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Mars ISSM simulation output
<p>These are the videos and plots output from Simulation 38 for the CO2 glacier project</p>
United States Environmentally-Extended Input-Output (USEEIO) Modeling Framework
<p>This is a snapshot of the <a href="https://github.com/USEPA/useeio">USEEIO modeling framework</a> repository that is compilation of a README, versioning scheme, and a python script supplemented with a to convert a USEEIO model generated from <a href="https://github.com/USEPA/useeior">useeior </a>in the API format into openLCA schema JSON-LD files. This is not source code for generating the USEEIO v2.0 model.</p>
AGCM output for "Effects of subseasonal variation in the East Asian monsoon system on the summertime heat wave in western North America in 2021"
<p>The file named "EXP_2021ANO_heat.nc" is the anomalous heating used in "EXP_2021ANO". The file of "EXP_2021ANO_output.nc" is simulated largescale fields including temperature, zonal and meridional winds and geopotential height at 5 sigma levels integrated for 121 days.</p> <p>The files named "EXP_2021ANO_ens*_heat.nc" and "EXP_2021ANO_ens*_output.nc" are the anomalous heating and simulated largescale fields from five ensemble members of "EXP_2021ANO" experiment, respectively. </p> <p>The files named "EXP_2021CL_*.nc", "EXP_2021HT_*.nc", and "EXP_CLIMJET_*.nc" are same with "EXP_2021ANO_*.nc", but for the results for sensitive experiemts of "EXP_2021CL", "EXP_2021HT" and "EXP_CLIMJET".</p>
COSP output for the TWPICE and KWAJEX case with varying cloud subgrid-scale parameters
<p>The control experiment is named SCG_LIN, in which the domain-averaged cloud fraction and condensate from CRM_LIN are used, along with median values of <em>L</em><sub>cf</sub>, <em>L</em><sub>cw</sub> and <em>v</em>. Two sensitivity experiments were carried out for each parameter using either lower or upper quartiles, while keeping the other two parameters unchanged. </p> <table> <tbody> <tr> <td> <p>CRM_LIN</p> </td> <td> <p>CRM output with LIN microphysics</p> </td> </tr> <tr> <td> <p>CRM_SAM</p> </td> <td> <p>CRM output with SAM1MOM microphysics</p> </td> </tr> <tr> <td> <p>CRM_M2005</p> </td> <td> <p>CRM output with M2005 microphysics</p> </td> </tr> <tr> <td> <p>SCG_LIN</p> </td> <td> <p>SCG output based on domain-averaged clouds from CRM_LIN and median subgrid-scale parameters (<em>L</em><sub>cf </sub>= 1.6, <em>L</em><sub>cw </sub>= 0.9, <em>v </em>= 2)</p> </td> </tr> <tr> <td> <p>SCG_SAM</p> </td> <td> <p>Same as SCG_LIN but based on clouds from CRM_SAM</p> </td> </tr> <tr> <td> <p>SCG_M2005</p> </td> <td> <p>Same as SCG_LIN but based on clouds from CRM_M2005</p> </td> </tr> <tr> <td> <p>SCG_Exact</p> </td> <td> <p>SCG output using exact parameters derived from CRM_LIN</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cf</sub>(3.2)</p> </td> <td> <p>Same as SCG_LIN but for <em>L</em><sub>cf </sub>= 3.2 in cloud overlap</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cf</sub>(0.9)</p> </td> <td> <p>Same as SCG_<em> L</em><sub>cf</sub>(3.2) but for <em>L</em><sub>cf </sub>= 0.9</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cw</sub>(1.5)</p> </td> <td> <p>Same as SCG_LIN but for <em>L</em><sub>cw </sub>= 1.5 in condensate overlap</p> </td> </tr> <tr> <td> <p>SCG_<em>L</em><sub>cw</sub>(0.5)</p> </td> <td> <table> <tbody> <tr> <td> <p>Same as SCG_<em> L</em><sub>cw</sub>(1.5) but for <em>L</em><sub>cw </sub>= 0.5</p> </td> </tr> </tbody> </table> <p> </p> </td> </tr> <tr> <td> <p>SCG_<em>v</em>(0.8)</p> </td> <td> <p>Same as SCG_LIN but for <em>v </em>= 0.8</p> </td> </tr> <tr> <td> <p>SCG_<em>v</em>(5.9)</p> </td> <td> <p>Same as SCG_<em>v</em>(0.8) but for <em>v </em>= 5.9</p> </td> </tr> </tbody> </table>
MITgcm model setup and output for "What determines the shape of the Pine-Island-like ice shelf?"
