Find research datasets worth reusing

Data from: Dynamic balancing of risks and rewards in a large herbivore: Further extending predator-prey concepts to road ecology

Animal behavior is shaped by the ability to identify risks and profitably balance the levels of risks encountered with the payoffs experienced. Anthropogenic disturbances like roads generate novel risks and opportunities that wildlife must accurately perceive and respond to. Basic concepts in predator-prey ecology are often used to understand responses of animals to roads (e.g., increased vigilance, selection for cover in their vicinity). However, prey often display complex behaviors such as modulating space use given varying risks and rewards, and it is unclear if such dynamic balancing is used by animals in the context of road crossings. We tested whether animals dynamically balance risks and rewards relative to roads using extensive field -based and GPS collar data from elk in Yoho National Park (British Columbia, Canada) where a major highway completely bisects their range during most of the year. We analyzed elk behavior by combining hidden Markov movement models with a step-selection function framework. Rewards were indexed by a dynamic map of available forage biomass and risks were indexed by road crossings and traffic volumes. We found that elk generally selected intermediate and high forage biomass and avoided crossing the road. Most of the time, elk modulated their behavior given varying risks and rewards. When crossing the highway compared with not crossing, elk selected for greater forage biomass and this selection was stronger as the number of highway crossings increased. However, with traffic volume, elk only balanced foraging rewards when they crossed a single time during a travel sequence. Using a road ecology system, we empirically tested an important component of predator-prey ecology – the ability to dynamically modulate behavior in response to varying levels of risks and rewards. Such a test articulates how decision-making processes that consider the spatiotemporal variation in risks and rewards allow animals to successfully and profitably navigate busy roads. Applying well-developed concepts in predator-prey theory helps understand how animals respond to anthropogenic disturbances and anticipate the adaptive capacity for individuals and populations to adjust to rapidly changing environments.

opencc-by-4.0Dec 2022View details →

Date set for the manuscript: The Role of Sediment Gas Storage in the Methane Dynamics of a Shallow Freshwater Reservoir

The dataset supports the findings of the manuscript entitled: Linking Sediment Gas Storage to the Methane Dynamics in a Shallow Freshwater Reservoir. The manuscript presents results based on in-situ monitoring at the main pre-dam of the Wupper reservoir. The reservoir is located in Germany and the monitoring was conducted from March 2020 to November 2021. The main goal of the monitoring was to measure the spatial variability of methane fluxes and its dynamics in the reservoir. The methane budget of the reservoir was then analysed in combination with estimates of the amount of free gas stored in the sediment matrix, which were derived from acoustic observations. The findings are discussed in the manuscript. Therefore, the dataset provided as an xlsx file includes separate sheets for: <ul> <li>Sediment measurements of potential methane production (PMP), loss on ignition (LOI), dissolved methane (CH4) in porewater, carbon and nitrogen content.</li> <li>Gas content in the sediment estimated from maximum acoustic backscatter for 2020 and 2021 for the reservoir spatial grid.</li> <li>Monthly time-series of methane fluxes (oxidation, diffusion, ebullition, and potential flux at the sediment water interface - PSWI) for each monitoring location and for the reservoir (average of all monitoring locations) with the inclusion of degassing at the dam overflow and advective methane transport (Net-export) by inflow and outflow.</li> <li>Time series of daily mean values of ebullition and environmental parameters monitored in the reservoir.</li> </ul> Explanations and units are provided in column labels. NaN refers to missing data. In this version (Version 2), the calculation of the PSWI has been corrected. It is now calculated as the vertically integrated potential methane production over the top 30 cm layer of the sediment.

Data for: Phylogenomics and historical biogeography of Hydrangeeae (Hydrangeaceae) elucidate the effects of geologic and climatic dynamics on diversification

Demonstrating the process of transregional biogeography and mechanisms underlying evolutionary radiations is crucial to understanding biological evolution. Here, we use Hydrangeeae (Hydrangeaceae), a tribe with a unique disjunct distribution and complex trait variations, using a solid phylogenetic framework, to investigate how geographical and climatic factors interact with functional traits to trigger plant evolutionary radiations. We constructed the first highly supported and dated phylogenetic framework using 79 protein-coding genes obtained from 81 plastomes, representing 63 species and all major clades, and found that most extant species originated from asynchronous diversification of two lineages undergoing repeated expansion and retraction, at middle and high latitudes of the Northern Hemisphere between East Asia and North America, during the Eocene to Pleistocene (driven by geologic and climatic dynamics). In accordance with these drivers, interactions of flora between central-eastern China and Japan occurred frequently after the Late Tertiary. We found that resource limitation and range fragmentation likely accelerated the diversification of Hydrangeeae, which supports the resource-use hypothesis. Our study sheds light on the evolutionary radiation and assembly of flora within East Asia, and the East Asian-North American disjunction, through integration of phylogenomic and biogeographic data with functional trait and ecological data.

