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Fig 5 in Evaluation of Gardi Bangana dero and Sahar Tor putitora with Rohu Labeo rohita and Naini Cirrhinus mrigala in polyculture at different stocking combinations
Fig 5: Transparency (cm) variation at different polyculture treatments
Fig 8 in Evaluation of Gardi Bangana dero and Sahar Tor putitora with Rohu Labeo rohita and Naini Cirrhinus mrigala in polyculture at different stocking combinations
Fig 8: pH variation at different polyculture
Fig 7 in Evaluation of Gardi Bangana dero and Sahar Tor putitora with Rohu Labeo rohita and Naini Cirrhinus mrigala in polyculture at different stocking combinations
Fig 7: Temperature (0C) variation at different polyculture
Dataset for publication "Tuning germanane bandgap via cyanoethyl functionalization for cutting-edge photoactive cathodes: photo-enhanced hybrid zinc-ion capacitor evaluation"
<p>This is a dataset for a paper "Tuning germanane bandgap via cyanoethyl functionalization for cutting-edge photoactive cathodes: photo-enhanced hybrid zinc-ion capacitor evaluation". All details about the data are included in the readme file.</p>
Evaluation of the Prototyping of a Web Tool for Customization and Management of Rubrics: A Focus Group Study
<p>Files containing the focus group chat and audio transcripted, the questionnaire used, the questionnaire answers sheet and the Figma prototypes applied on the evaluation of the prototyping of a web information system for rubric customization and management: RubricPro</p>
Fig 4 in Evaluating the sustainability of fishery resources and fishing gears: Case study of Ngoyè and Elabè, Kribi, South Cameroon
Fig 4: Catch profiles: a) Beach seine; b) bottom-set gillnet
Fig 2 in Evaluating the sustainability of fishery resources and fishing gears: Case study of Ngoyè and Elabè, Kribi, South Cameroon
Fig 2: Distribution of beach seines according to mesh size
Fig 1 in Evaluating the sustainability of fishery resources and fishing gears: Case study of Ngoyè and Elabè, Kribi, South Cameroon
Fig 1: Localisation study area
Fig 3 in Evaluating the sustainability of fishery resources and fishing gears: Case study of Ngoyè and Elabè, Kribi, South Cameroon
Fig 3: Distribution of bottom gillnets according to mesh size
Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics: dataset
<div><strong>Abstract:</strong></div> <div>(from [1])</div> <div>Their great versatility makes polymer nanocomposites an important class of engineering materials. In order to gain detailed insights into the nanoscale mechanisms underlying their macroscopic mechanical properties, molecular dynamics (MD) simulations are a valuable tool to complement experimental studies. In this work, we modify the analytical potential functions of an efficient bead-spring model representing a generic polymer nanocomposite to account for the breaking of covalent bonds. We perform uniaxial tensile simulations of double-notched specimens and validate the model using experimental trends for overall stiffness, strength, and toughness. First, we study the effects of sample size, notch geometry, strain rate, temperature, and molar mass for the pure thermoplastic matrix material. Second, we analyze the influence of filler size and filler content on the mechanical behavior of the polymer nanocomposite. With this study, we show that in both the development of new materials and the optimization of established materials, it is possible to gain important preliminary insights into the effects of pertinent material characteristics with a simple MD setup, which can then be further refined by increasing the complexity of the material description and the boundary conditions. </div> <div> </div> <div> </div> <div><strong>Contact:</strong></div> <div>Felix Weber</div> <div>Institute of Applied Mechanics</div> <div>Friedrich-Alexander-Universität Erlangen-Nürnberg</div> <div>Egerlandstr. 