed Snapshot Serengeti data used to train and evaluate WildCLIP, along with two versions of WildCLIP (model weights). Details on how to run these models can be found in the project <a href="https://github.com/amathislab/wildclip">github repository</a>. <h2>Provided data (images and attribute annotations): </h2> The data consists of 380 x 380 image crops corresponding to the MegaDetector output of Snapshot Serengeti with a confidence threshold above 0.7. We considered only camera trap images containing single individuals. A description of the original data can be found on LILA <a href="https://lila.science/datasets/snapshot-serengeti">here</a>, released under the <a href="https://cdla.dev/permissive-1-0/" rel="nofollow">Community Data License Agreement (permissive variant)</a>. We warmly thank the authors of LILA for making the MegaDetector outputs publicly available, as well as for structuring the dataset and facilitating its access. <h2>Adapted CLIP model (model weights): </h2> WildCLIP models provided: <ul> <li>[New] WildCLIP_vitb16_t1.pth: CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Trained on both base and novel vocabulary (see paper for details).</li> <li>[New] WildCLIP_vitb16_t1_lwf.pth: CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1, and with the additional VR-LwF loss. Trained on both base and novel vocabulary (see paper for details).</li> <li>WildCLIP_vitb16_t1_base.pth: CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Model used for evaluation and trained on base vocabulary only. (previously named WildCLIP_vitb16_t1.pth)</li> <li>WildCLIP_vitb16_t1t7_lwf_base.pth: CLIP model with the ViT-B/16 visual backbone trained on data with captions following templates 1 to 7, and with the additional VR-LwF loss. Model used for evaluation and trained on base vocabulary only. (previously named WildCLIP_vitb16_t1t7_lwf.pth)</li> </ul> We also provide the CSV files containing the train / val / test splits. The train / test splits follow camera split from LILA (https://lila.science/datasets/snapshot-serengeti). The validation split is custom, and also at the camera level. <ul> <li>train_dataset_crops_single_animal_template_captions_T1T7_ID.csv: Train set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li>val_dataset_crops_single_animal_template_captions_T1T7_ID.csv: Validation set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li>test_dataset_crops_single_animal_template_captions_T1T8T10.csv: Test set with captions from templates 1, 8, 9 and 10 (columns "all captions")</li> </ul> Details on how the models were trained can be found in the associated <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6" target="_blank" rel="noopener">publication</a>. <h2>References: </h2> If you find our code, or weights, please cite: <pre>@article{gabeff2024wildclip, title={WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models}, author={Gabeff, Valentin and Ru{\ss}wurm, Marc and Tuia, Devis and Mathis, Alexander}, journal={International Journal of Computer Vision}, pages={1--17}, year={2024}, publisher={Springer} }</pre> If you use the adapted Snapshot Serengeti data please also cite their article: <pre>@article{swanson2015snapshot, title={Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna}, author={Swanson, Alexandra and Kosmala, Margaret and Lintott, Chris and Simpson, Robert and Smith, Arfon and Packer, Craig}, journal={Scientific data}, volume={2}, number={1}, pages={1--14}, year={2015}, publisher={Nature Publishing Group} }</pre>

Data used for analysis in "Calibrating tropical forest coexistence in ecosystem demography models using multi-objective optimization through population-based parallel surrogate search"

Open the record for dataset details and reuse information.

Simulation data used in "Modeling the Inception and Stepped Propagation of Positive Lightning Leaders"

This dataset includes all simulation data used in "Modeling the Inception and Stepped Propagation of Positive Lightning Leaders." These datasets are output from an upward leader model, described in the paper, under a variety of different conditions and settings. Included also are several charts and animations for select datasets.

Raw data and correlation analysis of physiological data sampled from Atlantic halibut (Hippoglossus hippoglossus) for the development of a PBPK model

Raw data sampled from Atlantic halibut (Hippoglossus hippoglossus) for the characterization of physiological parameters for the development of a species-specific physiology-based pharmacokinetic (PBPK) model. Additionally, the document containes imputed data for a PCA analysis and correlation coefficients and the related p-values from a correlation analysis.

New parameterization scheme for modeling ozone-caused damage to vegetation in process-based models: data

gmd-2024-6: Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses (Fang Li et al., 2024); https://gmd.copernicus.org/preprints/gmd-2024-6/. It includes three directory: input (O3 concentration), observations (collected PODY-An and PODY-gs), and simulations. It is co-supported by the National Natural Science Foundation of China (41875137) and Guangdong Major Project of Basic and Applied Basic Research (2021B0301030007).