opencc-by-4.0May 2023View details →

Data underlying the article: 3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

This repository contains the datasets and results supporting the conclusions of the manuscript "3DDPDs: Describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors".  Publicly available data is not included in this repository. The source code to generate the results gathered here can be found on GitHub (https://github.com/CDDLeiden/3ddpd).

Model simulation data used in "The global impact of the transport sectors on the atmospheric aerosol and the resulting climate effects under the Shared Socioeconomic Pathways (SSPs)" (Righi et al., Earth Syst. Dynam., 2023)

This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (Earth Syst. Dynam., 2023). For details see the README.md file.

Dynamic robotic tracking of underwater targets using reinforcement learning

To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new approaches and techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study is to investigate the use of deep reinforcement learning as a tool for range-only underwater target tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.

Test data for RAW high dynamic range merging

* the data contains a set of .CR2 raw images taken with a Canon 5D mark II * the exposures where done on a tripod in manual mode, the EXIF information is correct * only the exposure times were varied: IMG_7220.CR2 Exposure Time                   : 1 IMG_7221.CR2 Exposure Time                   : 1/5 IMG_7222.CR2 Exposure Time                   : 1/20 IMG_7223.CR2 Exposure Time                   : 1/80 IMG_7224.CR2 Exposure Time                   : 1/320 IMG_7225.CR2 Exposure Time                   : 1/1250 * Author: Ivo Ihrke, ~2011, Universität des Saarlandes / MPI Informatik

Nitrogen-15 dynamic nuclear polarization of nicotinamide derivatives in biocompatible solutions

This data corresponds to the following paper: Title: Nitrogen-15 dynamic nuclear polarization of nicotinamide derivatives in biocompatible solutions DOI: 10.1126/sciadv.add3643 Journal: Science Advances Authors: Josh P. Peters, Arne Brahms, Vivian Janicaud, Maria Anikeeva, Eva Peschke, Frowin Ellermann, Arianna Ferrari, Dana Hellmold, Janka Held-Feindt, Na-mi Kim, Johannes Meiser, Konrad Aden, Rainer Herges, Jan-Bernd Hövener, Andrey N. Pravdivtsev In the upload the figures 2 - 6 are covered, so all figures from the main manuscript with acquired data.   Original abstract for the manuscript: Dissolution dynamic nuclear polarization (dDNP) increases the sensitivity of magnetic resonance imaging by more than 10,000 times enabling in vivo metabolic imaging can be performed non-invasively in real-time. Here, we are developing a group of dDNP polarized tracers based on nicotinamide (NAM). We synthesized 1-15N-NAM and 1-15N nicotinic acid and hyperpolarized them with dDNP, reaching (13.0±1.9)% 15N polarization. We found that the lifetime of hyperpolarized 1-15N-NAM is strongly field- and pH-dependent, with T1 being as long as 41 s at a pH of 12 and 1 T while as short as a few seconds at neutral pH and fields below 1 T. The remarkably short 1-15N lifetime at low magnetic fields and neutral pH drove us to establish a unique pH neutralization procedure. Using 15N dDNP and an inexpensive rodent imaging probe designed in-house, we acquired a 15N MRI of 1-15N-NAM (previously hyperpolarized for more than an hour) in less than one second.

opencc-by-4.0Oct 2022View details →

Extending the application of connectivity metrics within the framework of the characterization of the dynamic behaviour of a WDS subjected to users' activity

Water distribution networks (WDNs) are complex combinations of nodes and links, and the current tendency is to modify their topological structure through the closure of isolation valves for monitoring and water quality reasons. For their analysis, several approaches based on graph theory have recently been proposed, mainly considering steady-state flow conditions. However, in their real functioning, WDNs are continuously subjected to pressure transients generated by manoeuvres on regulation devices or by users’ activity. This study investigates the application of some metrics from graph theory, already used in the context of steady-state analysis, for assessing the effects of changes in the topological structure of a network ‒ due for example to sectorization or branching operations ‒ on its transient response when subjected to manoeuvres on devices such as hydrants, pumps, etc. or users’ activity. The analysis shows that some connectivity metrics can effectively reflect the dynamic pressure behaviour of the network and, thus, provide useful indications for design and management operations taking into account unsteady flow features.