<p><strong>(Contents)</strong><br> Here it contains<br> <br> jim_run3 = CTRL (Similar to Jordan et al., 2018 but with smaller ocean)</p> <p>melt/run/ = IOCTRL, M(all)V(dyn)U(dyn)</p> <p>melt2/ = M( changing, see below ) V( dyn ) U( 0 )<br> melt2/run/ = M(all)V(dyn)U(0)<br> melt2/run2/ = M(20)V(dyn)U(0)<br> melt2/run3/ = M(GL10)V(dyn)U(0)<br> melt2/run4/ = M(GL20)V(dyn)U(0)</p> <p><br> melt3/ = M ( changing, see below ) V( changing ) U( 0 )<br> melt3/run5/ = M(all)V(2000)U(0)<br> melt3/run10/ = M(20)V(2000)U(0)</p> <p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> Input melt data for running ice only experiments<br> #data.streamice#<br> meltCTRL.bin = M(all) ... melt rate data of CTRL for every time steps <br> meltCTRL2.bin = M(GL10)<br> meltCTRL3.bin = M(20 m/a)<br> meltCTRL4.bin = M(GL20)</p> <p>Input velocity data for running ice only experiments <br> melt3/<br> uvel_ext4.bin = U(0 m/a)<br> vvel_ext5.bin = V(2000 m/a)</p> <p><strong>(How to compile and run)</strong><br> mkdir build<br> cd build/<br> module load intel/2021.2.0<br> module load impi/2021.2.0<br> export LANG=en_US.UTF-8<br> export LC_ALL=en_US.utf8<br> ../../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas_tokyo3 -mpi -mods ../code/<br> make depend<br> export LANG=en_US.UTF-8<br> export LC_ALL=en_US.utf8<br> make -j 16</p> <p> mkdir ../run<br> cd ../run/<br> cp ../build/mitgcmuv .<br> cp ../input/* .<br> pjsub job*.pbs<br> <br> <strong>Other important links</strong><br> https://github.com/hgu784/MITgcm_67s<br> <br> For more info please send an email to Yoshihiro.Nakayama@lowtem.hokudai.ac.jp</p>
What is a Good School, and Can Parents Tell? Evidence on the Multidimensionality of School Output
<p>The package contains all the code necessary to reproduce the figures and tables in Beuermann, Jackson, Navarro-Sola and Pardo (forthcoming). "What is a Good School, and Can Parents Tell? Evidence on the Multidimensionality of School Output." Review of Economic Studies. Detailed instructions are also given about requesting access to the raw data.</p>
Blast output from: Lost in dead wood? Environmental DNA sequencing from dead wood shows little signs of saproxylic beetles
<p>eDNA metabarcoding has become a standard method for assessing wood-inhabiting fungi and bacteria, yet determination of dead-wood-inhabiting beetles still relies on time-consuming collection of beetle specimens. We thus tested whether beetle species can be identified by eDNA sequencing of wood in a mesocosm experiment that manipulated species assemblages. Dead wood samples were taken at exit holes of beetles and DNA was extracted and analyzed using two comparative methods: (i) metabarcoding with standard arthropod primers (421 bp) and (ii) using short species-specific primers (120-264 bp) with Sanger sequencing. Results showed that beetle DNA was amplified by each of the two approaches, however, with (i) we detected only one non-target saproxylic beetle species. In addition, we identified 80 different OTUs with four non-targeted species of arthropods. For (ii) we detected the targeted species in two fresh beetle exit holes out of 20 samples. We suggest that, in contrast to fungi and bacteria, this eDNA metabarcoding approach is not able to reliably detect saproxylic beetles from wood samples, likely due to rapid degradation of their target DNA. Adapting such an approach for large scale analyses thus requires a better knowledge of degradation processes affecting DNA quality and quantity in wood.</p>
Outputs simulated by E3SM for 2017 pyroCb study
<p>This dataset includs extinction coefficient from all ensumble study simulated from E3SMv1. </p>
AWI-CPS analysis and forecast output
