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SGS-LTER GIS layer with detailed information on Meteorological Stations on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on pasture boundaries on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on Public Land Survey on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on Roads on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer of Level 2 Soil Survey and Related Document on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on Streams on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on study sites on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on IBP Vegetation on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on Pasture Treatment on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on CPER Boundary on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on Buildings on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on Fences on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on Lakes on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on Landforms on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
SGS-LTER GIS layer with detailed information on Exclosures on Central Plains Experimental Range, Nunn, Colorado, USA 2012
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. No Abstract Available
Fig 2 in Significant range expansion for the rove beetle genus Deleaster, based on a new species from Papua New Guinea (Coleoptera: Staphylinidae: Oxytelinae)
Fig 2. Distribution of extant Deleaster Erichson, 1839.
ClepsHresEns-HbvRhein134-SbkReRhein Medium Range Waterlevel Ensemble Forecasts for Waterway Rhine
<p>The datasets provided here were produced as part of the IMPREX project for work package 9, task 3 “<em>Case studies</em>”. Analysis of the datasets are published in Deliverable 9.2 “<em>Framework for the assessment of forecast quality and value in the navigation sector</em>“(Klein & Meißner 2017) and Deliverable 9.4 “<em>Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning</em>“ (Klein & Meißner 2019). The aim of the dataset was to apply the statistical post-processing method Ensemble Model Output Statistics EMOS (Gneiting et al. 2005) to estimate the predictive uncertainty of the waterlevel ensemble forecasts, in order to provide probabilistic water level forecasts to the end users (Klein & Meißner 2019).</p> <p>Meteorological forcing data used to calculate water-level forecasts with an extended forecast horizon are based on a 68 member multi-model ensemble: 51 ensemble members from ECMWF ENS (1 control forecast and 50 perturbed members) as well as the control forecast ECMWF HRES with a higher spatial resolution (Leutbecher & Palmer 2008, Owens & Hewson 2018), and the 16 members of the limited-area ensemble prediction system by the consortium for small-scale modelling COSMO LEPS (Montani et al. 2011, Marsigli et al. 2014). Archived meteorological real-time forecasts of the period January 2008 to December 2015 have been used to produce this comprehensive water level re-forecast data set.</p> <p>The conceptual, semi-distributed rainfall-runoff model HBV-96 (Bergström 1995, Lindstrom et al. 1997) is applied to calculate the flow forecasts used as boundary conditions and lateral inflows of the hydrodynamic model SOBEK (Deltares 2012) used to calculate water level forecasts along the river Rhine. The river Rhine basin is divided into 134 subbasins which are further subdivided into hydrological response units (HRU) according to land use and elevation classes. The flow formation processes are calculated on those HRUs. The model calculates flow with a temporal resolution of 1 h using temperature and precipitation fields that have been interpolated over the subbasins as meteorological input.</p> <p>The hydrodynamic model suite SOBEK is used as one-dimensional model, which uses cross-section information of the River Rhine as well as its main tributaries. The distance between the cross-sections, which cover the river bathymetry as well as its floodplains, is non-equidistant and ranges between 100 m and 800 m. As the main tributaries of the River Rhine are impounded rivers (e.g. Moselle, Main) the SOBEK-model includes several weirs with their specific control rules in order to simulate the real behaviour of these elements, too.