Data and code for: A quantitative model for spatio-temporal dynamics of root gravitropism

This repository contains the experimental data presented in "A quantitative model for spatio-temporal dynamics of root gravitropism" and Python scripts for the presented root model.

Molecular dynamics simulation data 3: Structure of the connexin-43 gap junction channel in a putative closed state

Molecular dynamics data for the manuscript Qi C.*, Acosta-Gutierrez S.*, Lavriha P., Othman A., Lopez-Pigozzi D., Bayraktar E., Schuster D., Picotti P., Zamboni N., Bortolozzi M., Gervasio F.L., Korkhov V.M. Structure of the connexin-43 gap junction channel in a putative closed state. eLife (2023) <a href="https://doi.org/10.7554/eLife.87616.2">https://doi.org/10.7554/eLife.87616.2</a> The dataset includes: Production run gromacs trajectories for the Cx43 gap junction channel (500 mV)

Molecular dynamics simulation data 2: Structure of the connexin-43 gap junction channel in a putative closed state

Molecular dynamics data for the manuscript Qi C.*, Acosta-Gutierrez S.*, Lavriha P., Othman A., Lopez-Pigozzi D., Bayraktar E., Schuster D., Picotti P., Zamboni N., Bortolozzi M., Gervasio F.L., Korkhov V.M. Structure of the connexin-43 gap junction channel in a putative closed state. eLife (2023) <a href="https://doi.org/10.7554/eLife.87616.2">https://doi.org/10.7554/eLife.87616.2</a> The dataset includes: 1. The starting coordinates, topology, MD inputs 2. Production run gromacs trajectories for the Cx43 hemichannel

Input files for the MD simulations and free energy calculations for the article "Water Dissolved in a Variety of Polymers Studied by Molecular Dynamics Simulation and a Theory of Solutions"

Article: <a href="https://pubs.acs.org/doi/10.1021/acs.jpcb.1c04818">J. Phys. Chem. B. 125, 9357–9371 (2021) [DOI: 10.1021/acs.jpcb.1c04818]</a> The structures of the homopolymers and copolymers simulated are shown in Figures 1 and S1 and Tables 2 and 3. All-atom MD simulation was carried out using GROMACS, and this repository provides the input files with the GAFF/RESP force and initial coordinate files. The free energy of water dissolution was obtained with <a href="https://sourceforge.net/projects/ermod/">ERmod</a>, and the input files for the free-energy calculations are also contained. See the README files for details.

An analytical study of capillary rise dynamics: Critical conditions and hidden oscillations - Research Data

This dataset provides the research data and software for the publication: M. Fricke, E. Ouro-Koura, S. Raju, R. von Klitzing, J. De Coninck, D. Bothe: An analytical study of capillary rise dynamics: Critical conditions and hidden oscillations which was published in the Journal Physica D: Nonlinear Phenomena. The experimental data for the capillary rise of silicon oil, ethanol and ether has been extracted from the publication D. Quéré: Inertial capillarity, EPL 39 533, DOI:10.1209/epl/i1997-00389-2 (1997) using image analysis methods. It is stored in the csv files Silicon_Oil_Quere1997, Ethanol_Quere1997.csv and Ether_Quere1997. Please use the Python scripts silicon-oil.py, ethanol.py and ether.py to solve the ordinary differential equation model and to generate the plots presented in our publication. The Python library matplotlib was used for plotting. A simple explicit Euler scheme proved sufficient to solve the ordinary differential equation in its dimensionless form. It is implemented in the file capRiseOdeSolver.py.

