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Locally verified evaporation data from a NOAA evaporation pan at USDA Jornada Experimental Range headquarters, southern New Mexico USA, 1953-1979
This data package contains locally verified monthly total pan evaporation data from a NOAA National Weather Service station located at the USDA Jornada Experimental Range headquarters in southern New Mexico, USA. The evaporation pan measurements commenced in 1953 and ended in 1979 when the instrument was decommissioned. Pan evaporation observations were made using standard U.S. climatological service instrumentation and procedures. The included data were verified and transcribed directly from records retrieved from NOAA in ~1995 and have since undergone quality control and assurance procedures different than those in place at NOAA. These data therefore differ from those directly downloadable from NOAA servers. There is no further data from this decommissioned instrument, so this dataset is now complete and data will no longer be updated here. All observations from this weather station have also undergone NOAA QA/QC procedures and those data are available by accessing the Jornada Experimental Range, NM US GHCN station through the National Climatic Data Center portal (https://www.ncdc.noaa.gov/cdo-web/datasets/GSOM/stations/GHCND:USC00294426/detail - monthly pan evaporation data are available back to 1930, but there may be data issues prior to 1953).
Data: An experimental sound exposure study at sea: No spatial deterrence of free-ranging pelagic fish
<p>Data abstract:</p> <p>All data and scripts to replicate all plots and statistical results of the paper mentioned below. The data are sound recordings and processed echosounder data (raw echosounder data is available on request but > 100 GB in size and require specialized software).</p> <p> </p> <p>Paper reference:</p> <p>Jeroen Hubert<span>, </span>Jozefien M. Demuynck<span>, </span>M. Rafa Remmelzwaal<span>, </span>Carlota Muñiz<span>, </span>Elisabeth Debusschere<span>, </span>Benoit Berges<span>, </span>Hans Slabbekoorn; An experimental sound exposure study at sea: No spatial deterrence of free-ranging pelagic fish. <em>J. Acoust. Soc. Am.</em> 1 February 2024; 155 (2): 1151–1161. <a href="https://doi.org/10.1121/10.0024720" target="_blank" rel="noopener">https://doi.org/10.1121/10.0024720</a></p> <p> </p> <p>Paper abstract:</p> <p>Acoustic deterrent devices are used to guide aquatic animals from danger or toward migration paths. At sea, moderate sounds can potentially be used to deter fish to prevent injury or death due to acoustic overexposure. In sound exposure studies, acoustic features can be compared to improve deterrence efficacy. In this study, we played 200–1600 Hz pulse trains from a drifting vessel and investigated changes in pelagic fish abundance and behavior by utilizing echosounders and hydrophones mounted to a transect of bottom-moored frames. We monitored fish presence and tracked individual fish. This revealed no changes in fish abundance or behavior, including swimming speed and direction of individuals, in response to the sound exposure. We did find significant changes in swimming depth of individually tracked fish, but this could not be linked to the sound exposures. Overall, the results clearly show that pelagic fish did not flee from the current sound exposures, and we found no clear changes in behavior due to the sound exposure. We cannot rule out that different sounds at higher levels elicit a deterrence response; however, it may be that pelagic fish are just more likely to respond to sound with (short-lasting) changes in school formation.</p> <p> </p> <p> </p>
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
<h2>General</h2> <p>For more details and the most up-to-date information please consult our project page: <a href="https://kainmueller-lab.github.io/fisbe" target="_blank" rel="noopener">https://kainmueller-lab.github.io/fisbe</a>.</p> <h2>Summary</h2> <ul> <li>A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains <ul> <li>30 completely labeled (segmented) images</li> <li>71 partly labeled images</li> <li>altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)</li> </ul> </li> <li>To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects</li> <li>A set of metrics and a novel ranking score for respective meaningful method benchmarking</li> <li>An evaluation of three baseline methods in terms of the above metrics and score</li> </ul> <h2>Abstract</h2> <p>Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.