</p> <p>The flow and water level forecasts were initialized each day at 06:00 UTC, which means that observed real-time meteorological data, interpolated to the subbasins of the hydrological model, up to the forecast date were used as forcings of the hydrological model and observed flow was used as input for the hydrodynamic model to initialize the model states. For the forecast period meteorological ensemble runs from the different Numerical Weather Prediction (NWP) models interpolated to the subbasins were used as forcings of the hydrological model. Flow forecasts of the large tributaries of the river Rhine simulated with HBV were then used as input for the hydrodynamic model. To reduce the error of the input to the hydrodynamic model autoregressive error correction models (Broersen & Weerts 2005) was applied using the differences between the simulation of the model using meteorological observations as forcings and the actually past flow observations as training data. This error correction reduces the error of the hydrological model at the forecast initialization time to zero. To reduce the error of the waterlevel forecasts obtained by running the hydrodynamic model, again autoregressive error correction models were applied using the differences between the water level simulation using observed flow as input and the water-level observations of the past.</p> <p><strong>Dataset H_OBS_RHINE.nc:</strong></p> <p>Hourly observed water levels of the gauges Kaub, Koeln, Ruhrort / Rhine for the period 2008–2016 stored as variable <em><strong>h_obs(time=78912, stations=3).</strong></em></p> <p>Data originate from the database of gauge measurements of the Federal Waterways and Shipping Administration (WSV). These data were quality checked and published by the gauge-operating WSV offices. Nevertheless, data errors and inconsistencies cannot be ruled out completely, so that neither the WSV nor the BfG do accept any liability for the correctness and completeness of the data. Data source: "German Federal Waterways and Shipping Administration (WSV)", provided by the German Federal Institute of Hydrology (BfG).</p> <pre><code>float h_obs(time=78912, stations=3); :units = "cm"; :_FillValue = -9999.0f; // float :long_name = "observed waterlevel"; :coordinates = "lat lon";</code></pre> <p><strong>Dataset H_MM_HBV134_SOBEK.nc</strong></p> <p>Hourly forecasted water level of the hydrodynamic mode SOBEK forced by flow forecasts of the hydrological model HBV134 forced by a multi-model meteorological ensemble. Daily forecasts initialized at 06:00 UTC of the period 2008-01-01 to 2015-12-31 with a lead time of 240 hours. Gauges Kaub, Koeln, Ruhrort / Rhine.</p> <p>Forecast values are stored in the variable <em><strong>h_fcast_ens(time=2869, lead_time=241, realization=68, stations=3)</strong></em>, first dimension forecast dates, second dimension lead time, third dimension realization, fourth dimension station. ECMWF-HRES first realization, COSMO-LEPS realization 2 – 17, ECMWF-ENS realization 18- 68.</p> <pre><code>float h_fcast_ens(time=2869, lead_time=241, realization=68, stations=3); :_FillValue = -9999.0f; // float :long_name = "forecast waterlevel ensemble"; :units = "cm"; :coordinates = "lat lon";</code></pre> <p><strong>Literature</strong></p> <p>Bergström, S. (1995): The HBV model. In: V. P. Singh (Ed.): Computer models of watershed hydrology. Water Resources Publications, Colorado, USA, 443-476</p> <p>Broersen, P. & A. Weerts (2005): Automatic Error Correction of Rainfall-Runoff models in Flood Forecasting Systems. Conference Proceedings: IMTC 2005 – Instrumentation and Measurement Technology Conference, Ottawa, Canada, 17-19 May 2005.