opencc-by-4.0Mar 2023View details →

A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment

An experiment was conducted to understand if and how households change their power consumption in response to variable hourly electricity prices. The data were collected from several Norwegian regions, and various price signals were tested over two winter periods from early 2020 to spring 2021. The dataset includes hourly consumption data of all participating households and answers to three surveys about household characteristics such as electric appliances, living conditions, socio-demographic variables, and willingness to be flexible. Temperature data are added to the dataset from public sources. This comprehensive dataset can be used for in-depth analysis of household flexibility potential. Furthermore, subgroups, such as low-income households or highly electrified households with electricity as a primary heating source, can be investigated to enhance the understanding of how these are affected by variable power prices. The dataset is described in detail in an accompanying data article in Data in Brief: <a href="https://www.sciencedirect.com/science/article/pii/S2352340923006716">A rich dataset of hourly residential electricity consumption data and survey answers from the iFlex dynamic pricing experiment - ScienceDirect</a> Supplementary figures containing the survey results are available here: <a href="../records/11580541">Supplementary result diagrams from household surveys on implicit demand response (zenodo.org)</a> Survey answers in Norwegian are available here: <a href="https://zenodo.org/records/15063303">iFleks-prosjekt: Spørreundersøkelser med husholdninger og næringsliv om forbruksrespons på elektrisitetspriser</a>

Global Cenozoic paleogeography and paleo-dynamic topography

This repository contains the data and files supporting the research paper titled: "Impact of mantle convection and dynamic topography on the Cenozoic paleogeography of Central Eurasia and the West Siberian Seaway", EPSL 2024. It contains global paleogeography for every millionth year of the Cenozoic and paleo-Dynamic topography from 100 - 0 Ma.  ### Contents ### - Paleogeography:   - This folder contains NetCDF files representing global paleogeographic reconstruction for each millionth year of the Cenozoic era. There is also a folder with global plots for each time step.   - Paleo-Dynamic_Topography: - This folder contains NetCDF files from the paleo-dynamic topography models in 1 Myr time interval for the last 100 million years distributed in subfolders containing data computed in different reference frames i.e. paleomagnetic/pmag , mantle and dynamic topography in the reference frame of simplified individual plates where plate motions are from Torsvik et al. 2019 (as for both pmag and mantle), and we distinguish the plates using  present-day plate boundaries. Within these subfolders you find the dynamic topography based on various tomography models:         - SMEAN2: Dynamic topography results based on the SMEAN2 tomography model (preferred).         - Tx2019Slab: Dynamic topography results based on the Tx2019Slab tomography model (preferred).         - S40RTS: Dynamic topography results based on the S40RTS tomography model.         - SEMUCB-WM1: Dynamic topography results based on the SEMUCB-WM1 tomography model.

opencc-by-2.0Aug 2023View details →

Spatial Data collection for study spatial transition dynamic of Rohingya settlement in Bangladesh: 1st version

Full open access article can be found in: <a href="https://doi.org/10.1016/j.landusepol.2023.106874">https://doi.org/10.1016/j.landusepol.2023.106874</a>   Full Changelog: <a href="https://github.com/ssujit/SpatialTransitionDynamic/commits/version">https://github.com/ssujit/SpatialTransitionDynamic/commits/version</a>

Pore-scale fluid dynamics resolved in pressure fluctuations at the Darcy scale

Pressure data from the 5 experiments described in Spurin et al. Pore-scale fluid dynamics resolved in pressure fluctuations at the Darcy scale. GRL, 2023.  There are 2 data sets from the pore-scale observations with either gas or oil injected. There are 3 data sets from the core-scale observations: 1 with oil injected, and 2 with gas injected. The 2 gas experiments were conducted in the same sample, just with the flow direction reversed.  This upload also includes the code to perform the continuous wavelet transform on the pressure data.

