Find research datasets worth reusing

Dataset supporting the submission to the journal "Ocean Dynamic" and titled "Hybrid covariance super-resolution data assimilation"

Open the record for dataset details and reuse information.

Phonon Properties and Lattice Dynamics of Two- and Tri-Layered Lead Iodide Perovskites Comprising Butylammonium and Methylammonium Cations—Temperature-Dependent Raman Studies

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Temporal Dynamics and Demographic Profiles: Evaluating the Cultural Impact of Public Engagements with Civil War Shelters in Alicante (Spain)

In the context of sustainable tourism, understanding visitors' needs and preferences is crucial for planning effective strategies that promote the conservation of historical and cultural heritage. In this regard, the quantitative analysis of survey results from visitors to Civil War air-raid shelters in Alicante, Spain, emerges as a valuable tool for gaining detailed insights that enable the planning of specific visits and events. This paper aims to explore the significance of using quantitative data in developing strategies for sustainable tourism within this particular context.

Novel Atmospheric Dynamics Shape Inner Edge of Habitable Zone Around White Dwarfs [Dataset]

The simulation results on planets orbiting around white dwarfs using <a href="https://github.com/storyofthewolf/ExoCAM">ExoCAM</a>. These files are used to make figures and tables in our work "Novel Atmospheric Dynamics Shape Inner Edge of Habitable Zone Around White Dwarfs".

opencc-by-4.0Dec 2023View details →

Data for: Enhanced Hydrologic Connectivity and Solute Dynamics Following Wildfire and Drought in a Contaminated Temperate Peatland Catchment

Data underlying the publication: "Enhanced Hydrologic Connectivity and Solute Dynamics Following Wildfire and Drought in a Contaminated Temperate Peatland Catchment".

Inlists for "An Alternative Channel to Black Hole Low-Mass X-ray Binaries: Dynamical Friction of Dark Matter? "

In this work, we employ MESA code to diagnose whether the dynamical friction between dark matter and the companion stars can drive BH binaries to evolve toward the observed BH LMXBs and alleviate the effective temperature problem. Assuming that there exists a density spike of dark matter around BH, the dynamical friction can produce an efficient angular momentum loss, driving BH binaries with an intermediate-mass companion star to evolve into BH LMXBs for a spike index higher than $\gamma = 1.58$. MESA version: r12115; SDK compiler: mesasdk-x86_64-linux-20190830

opencc-by-4.0Mar 2024View details →

Advancing Recyclable Thermosets through Dynamic Covalent Metathesis based on C=C/C=N Exchange Reactions

DFT optimized structures accompanying the paper "Advancing Recyclable Thermosets through Dynamic Covalent Metathesis based on C=C/C=N Exchange Reactions".

Alpha1-antitrypsin molecular dynamics simulations

The zip archive contains molecular dynamics trajectories and related files organized into two main directories: ED for essential dynamics and MD for standard molecular dynamics. Each folder contains two simulations (ed1 and ed2 subfolders of ED; md1 and md2 subfolders for MD). Reference structures, corresponding to the beginning of each simulation are given both as GROMACS gro files and as pdb files. Trajectories are in the xtc GROMACS format, which can be loaded for visualization both in VMD and in Pymol.

Dynamical friction and measurements of the splashback radius in galaxy clusters (Data)

Data from O'Shea et al. (2024). Dynamical friction and measurements of the splashback radius in galaxy clusters.