</p> <h2>Dataset documentation:</h2> <p>We provide a detailed documentation of our dataset, following the <a href="https://arxiv.org/abs/1803.09010" target="_blank" rel="noopener">Datasheet for Datasets</a> questionnaire:</p> <p><em>>> <a href="https://kainmueller-lab.github.io/fisbe/datasheet" target="_blank" rel="noopener">FISBe Datasheet</a></em></p> <p>Our dataset originates from the <a href="https://www.janelia.org/project-team/flylight" target="_blank" rel="noopener">FlyLight project</a>, where the authors released a large image collection of nervous systems of ~74,000 flies, <a href="https://gen1mcfo.janelia.org/cgi-bin/gen1mcfo.cgi" target="_blank" rel="noopener">available for download</a> under CC BY 4.0 license.</p> <h2>Files</h2> <ul> <li>fisbe_v1.0_{completely,partly}.zip <ul> <li>contains the image and ground truth segmentation data; there is one <em>zarr</em> file per sample, see below for more information on how to access <em>zarr</em> files.</li> </ul> </li> <li>fisbe_v1.0_mips.zip <ul> <li>maximum intensity projections of all samples, for convenience.</li> </ul> </li> <li>sample_list_per_split.txt <ul> <li>a simple list of all samples and the subset they are in, for convenience.</li> </ul> </li> <li>view_data.py <ul> <li>a simple python script to visualize samples, see below for more information on how to use it.</li> </ul> </li> <li>dim_neurons_val_and_test_sets.json <ul> <li>a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.</li> </ul> </li> <li>Readme.md <ul> <li>general information</li> </ul> </li> </ul> <h2>How to work with the image files</h2> <p>Each sample consists of a single 3d MCFO image of neurons of the fruit fly.<br>For each image, we provide a pixel-wise instance segmentation for all separable neurons.<br>Each sample is stored as a separate <em>zarr</em> file (<a href="https://zarr.readthedocs.io" target="_blank" rel="noopener">zarr</a> is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").<br>The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.<br>The segmentation mask for each neuron is stored in a separate channel.<br>The order of dimensions is CZYX.</p> <p>We recommend to work in a virtual environment, e.g., by using conda:</p> <p><code>conda create -y -n flylight-env -c conda-forge python=3.9</code><br><code>conda activate flylight-env</code></p> <h3>How to open <em>zarr</em> files</h3> <ol> <li>Install the python zarr package: <pre><code>pip install zarr</code></pre> </li> <li>Opened a zarr file with:<br> <p><code>import zarr</code><br><code>raw = zarr.open(<path_to_zarr>, mode='r', path="volumes/raw")</code><br><code>seg = zarr.open(<path_to_zarr>, mode='r', path="volumes/gt_instances")</code></p> <p><code># optional:</code><br><code>import numpy as np</code><br><code>raw_np = np.array(raw)</code></p> </li> </ol> <p>Zarr arrays are read lazily on-demand.<br>Many functions that expect numpy arrays also work with zarr arrays.<br>Optionally, the arrays can also explicitly be converted to numpy arrays.</p> <h3>How to view <em>zarr</em> image files</h3> <p>We recommend to use <a href="https://napari.org" target="_blank" rel="noopener">napari</a> to view the image data.</p> <ol> <li>Install napari: <pre><code>pip install "napari[all]"</code></pre> </li> <li>Save the following Python script: <br> <p><code>import zarr, sys, napari</code></p> <p><code>raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")</code><br><code>gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")</code></p> <p><code>viewer = napari.Viewer(ndisplay=3)</code><br><code>for idx, gt in enumerate(gts):</code><br><code> viewer.add_labels(</code><br><code> gt, rendering='translucent', blending='additive', name=f'gt_{idx}')</code><br><code>viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')</code><br><code>viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')</code><br><code>viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')</code><br><code>napari.run()</code></p> </li> <li>Execute: <pre><code>python view_data.py <path-to-file>/R9F03-20181030_62_B5.zarr</code></pre> </li> </ol> <h2>Metrics</h2> <ul> <li>S: Average of avF1 and C</li> <li>avF1: Average F1 Score</li> <li>C: Average ground truth coverage</li> <li>clDice_TP: Average true positives clDice</li> <li>FS: Number of false splits</li> <li>FM: Number of false merges</li> <li>tp: Relative number of true positives</li> </ul> <p>For more information on our selected metrics and formal definitions please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>Baseline</h2> <p>To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely <a href="https://github.com/Kainmueller-Lab/PatchPerPix" target="_blank" rel="noopener">PatchPerPix (ppp)</a>, <a href="https://github.com/google/ffn" target="_blank" rel="noopener">Flood Filling Networks (FFN)</a> and a non-learnt application-specific <a href="https://www.biorxiv.org/content/10.1101/2020.06.07.138941v1" target="_blank" rel="noopener">color clustering from Duan et al.