5</div> <div>91058 Erlangen</div> <div>Germany</div> <div> </div> <div> </div> <div><strong>Software:</strong></div> <div>All simulations were performed with LAMMPS [2,3] (version 23 June 2022, patch_23Jun2022_update3) </div> <div> </div> <div>Compiler: GNU C++ 11.2.0 with OpenMP not enabled</div> <div>C++ standard: C++11</div> <div> </div> <div>Active compile time flags:</div> <div>-DLAMMPS_GZIP</div> <div>-DLAMMPS_SMALLBIG</div> <div> </div> <div>Installed packages:</div> <div>BPM CLASS2 DPD-BASIC EXTRA-DUMP EXTRA-FIX EXTRA-MOLECULE INTEL KSPACE MANYBODY </div> <div>MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT </div> <div> </div> <div>Moreover, we employ a self-avoiding random walker [4,5] implemented in MATLAB [6] for the initial positioning of the polymer chains and nanoparticles.</div> <div> </div> <div> </div> <div><strong>License:</strong></div> <div>Creative Commons Attribution 4.0 International</div> <div> </div> <div> </div> <div><strong>Context:</strong></div> <div>This dataset contains the results presented in [1] and the necessary data to obtain those.</div> <div> </div> <div> </div> <div><strong>Content:</strong></div> <div>Throughout this data set, LAMMPS lj units are used. The files to reproduce our simulations and their results are structured as follows:</div> <div>- 01_neat: Neat polymer systems</div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations, including the notch insertion (token "initcrack")</div> <div> - 1.1: Simulations for different sample sizes/numbers of chains (token "chains") at constant molar mass/number of beads per chain</div> <div> - 1.3: Simulations for different widths of the Dirichlet boundary (token "diri")</div> <div> - 2.1: Simulations for different critical bond lengths (token "bondcrit")</div> <div> - 2.2: Simulations for different bond breaking probabilities (token "bondcprob")</div> <div> - 3.1: Simulations for different crack widths (token "crackwidth")</div> <div> - 3.2: Simulations for different crack lengths (token "crackdepth")</div> <div> - 4: Simulations for different strain rates (token "strainrate")</div> <div> - 5: Simulations for different temperatures (token "tem")</div> <div> - 6: Simulations for different molar masses/numbers of beads per chain (token "chain-len")</div> <div>- 02_PNC: Polymer nanocomposite (PNC) systems </div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations for different filler radii (token "rF") and filler contents/numbers (token "nF"), including the notch insertion (token "initcrack")</div> <div>- parameter_study: Postprocessing of the MD results </div> <div> - parameter_study.xlsx: Overview of the simulations with their respective parameters and statistical analysis of stiffness, strength, and toughness from filtered stress-strain curves (Savitzky-Golay filter applying a linear polynomial and frame length 21)</div> <div> - .csv files of the single sheets of parameter_study.xlsx:</div> <div> - samples.csv: Individual specimens</div> <div> - averages.csv: Statistical analysis of the different samples corresponding to one batch</div> <div> </div> <div>Each simulation directory contains:</div> <div>- LAMMPS input script (*.in) of the simulation</div> <div>- input.prm: Input parameters of the simulation (read by the input script)</div> <div>- LAMMPS data file (*.data, molecular style) of the investigated sample</div> <div>- LAMMPS_out: Resulting LAMMPS data files, log files and simulation results in tabulated form</div> <div> - additional files for the tensile tests: </div> <div> - brokenbonds.dat: Fix print output for fix brokenbondsprint (step time brokenbondsPerStep brokenbondsSum)</div> <div> - stressstrain.dat: Time-averaged data for fix dumpOpt (step v_strain_xx v_OBSstrain_xx v_Piola_xx) with the local strain at the crack tip v_OBSstrain_xx</div> <div> - thermo_out.Dat: Thermodynamic output in condensed tabulated form</div> <div> - thermo_out_SG.Dat: Thermodynamic output in condensed tabulated form, filtered by a Savitzky-Golay filter (linear polynomial, frame length 21)</div> <div> - thermo_out_STD.Dat: Standard deviation between the filtered and unfiltered data</div> <div>- job.out: Simulation log file</div> <div>- meta.info: Meta data of the simulation run</div> <div> </div> <div>Naming convention:</div> <div>- 01_neat: GTPm-[number of chains]_chains-[number of beads per chain]_chain_len-[temperature]_tem-[parameter value]_[parameter]-[sample]</div> <div> - [parameter]: Parameter studied, i.e. diri/bondcrit/bondcprob/crackwidth/crackdepth/strainrate/tem (see above)</div> <div> - [parameter value]: Value of the parameter studied</div> <div> - [sample]: Sample ID</div> <div>- 02_PNC: GTPm_rF-[filler radius]_nF-[number of fillers]_[sample]</div> <div> - [sample]: Sample ID</div> <div> </div> <div>Output quantities (columns of *.Dat files):</div> <div>- Step: time step</div> <div>- Time: time</div> <div>- TotEng: total energy</div> <div>- PotEng: potential energy</div> <div>- KinEng: kinetic energy</div> <div>- E_pair: pair energy</div> <div>- E_bond: bond energy</div> <div>- E_angle: angle energy</div> <div>- E_dihed: dihedral energy</div> <div>- Temp: temperature</div> <div>- Press: hydrostatic pressure</div> <div>- Pxx: xx component of pressure tensor</div> <div>- Pyy: yy component of pressure tensor</div> <div>- Pzz: zz component of pressure tensor</div> <div>- Pxy: xy component of pressure tensor</div> <div>- Pxz: xz component of pressure tensor</div> <div>- Pyz: yz component of pressure tensor</div> <div>- Volume: volume of simulation box</div> <div>- Lx: box length in x direction</div> <div>- Ly: box length in y direction</div> <div>- Lz: box length in z direction</div> <div>- Density: mass density</div> <div>- c_RG: radius of gyration</div> <div>- c_RG[1]: squared radius of gyration tensor (xx component)</div> <div>- c_RG[2]: squared radius of gyration tensor (yy component)</div> <div>- c_RG[3]: squared radius of gyration tensor (zz component)</div> <div>- c_RG[4]: squared radius of gyration tensor (xy component)</div> <div>- c_RG[5]: squared radius of gyration tensor (xz component)</div> <div>- c_RG[6]: squared radius of gyration tensor (yz component)</div> <div>- c_bondave[1]: bond energy averaged over all atoms</div> <div>- c_bondave[2]: bond distance averaged over all atoms</div> <div>- c_bondave[3]: squared bond distance averaged over all atoms</div> <div>- c_angleave[1]: angle energy averaged over all atoms</div> <div>- c_angleave[2]: angle averaged over all atoms degree</div> <div>- c_angleave[3]: cosine of angle</div> <div>- c_angleave[4]: squared cosine of angle</div> <div>- c_MSD[1]: mean squared displacement x-direction</div> <div>- c_MSD[2]: mean squared displacement y-direction</div> <div>- c_MSD[3]: mean squared displacement z-direction</div> <div>- c_MSD[4]: total mean squared displacement</div> <div>- c_COM[1]: x coordinate of center of mass</div> <div>- c_COM[2]: y coordinate of center of mass</div> <div>- c_COM[3]: z coordinate of center of mass</div> <div>- v_strain_xx: xx component of engineering strain tensor </div> <div>- v_strain_yy: yy component of engineering strain tensor </div> <div>- v_strain_zz: zz component of engineering strain tensor </div> <div>- v_vMisesequivstress: von Mises equivalent stress</div> <div>- v_Piola_xx: xx component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yy: yy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_zz: zz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xy: xy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xz: xz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yz: yz component of the virial stress tensor normalized by the initial volume</div> <div>- v_strain_xy: xy component of engineering strain tensor </div> <div>- v_strain_xz: xz component of engineering strain tensor </div> <div>- v_strain_yz: yz component of engineering strain tensor </div> <div> </div> <div> </div> <div><strong>References:</strong></div> <div>[1] F. Weber, V. Dötschel, P. Steinmann, S. Pfaller, M. Ries, "Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics", Engineering Fracture Mechanics, vol. 307, p. 110270, 2024.</div> <div>[2] S. Plimpton, "Fast parallel algorithms for short-range molecular dynamics", Journal of computational physics, vol. 117, no. 1, pp. 1-19, 1995.</div> <div>[3] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales", Computer Physics Communications, vol. 271, p. 108171, 2022.</div> <div>[4] V. Dötschel, S. Pfaller, and M. Ries, "Studying the mechanical behavior of a generic thermoplastic by means of a fast coarse-grained molecular dynamics model", Polymers and Polymer Composites, vol. 31, pp. 1–11, 2023.</div> <div>[5] M. Ries, V. Dötschel, J. Seibert, and S. Pfaller, A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites, Zenodo, 2022, https://doi.org/10.5281/zenodo.6245699.</div> <div>[6] The MathWorks, Inc., "Matlab. the language of technical computing", https://de.mathworks.com/help/matlab/.