</p> <p>Deltares (2012): Technical Reference SOBEK-RE. Deltares, Delft, The Netherlands</p> <p>Gneiting, T., A. E. Raftery, A. H. Westveld & T. Goldman (2005): Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review 133(5), 1098-1118</p> <p>Klein, B. & D. Meissner (2017): Framework for the assessment of forecast quality and value in the navigation sector. Deliverable 9.2, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="http://www.imprex.eu/system/files/generated/files/resource/d9-2-imprex-v2-0.pdf">http://www.imprex.eu/system/files/generated/files/resource/d9-2-imprex-v2-0.pdf</a></p> <p>Klein, B. & D. Meissner (2019): Semi-operational forecasting system for Rhine, Danube and Elbe to support improved transport cost planning. Deliverable 9.4, IMPREX - Improving Predictions of Hydrological Extremes - Grant Agreement Number 641811, <a href="https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf">https://imprex.eu/system/files/generated/files/resource/deliverable9-4-imprex-v1-0.pdf</a></p> <p>Leutbecher, M. & T. N. Palmer (2008): Ensemble forecasting. Journal of Computational Physics 227(7), 3515-3539</p> <p>Lindstrom, G., B. Johansson, M. Persson, M. Gardelin & S. Bergstrom (1997): Development and test of the distributed HBV-96 hydrological model. Journal of Hydrology 201(1-4), 272-288</p> <p>Marsigli, C., A. Montani & T. Paccagnella (2014): Perturbation of initial and boundary conditions for a limited-area ensemble: multi-model versus single-model approach. Quarterly Journal of the Royal Meteorological Society 140(678), 197-208</p> <p>Montani, A., D. Cesari, C. Marsigli & T. Paccagnella (2011): Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges. Tellus Series a-Dynamic Meteorology and Oceanography 63(3), 605-624</p> <p>Owens, R. & T. R. E. Hewson (2018): ECMWF Forecast User Guide. ECMWF, Reading, doi: 10.21957/m1cs7h</p>
Checklist of the mosses of mountain ranges of the Pechora-Ilych State Nature Reserve (Northern Urals, Basins of the Pechora and Ilych Rivers)
<p>The Checklist of the mosses of mountain ranges of the Pechora-Ilych State Nature Reserve (Northern Urals, Basins of the Pechora and Ilych Rivers) is presented. The Checklist includes 173 species of mosses from 78 genera and 33 families, which is almost half of the species diversity of mosses in the Pechora-Ilych State Nature Reserve (348 species ).</p>
TagSeq for gene expression in non-model plants: a pilot study at the Santa Rita Experimental Range NEON core site
<p>TagSeq analysis scripts and assembled transcriptomes for four vascular plant species from the Santa Rita Experimental Range, AZ. Transcriptomes for each species were sequenced and assembled as described below. Additional details available in the associated manuscript: MS LINK. Raw reads for each available at NCBI BioProject #PRJNA599443.</p> <p> </p> <p><strong>Taxon selection and sampling </strong></p> <p>This study focused on four commonly-occurring species at the Santa Rita Experimental Range Long Term Research and Core NEON site (SRER). These include the native species <em>Tidestromia</em> <em>lanuginosa</em> (Nutt.) Standl. (Amaranthaceae; ‘woolly tidestromia’), <em>Parkinsonia</em> <em>florida</em> (Benth. ex A. Gray) S. Watson. (Fabaceae; ‘blue palo verde’), and <em>Bouteloua</em> <em>aristidoides</em> (Kunth) Griseb. (Poaceae; ‘needle grama’), as well as the introduced species <em>Eragrostis</em> <em>lehmanniana</em> Nees (Poaceae; ‘Lehmann lovegrass’; native to southern Africa). All species were identified using a combination of the historical flora of the Santa Rita Experimental Range (Medina, 2003), the Arizona Flora (Kearney et al., 1960), and the Flora of North America (Flora of North America Editorial Committee, eds. 1993). Vouchers were deposited in the University of Arizona herbarium (ARIZ). Tissue from mature plants was collected from an apparently healthy individual representing each target species during the 2017 growing season. An entire stem was sampled for <em>B. aristidoides</em> (with flowers and fruits) and <em>E. lehmanniana</em> (without flowers or fruits). Leaves and leaflets only were sampled for <em>P. florida</em> and <em>T. lanuginosa</em>.