Analysis Products: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency

This record contains analysis products for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen et al. Please refer to the READMEs in the directories, which are summarized below. The record contains the following files: `clusters.tsv`:  contains the cluster id, name and colour of clusters in the paper scATAC.zip Analysis products for the single-cell ATAC-seq data. Contains: - `cells.tsv`: list of barcodes that pass QC. Columns include:     - `barcode`     - `sample`: (time point)     - `umap1`     - `umap2`     - `cluster`     - `dpt_pseudotime_fibr_root`: pseudotime values treating a fibroblast cell as root     - `dpt_pseudotime_xOSK_root`: pseudotime values treating xOSK cell as root - `peaks.bed`: list of peaks of 500bp across all cell states. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA. - `features.tsv`: 50 dimensional representation of each cell  - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`  scATAC_clusters.zip Analysis products corresponding to cluster pseudo-bulks of the single-cell ATAC-seq data.  - `clusters.tsv`: contains the cluster id, name and colour used in the paper - `peaks`: contains `overlap_reproducibilty/overlap.optimal_peak` peaks called using ENCODE bulk ATAC-seq pipeline in the narrowPeak format. - `fragments`: contains per cluster fragment files  scATAC_scRNA_integration.zip Analysis products from the integration of scATAC with scRNA. Contains: - `peak_gene_links_fdr1e-4.tsv`: file with peak gene links passing FDR 1e-4. For analyses in the paper, we filter to peaks with absolute correlation >0.45. - `harmony.cca.30.feat.tsv`: 30 dimensional co-embedding for scATAC and scRNA cells obtained by CCA followed by applying Harmony over assay type. - `harmony.cca.metadata.tsv`: UMAP coordinates for scATAC and scRNA cells derived from the Harmony CCA embedding. First column contains barcode. scRNA.zip Analysis products for the single-cell RNA-seq data. Contains: - `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca), knn graphs, all associated metadata. Note that barcode suffix (1-9 corresponds to samples D0, D2, ..., D14, iPSC) - `genes.txt`: list of all genes - `cells.tsv`: list of barcodes that pass QC across samples. Contains:     - `barcode_sample`: barcode with index of sample (1-9 corresponding to D0, D2, ..., D14, iPSC)      - `sample`: sample name (D0, D2, .., D14, iPSC)     - `umap1`     - `umap2`     - `nCount_RNA`     - `nFeature_RNA`     - `cluster`     - `percent.mt`: percent of mitochondrial transcripts in cell     - `percent.oskm`: percent of OSKM transcripts in cell - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt`  - `pca.tsv`: first 50 PC of each cell - `oskm_endo_sendai.tsv`: estimated raw counts (cts, may not be integers) and log(1+ tp10k) normalized expression (norm) for endogenous and exogenous (Sendai derived) counts of POU5F1 (OCT4), SOX2, KLF4 and MYC genes. Rows are consistent with `seurat.rds` and `cells.tsv` multiome.zip multiome/snATAC: These files are derived from the integration of nuclei from multiome (D1M and D2M), with cells from day 2 of scATAC-seq (labeled D2).  - `cells.tsv`: This is the list of nuclei barcodes that pass QC from multiome AND also cell barcodes from D2 of scATAC-seq. Includes:     - `barcode`     - `umap1`: These are the coordinates used for the figures involving multiome in the paper.     - `umap2`: ^^^      - `sample`: D1M and D2M correspond to multiome, D2 corresponds to day 2 of scATAC-seq     - `cluster`: For multiome barcodes, these are labels transfered from scATAC-seq. For D2 scATAC-seq, it is the original cluster labels.  - `peaks.bed`: This is the same file as scATAC/peaks.bed. List of peaks of 500bp. 4th column contains the peak set label. Note that ~5000 peaks are not assigned to any peak set and are marked as NA. - `cell_x_peak.mtx.gz`: sparse matrix of fragment counts within peaks. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (combine sample + barcode). Rows correspond to peaks in `peaks.bed`. - `features.no.harmony.50d.tsv`: 50 dimensional representation of each cell prior to running Harmony (to correct for batch effect between D2 scATAC and D1M,D2M snMultiome). Rows correspond to cells from `cells.tsv`. - `features.harmony.10d.tsv`: 10 dimensional representation of each cell after running Harmony. Rows correspond to cells from `cells.tsv`. multiome/snRNA: - `seurat.rds`: seurat object that contains expression data (raw counts, normalized, and scaled), reductions (umap, pca),associated metadata. Note that barcode suffix (1,2 corresponds to samples D1M, D2M). Please use the UMAP/features from snATAC/ for consistency. - `genes.txt`: list of all genes (this is different from the list in scRNA analysis) - `cells.tsv`: list of barcodes that pass QC across samples. Contains:     - `barcode_sample`: barcode with index of sample (1,2 corresponding to D1M, D2M respectively)      - `sample`: sample name (D1M, D2M)     - `nCount_RNA`     - `nFeature_RNA`     - `percent.oskm`: percent of OSKM genes in cell - `gene_x_cell.mtx.gz`: sparse matrix of gene counts. Load using scipy.io.mmread in python or readMM in R. Columns correspond to cells from `cells.tsv` (barcode suffix contains sample information). Rows correspond to genes in `genes.txt`