</div> <div> </div> <div> </div> <div><strong>Funding:</strong></div> <div>The authors gratefully acknowledge funding by various sources:</div> <div>The overall research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/2-2023. Sebastian Pfaller is furthermore funded by the DFG projects 396414850 (Individual Research Grant 'Identifikation von Interphaseneigenschaften in Nanokompositen') and 505866713 together with the Agence nationale de la recherché (ANR, French Research Agency) – ANR-22-CE92-0049 (Individuel Research Grant 'BIO ART'). In addition, scientific support and HPC resources have been provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b136dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the DFG project 440719683.</div>
Evaluating Cloud Properties Across New Zealand: Ceilometer Network Data
<p><span>A network of 16 Vaisala CL31 ceilometers operated by MetService at airports across New Zealand had data available and were processed using ALCF with a cloud threshold mask of 6e-6. Nine sites were in Te Ika-a-Māui / North Island, six in Te Waipounamu / South Island and one site on Chatham Island. Metadata for the 16 sites are available in the Processed Ceilometer Medata.csv file.<br></span></p>
Applicability of new sustainable and efficient alginate-based composites for critical raw materials recovery: General composites fabrication optimization and adsorption performance evaluation
<p>This dataset contains the raw data for the publication "Applicability of new sustainable and efficient alginate-based composites for critical raw materials recovery: General composites fabrication optimization and adsorption performance evaluation" by Fila et al, published in Chemical Engineering Journal. The upload includes raw data of physicochemical characterizations of calcium alginate and its composites, i.e. alginate-biochar and alginate-clinoptilolite, including BET, SEM, TG, XPS and XRD analyses. </p>
The University of California, Riverside Environmental Chamber Data Base for Evaluating Oxidant Mechanism. Indoor Chamber Experiments through 1993
<strong><span>THE UNIVERSITY OF CALIFORNIA, RIVERSIDE ENVIRONMENTAL CHAMBER DATA BASE FOR EVALUATING OXIDANT MECHANISMS</span></strong> <p>William P. L. Carter, Dongmin Luo, Irina L. Malkina and Dennis Fitz</p> <p>Project Report for<br>Cooperative Agreement 815779, United States Environmental Protection Agency</p> <p>March 20, 1995</p> <p>This two report describes the data base of University of California, Riverside, environmental chamber experiments for use when evaluating photochemical mechanisms for urban and regional airshed models. This includes data obtained using the Statewide Air Pollution Research Center (SAPRC) Evacuable Chamber (EC), Indoor Teflon Chamber #1 (ITC), Indoor Teflon Chamber #2 (ETC), Dividable Teflon Chamber (DTC), and Xenon arc Teflon Chamber (XTC) between September of 1975 through November of 1993. This document provides backing information and data for that data set as well. This document lists and summarizes the experiments, summarizes the facility and procedures employed, documents the analytical and monitoring methods and their calibration data and associated uncertainties, assigns and documents the input data needed to conduct model simulations of the experiments in the present data base, and describes the format of the data sets which are distributed with this document on computer diskettes. Files are included in the distribution to permit modeling of the experiments in the present data base using the SAPRC-90 and the Carbon Bond IV chemical mechanisms, though a full mechanism evaluation procedure is beyond the scope of this report. Recommendations are made concerning the steps that need to be taken before using these data to evaluate chemical mechanisms.</p> <p>This report consists of two volumes. Volume 1 contains the main body of the text documenting the data base, and Volume 2 contains the three appendices. Appendix A contains printouts of spreadsheets containing summaries of the runs in the data base. Appendix B contains tabulations of the NOx and GC calibration data, which are too lengthy to include in the main body of the report. Appendix C describes how to install the distributed data files and software on a computer and how to conduct initial model simulations of the runs using the SAPRC modeling software and the SAPRC-90 and Carbon Bond IV mechanisms.</p>