Data from: Boom and bust: The effects of masting on seed predator range dynamics and trophic cascades

Aim: Spatiotemporal variation in resource availability is a strong driver of animal distributions. In northern hardwood and boreal forests of the northeastern United States, tree mast events provide resource pulses that drive the population dynamics of small mammals, including the American red squirrel (Tamiasciurus hudsonicus), a primary songbird nest predator. This study sought to determine whether mast availability ameliorates their abiotic limits, enabling red squirrel elevational distributions to temporarily expand and negatively impact high elevation songbirds. Location: Northeastern United States Methods: We used two independent datasets to evaluate our hypotheses. First, we fit a dynamic occupancy model using data from camera trap surveys to evaluate red squirrel distributional responses to pulses in tree mast. We also assessed population responses using systematic auditory surveys analyzed with an open-population binomial mixture model. Further, we used modeled red squirrel abundance in nest-survival models to evaluate whether their abundance is correlated with daily nest survival of three songbird species. Results: Tree mast provided a critical resource pulse that resulted in a two-fold increase in the annual elevational distribution of red squirrels. The elevational distribution of red squirrels ranged from a minimum of ~450 m (range: 663 – 1145 m asl) following two consecutive years without a masting event to a maximum of over 1000 m (range: 443 – 1545 m asl) after a large mast event. Daily nest survival of three songbird species tended to decline with an increase in the abundance of red squirrels. Main Conclusions: Tree mast is a central biological phenomenon in many temperate and boreal forests. This study reveals how this resource pulse results in range changes in a small mammal that is both a seed and bird predator, as well as prey for many carnivores. Thus, understanding this phenomenon can inform the conservation and management of northern forests, including breeding songbirds.

Data from: Unravelling the complexities of temporal biotic homogenisation and heterogenization: Avian assemblage dynamics in Britain

Biotic homogenization is a process whereby species assemblages become more similar through time. The standard way of identifying the process of biotic homogenization is to look for decreases in spatial beta-diversity. However, using a single assemblage-level metric to assess homogenization can mask important changes in the occupancy patterns of individual species. Here, we analysed changes in the spatial beta-diversity patterns (i.e., biotic heterogenization or homogenization) of British bird assemblages within 30km x 30km regions between two periods (1988-1991 and 2008-2011). We partitioned the change in spatial beta-diversity into extirpation and colonisation resultant change (i.e., change in spatial beta-diversity within each region resulting from both extirpation and colonisation). We used measures of abiotic change in combination with Bayesian modelling to disentangle the drivers of biotic heterogenization and homogenization. We detected both heterogenization and homogenization across the two time periods and three measures of diversity (taxonomic, phylogenetic, and functional). In addition, both extirpation and colonisation contributed to the observed changes, with heterogenization mainly driven by extirpation and homogenization by colonisation. These assemblage-level changes were primarily due to shifting occupancy patterns of generalist species. Compared to habitat generalists, habitat specialists had significantly (i) higher average contributions to colonisation resultant change (indicating heterogenization within a region due to colonisation) and (ii) lower average contributions to extirpation resultant change (indicating homogenization from extirpation). Generalists showed the opposite pattern. Increased extirpation resultant homogenization within regions was associated with increased urban land cover and decreased habitat diversity, precipitation, and temperature. Changes in extirpation resultant heterogenization and colonisation resultant heterogenization were associated with differences in elevation between regions and changes in temperature and land cover. Many of the 'winners' (i.e., species that increased in occupancy) were species that had benefitted from conservation action (e.g., buzzard (Buteo buteo)). The 'losers' (i.e., those that decreased in occupancy) consisted primarily of previously common species, such as cuckoo (Cuculus canorus). Our results show that focusing purely on changes in spatial beta-diversity over time may obscure important information about how changes in the occupancy patterns of individual species contribute to homogenization and heterogenization.

Temporal pattern recognition in retinal ganglion cells is mediated by dynamical inhibitory synapses

All data used in the study https://doi.org/10.1101/2023.01.12.523643  can be found here.