<p>The dataset contains the analysis and sea ice forecast results from the Alfred Wegener Institute Coupled Prediction System (AWI-CPS). Note that due to the large ensemble used (30) in the AWI-CPS, the output data is generated with only monthly mean data. One who has further interest in the daily output could reach the authors through emails.</p> <p>AWI-CPS.DA.tar.gz has both ocean and sea ice variables, where the variable has a suffix of 'f' means forecast, 'a' stands for analysis, and 'ini' stands for initialized states. </p> <p>AWI-CPS.FCST.tar.gz contains the sea ice forecasts for different lead times initialized in January, April, July and October. The axis 'fcst' for each variable actually represents forecasts with 4 different lead times of L0-2, L3-5, L6-8, L9-11. For example, L0-2 indicates forecasts with lead times of 0, 1, and 2 months, and so forth for other lead times. Both sea ice probability and sea ice concentration before and after the calibration are given in the data file. Specifically, SIP_FCST_CORR is for sea ice probability after calibration, while SIP_FCST_RAW is the raw forecast; SIC_Cal is after calibration, while SIC_RAW is the raw forecast. </p> <p>To obtain the mesh file for FESOM2 and visualize the data, one is referred to <a href="https://doi.org/10.5281/zenodo.6335530"> https://doi.org/10.5281/zenodo.6335530</a>.</p>
The input files and processed output data for the OSSEs using particle filter for the forecast of cloud and precipitation
<p>DART_input.tar.gz contains the input files for the DART system for the control run, Exp-1 ~ Exp-5.</p> <p>WRF&WRS_namelist.tar.gz contains the input namelist files for the WPS and WRF model for the nature run, the control run, and Exp-1 ~ Exp-5.</p> <p>cloud_data.tar.gz contains the procssed output data for the WRF/DART system corresponding to the nature run, the control run, and Exp-1 ~ Exp-5. Untarring the file will generate several subdirectories named after the UTC time. For example, 202008200200 denotes the results for at 02:00 UTC, 20 August, 2020. Because the data for all times are quite large (~10G), we only uploaded the data at 02:00 UTC, 20 August, 2020, 10:30 UTC, 20 August, 2020, 19:00 UTC, 20 August, 2020, 03:30 UTC, 21 August, 2020, and 12:00 UTC, 21 August, 2020. Each subdirectory contains preassim_mean.nc and postassim_mean.nc, which mean the posterior and prior estimate of atmosphere state variables and other processed variables. Either preassim_mean.nc or postassim_mean.nc contains the cloud water path (CWP, unit:kgm-2), cloud water content (CWC, which is the sum of the mixing ratio of six cloud hydrometeors, unit: kgkg-1), RE_CLOUD(unit:um), RE_ICE(unit:um), QVAPOR(unit:kgkg-1), T(the perturbation of potential temperature, unit:K).</p> <p>rain_rate.tar.gz contains the procssed output data for the rain rate corresponding to the nature run, the control run, and Exp-4 ~ Exp-5.</p>
Niche differentiation in the bank vole: Maxent input and output data
<p>Species-level environmental niche modelling has been crucial in efforts to understand how species respond to climate variation and change. However, species often exhibit local adaptation and intraspecific niche differences that may be important to consider in predicting responses to climate. Here, we explore if phylogeographic lineages of the bank vole originating from different glacial refugia (Carpathian, Western, Eastern and Southern) show niche differentiation, which would suggest a role for local adaptation in biogeography of this widespread Eurasian small mammal. We first model the environmental requirements for the bank vole using species-wide occurrences (210 filtered records) and then model each lineage separately to examine niche overlap and test for niche differentiation in geographical and environmental space. We then use the models to estimate past [Last Glacial Maximum (LGM) and mid-Holocene] habitat suitability to compare to previously hypothesized glacial refugia for this species. Environmental niches are statistically significantly different from each other for all pairs of lineages in geographical as well as environmental space and these differences cannot be explained by habitat