An Evaluation of the Nigerian National Information Infrastructure and E-Governance
<p>Nigeria has the fastest growing and most lucrative ICT market in Africa (which fellow emerging economies like South Africa, India, Malaysia and Singapore are making the most of); yet in spite of this significant progress is still being ranked low in e-government provision to its citizens. The need for national information infrastructure cum e-governance framework as a national imperative for a 21st Century Nigeria cannot be overemphasized. National information infrastructure and e-governance policy engineering is important in achieving Vision 2020 and the mission of re-branding and transforming Nigeria. Unless the significance of e-governance is recognized and made the engine room of nation-building, economic and social developments aims in our country may become unattainable. Soonest, intellectual capital, not mineral resources like oil, would be the most strategic and valuable global resource. This paper therefore evaluates and recognizes with great concern that the current national information infrastructure and e-governance readiness framework in Nigeria is grossly inadequate-especially in the areas of National Power Generation and Supply (as the government fixes June 1st as the take-off of new electricity tariff). It highlights the bureaucracies and politics that hamper speedy development of our national information infrastructure and e-governance in comparison to what is obtainable in other evolving economies. The study investigates the limitations and deficiencies of our national information infrastructure and e-governance. The study examines areas of paucities which have contributed to the low e-service delivery in the country despite the success recorded in the country’s ICT and telecommunications sector and finally suggests how this situation may be improved for an enviable national information infrastructure and e-governance as a national imperative for 21st century Nigeria of our dream.</p>
Data from: Primer sets evaluation and sampling method assessment for the monitoring of fish communities in the North-western part of the Mediterranean Sea through eDNA metabarcoding
<p>Environmental DNA (eDNA) metabarcoding appears to be a promising tool for surveying fish communities. However, the effectiveness of this method relies on primer set performance and on a robust sampling strategy. While some studies have evaluated the efficiency of several primers for fish detection, it has not yet been assessed <em>in situ </em>for the Mediterranean Sea. In addition, mainly surface waters were sampled and no filter porosity testing was performed. In this pilot study, our aim was to evaluate the ability of six primer sets, targeting 12S rRNA (AcMDB07; MiFish; Tele04) or 16S rRNA (Fish16S; Fish16SFD; Vert16S) loci, to detect fish species in the Mediterranean Sea using a metabarcoding approach. We also assessed the influence of sampling depth and filter pore size (0.45 µm <em>versus</em> 5 µm filters). To achieve this, we developed a novel sampling strategy allowing simultaneous surface and bottom filtration of large water volumes along on-site the same transect. We found that 16S rRNA primer sets enabled more fish taxa to be detected across each taxonomic level. The best combination was Fish16S/Vert16S/AcMDB07, which recovered 95% of the 97 fish species detected in our study. There were highly significant differences in species composition between surface and bottom samples. Filters of 0.45 µm led to the detection of significantly more fish species. Therefore, to maximize fish detection in the studied area, we recommend to filter both surface and bottom waters through 0.45 µm filters and to use a combination of these three primer sets.</p>
Fig. 1 in Evaluation Of Winter Hardiness In Different Cultivated Tilia Taxa - Experience Of Some Most Valuable Dendrological Plantations In Central Latvia (Vidzeme) After Extremely Hard Winter In Year 2009/2010
Fig. 1. Location of inventoried dendrological objects in central part of Latvia.