</a>.<br>For detailed information on the methods and the quantitative results please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>License</h2> <p>The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener">Creative Commons Attribution 4.0 International (CC BY 4.0) license</a>.</p> <h2>Citation</h2> <p>If you use <em>FISBe</em> in your research, please use the following BibTeX entry: </p> <pre><code>@misc{mais2024fisbe, title = {FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures}, author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller}, year = 2024, eprint = {2404.00130}, archivePrefix ={arXiv}, primaryClass = {cs.CV} }</code></pre> <h2>Acknowledgments</h2> <p>We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable<br>discussions.<br>P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.<br>This work was co-funded by Helmholtz Imaging.</p> <h2>Changelog</h2> <p>There have been no changes to the dataset so far.<br>All future change will be listed <a href="https://kainmueller-lab.github.io/fisbe/changelog" target="_blank" rel="noopener">on the changelog page</a>.</p> <h2>Contributing</h2> <p>If you would like to contribute, have encountered any issues or have any suggestions, please <a href="https://github.com/Kainmueller-Lab/fisbe/issues" target="_blank" rel="noopener">open an issue</a> for the FISBe dataset in the accompanying github repository.</p> <p>All contributions are welcome!</p>
Modern aridity in the Altai-Sayan Mountain Range derived from multiple millennial proxies
<p><em>1500-year stable carbon and oxygen isotopes in larch tree-ring cellulose from the Altai-Sayan Mountain Range region </em>(49-51N, 87-89 E)</p> <p><em>Regional summer (June-July-August) precipitation reconstruction for the Altai-Sayan Mountain Range region based on d<sup>13</sup>C in tree-ring cellulose (d<sup>13</sup>C<sub>cell </sub>) combined with Co/Inc and Rb/Sr from Teletskoe Lake core sediments (TLs).</em></p> <p><em>Regional summer air temperature (June-July-August) reconstruction based on d<sup>18</sup>O<sub> </sub>in tree-ring cellulose (d<sup>18</sup>O<sub>cell</sub>), tree-ring width (TRW), latewood density (MXD) and elemental concentrations (Ca, Ti, Br/Sr) in the Teletskoe Lake core sediments (TLs).</em></p>
Supporting Information for 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'
<p><strong>Supporting Information of 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'</strong></p> <p>This dataset contains the Supporting Information of the publication </p> <p>Rühr PT & Blanke A <strong>(2022)</strong>: 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'. doi: <a href="https://doi.org/10.1111/2041-210X.13909">10.1111/2041-210X.13909</a>.</p> <p>It includes</p> <ul> <li>validation measurements the forceX setups (1 Ruehr Blanke 2022 validation measurements.zip)</li> <li>all CAD files to build the forceX setup (3D-printed or metal-turned) (2 Ruehr Blanke 2022 forceX CAD files.zip)</li> <li>forceX assembly instructions in HTML format, including schematics of custom electronics (3 Ruehr Blanke 2022 forceX Assembly instructions.html)</li> <li>forceX assembly instructions as video (4 Ruehr Blanke 2022 forceX assembly video 03.mp4)</li> <li>R code that produced all validation-related figures used in the original publication and that functions as a forceR v.1.0.13 example workflow (5 Ruehr Blanke 2022 forceR_workflow_example.R)</li> <li>Python code to take videos of force measurements using the forceX camera module (6 Ruehr Blanke 2022 forceX_RPi_camera_code.py)</li> <li>bundled version of forceR v.1.0.15 (forceR_1.0.15.tar.gz)</li> </ul> <p>The CAD files and assembly instructions are also available on <a href="https://www.thingiverse.com/thing:4961834">Thingiverse</a>. The forceR package is available on <a href="https://cran.r-project.org/web/packages/forceR/index.html">CRAN</a> (stable version) and <a href="https://github.com/Peter-T-Ruehr/forceR">GitHub</a> (development version).</p>
Plant Atlas 2020 — British and Irish phenological data (flowering and leafing ranges)