Data (2) with paper "Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells"

See also: https://zenodo.org/records/11401115 Data files related to manuscript: Wehrens M, Ershov D, Rozendaal R, Walker N, Schultz D, Kishony R, Levin PA, Tans SJ (2018). “Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells”. Current Biology. <a href="https://doi.org/10.1016/j.cub.2018.02.006">https://doi.org/10.1016/j.cub.2018.02.006</a>   Scripts are available at: <a href="https://github.com/TansLab/Tans_filamentation">https://github.com/TansLab/Tans_filamentation</a>   And you will also need the additional scripts from the repositories: <a href="https://github.com/TansLab/Common_libraries">https://github.com/TansLab/Common_libraries</a> <a href="https://github.com/TansLab/Tans_Schnitzcells">https://github.com/TansLab/Tans_Schnitzcells</a>   Script that generates figures: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a>   And more specifically, data is loaded, (partially analyzed,) and plotted here: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m</a>   Figure 3   See also <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a> <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m</a>   Related files: <table> <tbody> <tr> <td> Data files for cell morphology properties </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos2crop/data/pos2crop-skeletonData.mat </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos3crop/data/pos3crop-skeletonData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with fluorescence data </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos2crop/analysis/straightenedCells/2016-04-07pos2crop_straightFluorData.mat </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos3crop/analysis/straightenedCells/2016-04-07pos3crop_straightFluorData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with division and lineage data (also supplied above) </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos2crop/data/pos2crop-Schnitz.mat </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777/pos3crop/data/pos3crop-Schnitz.mat </td> </tr> </tbody> </table>   Supplemental figure with nucleoid data   See also <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a> <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160422_filamentRecoveryFtslabelLocations.m</a>   Related files: <table> <tbody> <tr> <td> Data files for cell morphology properties </td> </tr> <tr> <td> 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/data/pos1cropd-skeletonData.mat </td> </tr> <tr> <td> 2017-10-12_FilaRecovery_hupA-mRuby2/pos1cropa2/data/pos1cropa2-skeletonData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with fluorescence data </td> </tr> <tr> <td> 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/analysis/straightenedCells/2017-11-08pos1cropd_straightFluorData.mat </td> </tr> <tr> <td> 2017-10-12_FilaRecovery_hupA-mRuby2/pos1cropa2/analysis/straightenedCells/2017-10-12pos1cropa2_straightFluorData.mat </td> </tr> </tbody> </table>   <table> <tbody> <tr> <td> Data files with division and lineage data (also supplied above) </td> </tr> <tr> <td> 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/data/pos1cropd-Schnitz.mat </td> </tr> <tr> <td> 2017-10-12_FilaRecovery_hupA-mRuby2/pos1cropa2/data/pos1cropa2-Schnitz.mat </td> </tr> </tbody> </table>   Lookup data file: 2017-11-08_FilaRecovery_asc1106_hupA-mCherry/pos1cropd/data/slookup.mat

opencc-by-4.0Mar 2018View details →

Data with paper "Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells"