availability within their respective ranges. Together with the inability of most of the lineages to correctly predict the distributions of other lineages, these result support intraspecific ecological differentiation in the bank vole. Model projections of habitat suitability during the LGM support glacial survival of the bank vole in the Mediterranean region as well as in central and western Europe. Niche differences between lineages and the resulting spatial segregation of habitat suitability suggest ecological differentiation has played a role in determining the present phylogeographic patterns in the bank vole. Our study illustrates that models pooling lineages within a species may obscure the potential for different response to climate change among populations.</p>
Outputs of the Jupyter Notebook - Cosmos-UK soil moisture
<p>The dataset contains the outputs of the notebook "Cosmos-UK soil moisture" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> <li> <p>Doran Khamis (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/dorankhamis">@dorankhamis</a></p> </li> <li> <p>Matt Fry (reviewer), UK Centre for Ecology & Hydrology, <a href="https://github.com/mattfry-ceh">@mattfry-ceh</a></p> </li> </ul> <p><em>Dataset originator/creator</em></p> <ul> <li> <p>UK Centre for Ecology & Hydrology (creator)</p> </li> <li> <p>Natural Environment Research Council (support)</p> </li> </ul> <p><em>Dataset reference and documentation</em></p> <ul> <li> <p>S. Stanley, V. Antoniou, A. Askquith-Ellis, L.A. Ball, E.S. Bennett, J.R. Blake, D.B. Boorman, M. Brooks, M. Clarke, H.M. Cooper, N. Cowan, A. Cumming, J.G. Evans, P. Farrand, M. Fry, O.E. Hitt, W.D. Lord, R. Morrison, G.V. Nash, D. Rylett, P.M. Scarlett, O.D. Swain, M. Szczykulska, J.L. Thornton, E.J. Trill, A.C. Warwick, and B. Winterbourn. Daily and sub-daily hydrometeorological and soil data (2013-2019) [cosmos-uk]. 2021. URL: <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>, <a href="https://doi.org/10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185">doi:10.5285/b5c190e4-e35d-40ea-8fbe-598da03a1185</a>.</p> </li> </ul> <p><strong>Further references</strong></p> <ul> <li> <p>Jonathan G. Evans, H. C. Ward, J. R. Blake, E. J. Hewitt, R. Morrison, M. Fry, L. A. Ball, L. C. Doughty, J. W. Libre, O. E. Hitt, D. Rylett, R. J. Ellis, A. C. Warwick, M. Brooks, M. A. Parkes, G. M.H. Wright, A. C. Singer, D. B. Boorman, and A. Jenkins. Soil water content in southern england derived from a cosmic-ray soil moisture observing system – cosmos-uk. <em>Hydrological Processes</em>, 30:4987–4999, 12 2016. <a href="https://doi.org/10.1002/hyp.10929">doi:10.1002/hyp.10929</a>.</p> </li> <li> <p>M. Zreda, W. J. Shuttleworth, X. Zeng, C. Zweck, D. Desilets, T. Franz, and R. Rosolem. Cosmos: the cosmic-ray soil moisture observing system. <em>Hydrology and Earth System Sciences</em>, 16(11):4079–4099, 2012. URL: <a href="https://hess.copernicus.org/articles/16/4079/2012/">https://hess.copernicus.org/articles/16/4079/2012/</a>, <a href="https://doi.org/10.5194/hess-16-4079-2012">doi:10.5194/hess-16-4079-2012</a>.</p> </li> </ul>
Model outputs and observation data for "Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)"
<p>Model outputs and observation data for paper "Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)".</p>
Selected HRRRv4 model output and plotting scripts for 'Evaluation of a cloudy cold-air pool in the Columbia River Basin in different versions of the HRRR model'