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data behind the phenological diagrams (flowering and leafing) presented on the Plant Atlas 2020 website (<a href="http://www.plantatlas2020.org"><span>www.plantatlas2020.org</span></a><span>) and in the <em>Plant Atlas 2020</em> book. </span><span>Note that for non-flowering plants included in the atlas (e.g. ferns, horsetails etc.), the “flowering” fields in the phenology file included here are equivalent to the months when spore-bearing structures are visible.</span></p>
Supplemental Figures for "On the comparative utility of entropic learning versus deep learning for long-range ENSO prediction"
<p>Supplemental figures for the paper "On the comparative utility of entropic learning versus deep learning for long-range ENSO prediction".</p>
Energy Levels of ThII in the range of 7 to 10 eV
<p>Data is presented on 166 previously unknown energy levels of Th+ ions, observed using resonant two-step laser excitation of trapped ions. The levels are of even parity and within the energy range from 7.8 to 9.8 eV. Observed lines are listed.<br> The observed levels can be relevant for the excitation or decay of the Th-229m isomeric nuclear state which lies in this energy range.</p>
Updated NLL-SSST-coherence earthquake relocation catalog for the 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake sequence.
<p>CSV catalog and plots of NLL-SSST-coherence earthquake relocations of 17583 events M≥1 2020.01.01 to 2021.08.20 for the 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake sequence.</p> <p>This dataset is an update of the relocations presented in the pre-print article:<br> The 2020 Mw 6.5 Monte Cristo Range, Nevada earthquake: relocated seismicity shows rupture of a complete shear-crack system<br> https://eartharxiv.org/repository/view/1904/</p> <p> </p> <p>Plots show map, view from south and view from N73E of NLL-SSST-coherence relocations events with error-ellipsoid, semi-major axis ≤ 5 km. Caption (adapted from https://eartharxiv.org/repository/dashboard/1904/ ; Fig 2):</p> <p>Event color showing hypocenter depth and symbol size proportional to magnitude. Re-picked Mw6.5 hypocenter and its proxy (mean hypocenter of 3 well constrained foreshocks) indicated by small and large, dark red, cross symbols, respectively. SHmin and SHmax show directions of regional minimum and maximum compressive stress, respectively; the intermediate principal stress axis is vertical. Seismic stations shown as dark gray tetrahedrons. Brown lines show faults from the Quaternary fault and fold database for the United States. Background topography image from OpenTopgraphy.org. Red lines show mapped surface ruptures from http://www.nbmg.unr.edu/Geohazards/Earthquakes/MonteCristoRangeEQData.html and https://doi.org/10.1785/0220200371</p>
Replication Data for: "Ocean acidification increases susceptibility to sub-zero air temperatures in ecosystem engineers and limit poleward range shifts"
<p>These datasets contain all the raw data needed to replicate the results from our paper <em>Ocean acidification increases susceptibility to sub-zero air temperatures in ecosystem engineers and limit poleward range shifts</em> published in eLife - <a href="https://doi.org/10.7554/eLife.81080">https://doi.org/10.7554/eLife.81080</a></p>
Short-Range Electronic Interactions between Vanadium and Molybdenum in Bimetallic SAPO‑5 Catalysts Revealed by Hyperfine Spectroscopy
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, Computer Simulation and Analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DTA</strong>, and <strong>m</strong>.</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong> and <strong>DTA</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension<strong> m</strong></li> </ul> </li> <li>Are the data <strong>generated</strong> (e.g. by a machine) or <strong>collected</strong> (e.g. by means of a survey)? <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>Q-band Pulsed-EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with ER5106QT cavity and ER035 M NMR gaussmeter produced by Bruker.</li> </ul> </li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20230612_01_CW </strong>folder includes CW-EPR spectroscopic measurements and computer simulations/analyses, original data are in DTA/DSC formats; simulations in m format.</li> <li>Files in <strong>PARACAT_WP4_20230612_02_Pulse</strong> folder includes Pulsed-EPR spectroscopic measurements and computer simulations/analyses, original data are in DTA/DSC formats; files in m format were used to process the data.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>HYSCORE </strong>– HYperfine Sublevel CORrelation spectroscopy</li> <li>definitions of variables: <strong>Magnetic field, Temperature</strong></li> <li>units of measurement: <strong>Gauss (G), K</strong></li> </ul> </li> </ul>