Data used in: Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city
<p>This dataset contains the processed data and analysis code used in the article:</p> <div>Hall, T.W., Blunn, L., Grimmond, S., McCarroll, N., Merchant, C.J., Morrison, W., et al. (2024) Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city. <em>Quarterly Journal of the Royal Meteorological Society</em>, 150(760), 1771–1790. Available from: <div><a href="https://doi.org/10.1002/qj.4669">https://doi.org/10.1002/qj.4669</a></div> <div> </div> <div>The data consists of LST data, UM100 model output and ancillary files (all netCDF format).</div> <div> </div> <div><em>LST_data</em> contains:</div> </div> <ol> <li>Landsat LST data retrieved in this study (CALC) on four study days, LST data from FORTH and NASA JPL on two days</li> <li>MODIS LST data for 2018-07-15</li> </ol> <p><em>UM100_output</em> contains model output from initial and final runs for the four study days</p> <p>The python script <em>plot.py </em>can be used to generate the figures shown in this article. </p>
MineSegSAT: An automated system to evaluate mining disturbed area extents from Sentinel-2 imagery
<p>This dataset includes segmentation maps of environmentally impacted areas of mineral extraction sites in Canada and corresponding atmospherically corrected Sentinel-2 data from 2021. </p> <p>To look at an example of how to use the uncompressed assets, see this GitHub repository here: https://github.com/macdonaldezra/MineSegSAT. </p> <p>This dataset and code accompany the paper published at ECRS 2023 which can be found on the conference website at https://doi.org/10.3390/ECRS2023-16886 or at the Arxiv link http://arxiv.org/abs/2311.01676.</p>
Usable observations over Europe: Evaluation of compositing windows for landsat and sentinel-2 time series
<p>Landsat and Sentinel-2 data archives provide ever-increasing amounts of satellite data. However, the availability of usable observations greatly varies spatially and temporally. Pixel-based compositing that generates temporally equidistant cloud-free synthetic images can mitigate temporal variability, by constructing uninterrupted time series using different compositing windows. Here, we evaluated the feasibility of using compositing windows ranging from five days to one year for 1984-2021 Landsat and 2015-2021 Sentinel 2 time series to derive uninterrupted time series across Europe. We considered separate and joint use of both data archives and analyzed the spatio-temporal availability of composites during each calendar year and pixel-specific growing season across a variety of time windows and hypothesizing data interpolation. Our results demonstrated opportunities and limitations in the available data records to support medium- and long-term analyses requiring uninterrupted time series of composites with sub-annual temporal resolution. Spatial disparities across different compositing windows provide guidance on the feasibility of workflows relying on different data densities and on the challenges in wall-to-wall analyses. The feasibility of consistent time series based on composites with sub-monthly aggregation periods was mostly limited to the combined Landsat and Sentinel-2 archives after 2015, yet in some geographies requires interpolation of up to 50% of data.</p>
Data from: Evaluating the vulnerability of Tetracentron sinense habitats to climate-induced latitudinal shifts
<p><strong>Objective:</strong> Exploring the changing process of the geographical distribution pattern of <em>Tetracentron sinense</em> Oliv. and its main influencing factors since the last interglacial period can provide a scientific basis for the effective protection and management of the species.</p> <p><strong>Methods: </strong>The MaxEnt model was used to construct the potential distribution areas of <em>T. sinense</em> in different periods such as the last interglacial (LIG), the last glacial maximum (LGM), the Mid-Holocene (MID), the current and future (2050s, 2070s). On the premise of discussing the influence of dominant environmental factors on its distribution model, the suitable area changes of <em>T. sinense</em> under different ecological climate situations were quantitatively analyzed.</p> <p><strong>Results:</strong> (1) The AUC and TSS values predicted by the optimized model were 0.959 and 0.835, respectively, indicating a good predictive effect by the MaxEnt model; the potential suitable areas for <em>T. sinense</em> in the current period are mainly located in southwest China, which are wider compared to the actual habitats. (2) Jackknife testing showed that the lowest temperature in the coldest month (Bio6), elevation (Elev), seasonal variation coefficient of temperature (Bio4) and surface calcium carbonate content (T-CACO3) are the dominant environmental factors affecting the distribution of <em>T. sinense</em>. (3) From the last interglacial period to the current period, the total suitable area of <em>T. sinense</em> showed a decreasing trend; the distribution points of <em>T. sinense</em> populations in Mid-Holocene period may be the origin of the postglacial population, and Southwest China may be its glacial biological refuge. (4) Compared with the current period, the total suitable area ranges of <em>T. sinense</em> in China in the 2050s and 2070s decreased, and the centroid location of its total fitness area all migrated to the northwest, with the largest migration distance in 2070s under the SSPs 7.0 climate scenario.</p> <p><strong>Conclusion:</strong> Temperature was the principal factor influencing the geographical distribution of <em>T. sinense</em>. With the global warming, the range of <em>T. sinense</em> suitable areas will show a shrinking trend, with a shift towards higher-latitude regions. Ex-situ conservation measures could be taken to preserve its germplasm resources.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.