<p>This zip file contains selected model output data from HRRRv4 and plotting scripts used for the paper 'Evaluation of a cloudy cold-air pool in the Columbia River Basin in different versions of the HRRR model' which is submitted for publication to the journal Geoscientific Model Development (GMD).</p> <p>The individual files are:</p> <p>d01_d02_lwd_allsurfacestations_v4fp1_v4fp2.nc: longwave downward radiation flux at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_Wasco_v4fp1_v4fp2.nc: liquid water path at Wasco for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_allsurfacestations_v4fp1_v4fp2.nc: liquid water path at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_gridpointsbelow500mMSL_v4fp1_v4fp2.nc: liquid water path at all grid points in the Columbia River Basin with a terrain height of less than 500 m for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_lwp_numbergridpointsbelow500mMSL_v4fp1_v4fp2.nc: Number of gridpoints in the Columbia River Basin with a terrain height of less than 500 m MSL and a LWP larger than 10 g/m2 for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_swd_allsurfacestations_v4fp1_v4fp2.nc: shortwave downward radiation flux at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_t2m_allsurfacestations_v4fp1_v4fp2.nc: 2-m temperature at the locations of surface stations in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_d02_windspeed_7sites_v4fp1_v4fp2.nc: Wind speed profiles at 7 sites in the investigation area for domains d01 and d02 and runs v4fp1 and v4fp2<br> d01_temperatureprofiles_Wasco_v4fp1.nc: temperature profiles at Wasco for domain d01 and run v4fp1<br> d01_temperatureprofiles_Wasco_v4fp2.nc: temperature profiles at Wasco for domain d01 and run v4fp2<br> d02_swd_spatial_v4fp1.nc: spatial distribution of surface shortwave downward radiation for domain d02 and run v4fp1<br> d02_temperatureprofiles_Wasco_v4fp1.nc: temperature profiles at Wasco for domain d02 and run v4fp1<br> d02_temperatureprofiles_Wasco_v4fp2.nc: temperature profiles at Wasco for domain d02 and run v4fp2<br> d02_terrain.nc: terrain height for domain d02<br> plot_miscs_hrrr_longforecasts.py: master script for plotting<br> plot_routines_wfip2_lf.py: plot routines for various type of plots<br> read_routines_wfip2_lf.py: read routines for all kinds of data<br> sites_locations_wfip2.txt: latitude, longitude and height of used stations<br> basic_functions.py: helper functions for plotting</p>
Datasets and model outputs used for validating WASP for SURFEX v8.0
<p>These datasets contain the model outputs used for validating the WASP SURFEX v8.0 parameterization of turbulent fluxes at sea. The file LION_BUOY.tar contains (ascii format) the tubrulent fluxes computed from the surface parameters recorded at the Météo-France LION buoy (in the centre of the Gulf of Lion, NW Mediterranean Sea) at an hourly frequency between 2001 and 2014, using the bulk softwares COARE 3.0 with various wave representation and WASP with waves. The file AROME_MED.tar contains the outputs of the AROME operational model with ECUME or WASP representation of the turbulent fluxes, on the case study of October 2016, that were used to validate the parameterization as presented in the paper. The file AROME_OI.tar contains the outputs of AROME OI on the case of the tropical cyclone Batsirai in the Indian Ocean, in February 2022. The file ARPEGE_CLIM_AMIP contains the outputs of the low-resolution, AMIP runs performed using the ECUME and WASP parameterization.</p>
yarlagadda_etal_2022_trade_mitigation Output Data
<p>GCAM output databases for the paper: Yarlagadda, B., T. Wild, X. Zhao, L. Clarke, R. Cui, Z. Khan, A. Birnbaum, and J. Lamontagne. "Trade and Climate Mitigation Interactions Create Agro-Economic Opportunities with Social and Environmental Trade-offs in Latin America and the Caribbean" (under review).</p>
Forcing data, evaluation data, model output and analysis scripts used in LPJ-GUESS/LSM description paper