A scrubbed subset of near-surface, soil, and air temperature data acquired across multiple locations on the San Joaquin Experimental Range, California, 2012-2017
These temperature records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These temperature records are for studies at the San Joaquin Experimental Range (Lat 37.083, Long -119.716, elevation 210-520 m, www.fs.fed.us/psw/ef/san_joaquin/). Temperature sensors were located at 23 sites across the landscape. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges, and valleys. To characterize surface temperature variation within a site, 21 sensors were arranged in an identical pattern around and in six, 5x5 m experimental gardens. An additional 18 sensors were placed along three transects over the landscape running E-W. They were placed strategically to sample topographic inflection points (hill tops and valley bottoms) as well as north and south facing slopes. Temperatures were recorded on a 10 or 20-minute interval, depending on the sensor, using HOBO (Onset, www.onsetcomp.com) devices.
SGS-LTER Standard Production Data: 1983-2008 Annual Aboveground Net Primary Production on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-2008, ARS Study Number 6 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/700/1. The abstract below was extracted from the Level 0 data package and is included for context: 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. The objective of the long-term ANPP study is to monitor long-term net above ground primary production of the shortgrass steppe community by species. There are 6 sites: ridgetop (ridge), midslope (mid), swale, ESA (replicate 1 not 2), Section 25 (SEC 25), and owl-creek (OC). Each site is located in a different landscape position or soil type on the shortgrass steppe and may be grazed or not. Ridgetop, midslope and swale are grazed and are sampled along a catena. Section 25 is grazed and is located in an upload grassland. ESA is an ungrazed upland grassland an is the control from the Ecosystem Stress Area experiment. Owl Creek is ungrazed and is located in the lowland along the owl creek drainage. There are 3 transects with 5 plots in each transect. Plots in the grazed locations are protected by cages. Because this is a monitoring effort, true replicates across the landscape are not
SGS-LTER Long-Term Monitoring Project: Vegetation Cover on Small Mammal Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1999 -2006, ARS Study Number 118 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/140/17. The abstract below was extracted from the Level 0 data package and is included for context: 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. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83458. The abundance and diversity of small mammals in shortgrass steppe is strongly influenced by the structure and composition of vegetation. Vegetation structure provides cover from predators and harsh abiotic conditions. Plant species composition affects the types of seeds and herbaceous material available to granivores and herbivores, and influences arthropod populations, which are important prey for the omnivorous species that dominate in shortgrass steppe. Both vegetation structure and plant community composition are sensitive to the availability of precipitation as well as the activity of large mammalian herbivores. In 1999, we began measuring vegetation structure and plant community composition on the three grassland and three shrubland trapping webs where we live-trap small mammals
SGS-LTER Long-term Monitoring Project: Spotlight Rabbit Count on the Central Plains Experimental Range, Nunn, Colorado, USA 1994-2006, ARS Study Number 98 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/136/17. The abstract below was extracted from the Level 0 data package and is included for context: 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. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83448. Rabbits are the most important small-mammal herbivores in shortgrass steppe, and may significant influence the physiognomy and population dynamics of herbaceous plants and woody shrubs. Rabbits also are the most important prey of mammalian carnivores such as coyotes and large raptors such as golden eagles and great horned owls. Two hares (Lepus californicus, L. townsendii) and one cottontail rabbit (Sylvilagus audubonii) occur in shortgrass steppe. In 1994, we initiated long-term studies to track changes in relative abundance of rabbits on the Central Plains Experimental Range (CPER). On four nights each year (one night each season, usually on new moon nights in January, April, July, October), we drove a 32-km route consisting of pasture two-track and gravel roads on the CPER. This wa