See also the other Zenodo record: https://zenodo.org/records/11401470   Data files related to manuscript: Wehrens M, Ershov D, Rozendaal R, Walker N, Schultz D, Kishony R, Levin PA, Tans SJ (2018). “Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells”. Current Biology. <a href="https://doi.org/10.1016/j.cub.2018.02.006">https://doi.org/10.1016/j.cub.2018.02.006</a>   Currently, single cell experimental time trace data for figures 1, 2 and 4 is added. Additional data will follow.   Scripts are available at: <a href="https://github.com/TansLab/Tans_filamentation">https://github.com/TansLab/Tans_filamentation</a>   And you will also need the additional scripts from the repositories: <a href="https://github.com/TansLab/Common_libraries">https://github.com/TansLab/Common_libraries</a> <a href="https://github.com/TansLab/Tans_Schnitzcells">https://github.com/TansLab/Tans_Schnitzcells</a>   Script that generates figures: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m">https://github.com/TansLab/Tans_filamentation/blob/master/ershovwehrensallfigures.m</a>   And more specifically, data is loaded, (partially analyzed,) and plotted here: <a href="https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m">https://github.com/TansLab/Tans_filamentation/blob/master/script20160429_filamentRecoveryDivisionRatioss.m</a>   Description of the .mat files   These .mat files contain the lineage information, which were used to generate figures 1 and 2.   Each row corresponds to a cell from birth to division.   Most important fields are: P is its parent cell (number referring to the row of the table). E is the daughter cell after this cell divides D is the other daughter cell after this cell divides frame_nrs is in which frame this cell lived areapx is its area in pixels   Different methods were used to determine the bacterias length. For the tetracycline experiments, often the field “length_fitNew” was used, which is a higher order polynomial fitted through the bacterium. For the other stress conditions, mostly the field “length_skeleton” was used, which is the length of the skeleton of the bacteria, extrapolated until it reaches the bacterial edge (this was done because in those experiments, these bacteria often had weird shapes that couldn’t be fitted by polynomials).   The growth rate of the bacteria is determined by fitting an exponential curve through length information of multiple frames. Given the fluctuation of growth rates, sometimes bacteria grow relatively slow over a certain amount of frames, or relatively fast. Therefor, sometimes the fit is done using more or less frames. The growth rate information can be found in e.g. the parameters 'muP15_fitNew_all','muP9_skeleton_all','muP5_skeleton_all', where mu refers to growth rate, PX indicates X frames around the frame of interest were used for the fit, and fitNew_all or skeleton_all refers respectively to which length parameter was used for the fit.   Note that birth sizes and interdivision times, as wel as added length, can be calculated from the above parameters using the data structure. (See also applicable scripts.)   The data also contains more fields with information about length and size, and also fields for fluorescence data. The latter is not really applicable here.    See also the methods section of the paper for more information.   INFORMATION ABOUT THE DATA   Files for the SulA condition: <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos1crop\data\pos1crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos2crop\data\pos2crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos3crop\data\pos3crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 0 </td> </tr> <tr> <td> \2016-04-08_FilaRecovery_sulA_recovery_200uM_IPTG\pos7crop\data\pos7crop-Schnitz.mat </td> <td> 0 </td> </tr> </tbody> </table> If switch time is zero, recording started at the switch time.   For the temperature condition <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> 2016-03-23_FilaRecovery_asc777_42C\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 450 </td> </tr> <tr> <td> 2016-04-07_FilaRecovery_asc777\pos2crop\data\pos2crop-Schnitz.mat </td> <td> 329 </td> </tr> </tbody> </table>   Delta min tetracycline condition <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos1cropb\data\pos1cropb-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos2cropa\data\pos2cropa-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos2cropb\data\pos2cropb-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos2cropc\data\pos2cropc-Schnitz.mat </td> <td> 5 </td> </tr> <tr> <td> 2017-09-22_FilaRecovery_asc1035_DeltaMinCDE\pos3cropa\data\pos3cropa-Schnitz.mat </td> <td> 5 </td> </tr> </tbody> </table>   Tetracycline data Only data sets 1 to 5 where used here     <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos3_long.mat </td> <td> 890.9800  </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos4.mat </td> <td> 404.7500         </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos4_long.mat </td> <td> 0  </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos5.mat </td> <td> 529.7600  </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\1uM_pos5_long.mat </td> <td> 0 </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\2uM_pos2.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\2uM_pos4.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\2uM_pos6.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\10uM_pos1.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\10uM_pos3.mat </td> <td> NA </td> </tr> <tr> <td> F schijf AmolfBackup_3april2014\USE_DIV\10uM_pos6_long.mat </td> <td> NA </td> </tr> </tbody> </table>   Tetracycline data 2 (redundant with above)   <table> <tbody> <tr> <td> Data file </td> <td> Switch time from stress to stress-free condition (min) </td> </tr> <tr> <td> 2013-12-09\pos3crop\data\pos3crop-Schnitz.mat </td> <td> 890.9800  </td> </tr> <tr> <td> 2013-09-24\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 404.7500         </td> </tr> <tr> <td> 2013-12-16\pos4crop\data\pos4crop-Schnitz.mat </td> <td> 0  </td> </tr> <tr> <td> 2013-09-24\pos5crop\data\pos5crop-Schnitz.mat </td> <td> 529.7600  </td> </tr> <tr> <td> 2013-12-16\pos5crop\data\pos5crop-Schnitz.mat </td> <td> 0 </td> </tr> </tbody> </table>