<p>This archive contains:</p> <p>- model_output.tar.gz: Model output<br> - extracted_fluxes.tar.gz: Sensible heat, latent heat and CO2 fluxes extracted from the FLUXNET2015 dataset, used to evaluate the model output<br> - extracted_climate.tar.gz: Climate data extracted from the FLUXNET2015 dataset, used to force the simulations<br> - scripts.tar.gz: Python scripts used to analyze the data and produce the results reported in the model description paper</p> <p>The climate forcing data has been extracted from the FLUXNET2015 dataset [1]. Input and output data is stored in netCDF files. Evaluation data is stored in python serialized files (pickle).</p> <p>[1] Pastorello, G., Trotta, C., Canfora, E. <em>et al.</em> The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. <em>Sci Data</em> <strong>7, </strong>225 (2020). https://doi.org/10.1038/s41597-020-0534-3</p>
Input data and model output for study about wind changes and impact on the Subtropical Front
<p>This dataset contains:</p> <p>Model data for the CONTROL simulation (CONTROL.gz)</p> <p>Model data for the SHIFT simulation (SHIFT.gz) where the westerly winds have been shifted by 1degree per decade</p> <p>Model data for the INCREASE simulation (INCREASE.gz) where the westerly winds have been incresaed by 1 percent per decade</p> <p>Reference dataset are provided (Argo.gz and Modiz.gz)</p>
Specialization indexes of countries for 254 subject categories, by input and by output
<p>This is a Supplementary Material of the manuscript "<strong>Measuring and interpreting the differences of the nations’ scientific specialization indexes by output and by input</strong>". The file contains complete data on 199 countries in 254 subject categories.</p>
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925). in Muridae
Distribution. Now restricted to the Channel Country of SW Queensland and the Lake Eyre Basin in NE South Australia. Descriptive notes. Head-body 95-120 mm, tail 105-160 mm, ear 23-29 mm, hindfoot 32-37 mm; weight 30-50 g. The Fawn Hopping Mouse has body form typical of hopping mice, with very long hindfeet, long tail with distal brush of longer hairs, very long ears, and large protruberant eyes. Dorsal fur is of variable color, from pale pinkish fawn to gray; ventral fur white. Unlike most other hopping mice, it has no throat pouch, but males have a glandular area of naked skin on the chest. Habitat. Occurs in low shrublands and tussock grasslands on stony ("gibber") plains and claypans. Shows marked habitat segregation from the Dusky Hopping Mouse (N. fuscus), which is closely associated with sandy substrates. Food and Feeding. The Fawn Hopping Mouse is mostly granivorous, but also eats other plant material (stems, leaves) and occasionally invertebrates. It uses succulent, salt-adapted plants around edges of claypans as a source of water. Breeding. Reproduction is probably largely opportunistic and aseasonal, with high reproductive output from near-continuous breeding after periods of high rainfall; reported littersize is 1-5, most commonly three; gestation period 38-43 days for nonlactating females. Females may mature later than other hopping mice, with reproductive maturity reached at about six months. Activity patterns. Terrestrial and nocturnal. Fawn Hopping Mice shelter during day in burrow systems that are typically simpler and shallower than those of other hopping mice. Movements, Home range and Social organization. Fawn Hopping Mice generally live singly or in small groups; typically uncommon within range, but population density may increase by an order of magnitude following periods of high rainfall. Status and Conservation. Classified as Near Threatened on The IUCN Red List. The Fawn Hopping Mouse has shown marked decline in range (estimated at greater than 50%), and presumably population size, since European settlement of Australia. This is mostlikely due to predation by the introduced house cat and Red Fox (Vulpes vulpes), and to habitat degradation associated with pastoralism. Bibliography. Brazenor (1934), Burbidge et al. (2008), Finlayson (1939), Gould (1853), Jackson & Groves (2015), Murray et al. (1999), Ogilby (1892), Thomas (1921h), Van Dyck & Strahan (2008), Waite (1898), Watts & Aslin (1981), Woinarski et al. (2014), Wood Jones (1925).
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.