</p> <p> </p> <p><strong>RNA extraction and RNA-seq</strong></p> <p>Total RNA was extracted from tissue using the Spectrum Plant Total RNA Kit (Sigma-Aldrich Co., St. Louis, MO, USA) following Protocol A. RNA was used to prepare cDNA using Nugen’s Ovation RNA-Seq System via single primer isothermal amplification (Catalogue # 7102-A01) and automated on the Apollo 324 liquid handler (Wafergen). cDNA was quantified on the Nanodrop (Thermo Fisher Scientific) and was sheared to approximately 300 bp fragments using the Covaris M220 ultrasonicator. Libraries were generated using Kapa Biosystem’s library preparation kit (KK8201). Fragments were end repaired and A-tailed, and individual indexes and adapters (Bioo, catalogue #520999) were ligated on each separate sample. The adapter ligated molecules were cleaned using AMPure beads (Agencourt Bioscience/Beckman Coulter, A63883), and amplified with Kapa’s HIFI enzyme (KK2502). Each library was then analyzed for fragment size on an Agilent’s Tapestation, and quantified by qPCR (KAPA Library Quantification Kit, KK4835) on Thermo Fisher Scientific’s Quantstudio 5 before multiplex pooling (13-16 samples per lane) and paired-end sequencing at 2x150 bp on the Illumina NextSeq500 platform at Arizona State University’s CLAS Genomics Core facility. Raw read quality was assessed using fastQC (Andrews, 2010).</p> <p> </p> <p><strong><em>De novo</em> transcriptome assembly</strong></p> <p>Raw sequence reads were processed using the SnoWhite pipeline (Barker et al., 2010a; Dlugosch et al., 2013), which included trimming adapter sequences and bases with a quality score below 20 from the 3' ends of all reads, removing reads that are entirely primer and/or adapter fragments using TagDust (Lassmann et al., 2009), and removing polyA/T tails with SeqClean (https://sourceforge.net/projects/seqclean/). All transcriptomes were assembled with SOAPdenovo-Trans v1.03 (Xie et al., 2014) using a k-mer of 57. Assembled sequences for each species are in the files ending ".scafSeq".</p> <p> </p> <p><strong>Protein Translations</strong></p> <p>We used TransPipe (Barker et al., 2010) to identify plant proteins within the assembled transcripts for each reference transcriptome and provide protein and in-frame nucleic acid sequences for each species. The reading frame and protein translation for each sequence was identified by comparison to protein sequences from 25 sequenced and annotated plant genomes from Phytozome (Goodstein et al., 2012). Using BLASTX (Wheeler et al., 2008), best hit proteins were paired with each gene at a minimum cutoff of 30% sequence similarity over at least 150 sites. Genes that did not have a best hit protein at this level were removed. To determine the reading frame and generate estimated amino acid sequences, each gene was aligned against its best hit protein by Genewise 2.2.2 (Birney et al., 2004). Based on the highest scoring Genewise DNA-protein alignments, stop and 'N' containing codons were removed to produce estimated amino acid sequences for each gene. Output included paired DNA and protein sequences with the DNA sequence reading frame corresponding to each protein sequence. Nucleic acid sequence files end in “.fna”, whereas amino acid sequence files end in “.faa”. Numbers of sequences in each of these files correspond to the position of the sequence in the associated assembly file.</p> <p> </p> <p><strong>Custom scripts</strong></p> <p>“removePCRdups57.pl” is a Perl script that takes an input FASTQ file and removes exact duplicates identified over a supplied length at the beginning (3’ end) of the read. </p> <p>Run: perl removePCRdups57.pl <inputFASTQ> <length></p> <p> </p> <p>“create_GTF.pl” is a Perl script that takes an input FASTA file and creates a GTF file suitable for input into HtSeq-count v.0.5.4 (Anders et al., 2015).</p> <p>Run: perl create_GTF.pl <inputFASTA></p> <p> </p> <p>“combine_HtSeq.pl” is a Perl script that takes a set of htseq output files and makes a tab delim table of counts with header of sample names and first col of row names. The input file list file should be a text file with lists of Htseq files to combine on each line, where lines are tab delimited of the following form:</p> <p> <NameForOutputFile> <firstHtseqFile> <NextHtseqFile> <...etc...></p> <p>Run: perl combine_HtSeq.pl <inputFileList></p> <p> </p>
Fig. 14 in New narrow-range endemic land snails from the sky islands of northern South Africa (Gastropoda: Streptaxidae and Urocyclidae)
Fig. 14. Sheldonia wolkbergensis sp. nov., holotype, shell diameter 7.7 mm (NMSA P0156/T4074).
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.