Genetic diversity and spread dynamics of SARS-CoV-2 variants present in African populations

The dynamics of coronavirus disease-19 (COVID-19) have been extensively researched in many settings around the world, but little is known about these patterns in Africa. 7540 complete nucleotide genomes from 51 African nations were obtained and analysed from the National Center for Biotechnology Information (NCBI) and Global Initiative on Sharing Influenza Data (GISAID) databases to examine genetic diversity and spread dynamics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) lineages circulating in Africa. Utilising a variety of clade and lineage nomenclature schemes, we looked at their diversity, and used maximum parsimony inference methods to recreate their evolutionary divergence and history. According to this study, only 465 of the 2610 Pango lineages found to have existed in the world circulated in Africa after three years of the COVID-19 pandemic outbreak, with five different lineages dominating at various points during the outbreak. We identified South Africa, Kenya, and Nigeria as key sources of viral transmissions between Sub-Saharan African nations. These findings provide insight into the viral strains that are circulating in Africa and their evolutionary patterns.

Data for: Genetic relatedness shapes social dynamics in a threatened finch: Implications for population assessment

Tropical granivorous finches often form large flocks around resources. The composition of these flocks, whether they are random groups of individuals or comprise related birds travelling together, is currently unknown. Understanding this distinction would aid in assessing the accuracy of population counts. To bridge this knowledge gap, we combined high-frequency location tracking with comprehensive genetic sequencing to investigate the relationship between pairwise association strength and genetic relatedness in Gouldian finches (Erythrura gouldiae). Our study revealed that birds captured near each other were more inclined to travel together, and their relatedness was significantly linked to the strength of their association. These findings suggest that within-flock associations are influenced by genetic relatedness, contributing to the stability of the flock size. We propose that juvenile kin associations play a pivotal role in this dynamic, potentially enhancing survival rates by forming sibling subgroups. The consistent flock sizes of Gouldian finches during movement have implications for estimating population sizes from waterhole counts, allowing flocks to be considered as distinct units for concurrent counts at multiple waterholes. This approach would offer a reasonably accurate method for estimating local populations, and conducting repeated counts on consecutive days could provide reliable and replicable results.

Supplementary material for 3D wave propagation and earthquake dynamic rupture simulations in complex poroelastic media

This repository contains all files to reproduce the SeisSol simulations for the article "3D wave propagation and earthquake dynamic rupture simulations in complex poroelastic media" submitted to GJI.

Role of net charges and charge clustering in a dynamic disordered complex between an IDP and a folded domain.

Simulation input files and trajectories for the paper "Role of net charges and charge clustering in a dynamic disordered complex between an IDP and a folded domain".  Files here: cg_prota_multigd.tgz -- coarse-grained simulations of prothymosin alpha with multiple globular domains gd_prota_cg_umbrella.tgz -- coarse grained umbrella sampling simulations of prothymosin alpha associating with a single globular domain prota_aa.tgz -- all-atom simulations in explicit water of prothymosin alpha alone, with several force fields gd_amber03ws.tgz -- all-atom simulations in explicit water of globular domain alone, with amber ff03ws force field gd_des-amber.tgz-- all-atom simulations in explicit water of globular domain alone, with des-amber force field prota+gd_des-amber-SF1.0.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, des-amber-SF1.0 force field prota+gd_des-amber.tgz-- all-atom simulations in explicit water of prothymosin alpha + single globular domain, des-amber force field prota+gd_amber_ff99sbws.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, amber ff99sbws force field prota+gd_amber_ff99sb-disp.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, amber ff99sb-disp force field prota+gd_amber_ff03ws.tgz -- all-atom simulations in explicit water of prothymosin alpha + single globular domain, amber ff03ws force field

opencc-by-4.0Jun 2024View details →

supplementary tables for 'The dynamic landscape of competing endogenous RNA (ceRNA) network in Early-Onset Preeclampsia under hypoxia condition '

Open the record for dataset details and reuse information.