SGS-LTER Long-Term Montioring Project: Arthropod Pitfall Trapping on Small Mammal Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1998-2006, ARS Study Number 118 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/134/17. The abstract below was extracted from the Level 0 data package and is included for context: 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. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83450. With the exception of heteromyids, eg kangaroo rats and pocket mice, most small rodents in shortgrass steppe are omnivorous. Depending on season, arthropods (insects and arachnids) make up 40-85% of the diet of grasshopper mice and thirteen-lined ground squirrels, the most widespread rodents in northern shortgrass steppe. Small mammals are among the most important predators of ground-dwelling macroarthropods and herbivorous insects provide a direct resource link between weather and plant production. Understanding temporal variability in the abundance of arthropods is central to determining the mechanisms that drive small rodent populations. At present, there are no long-term studies of arthropods in shortgrass steppe, despite the important role that these taxa play in grassland food w
SGS-LTER Long-Term Monitoring Project: Small Mammals on Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1994 -2006, ARS Study Number 118 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/137/17. The abstract below was extracted from the Level 0 data package and is included for context: 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. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83452. Small mammals (rabbits, rodents) are integral components of semiarid ecosystems because of their roles as consumers of plants, seeds and arthropods, as soil disturbance agents, and as food for raptors, snakes and mammalian carnivores. Because of their vagility and intermediate trophic position, populations of small mammals may track changes in vegetation and the abiotic environment that may result from shifts in land-use and other anthropogenic disturbances. However, these populations are variable over space and time, and their response to environmental changes may not be immediately apparent given their behavioral flexibility and relatively long life-spans and generation times. Patterns in the distribution and abundance of small mammals thus may simultaneously reflect and affect the
SGS-LTER Ecosystem Stress Area - long-term density dataset following nutrient enrichment stress on the Central Plains Experimental Range in Nunn, Colorado, USA 1975-2011, ARS Study Number 3 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/520/8. The abstract below was extracted from the Level 0 data package and is included for context: 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. Water, nitrogen, and water-plus-nitrogen at levels beyond the range normally experience by shortgrass steppe communities were applied from 1971 through 1975, plant densities were sampled through 1977, and then sampling resumed in 1982, with sampling frequencies changing from annually to every other year. The initial sampling from 1970 to 1974 showed that the water and water plus nitrogen treatments had the strongest effect on plant community structure, both treatments increased biomass, and exotic weed species were noted on the water plus nitrogen treatment. Later sampling from 1982 to 1991 showed a ten-fold increase in exotic weed species on the water plus nitrogen plots as compared to the controls (Milchunas and Lauenroth 1995), a community change that has persisted on this site due to a chronic elevation of soil nitrogen caused by a plant tissue/soil organic matter feedback mec
SGS-LTER Ecosystem Stress Area - long-term point-frame (percent basal cover) dataset following nutrient enrichment stress on the Central Plains Experimental Range in Nunn, Colorado, USA 1982-2011, ARS Study Number 3 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/521/7. The abstract below was extracted from the Level 0 data package and is included for context: 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. Water, nitrogen, and water-plus-nitrogen at levels beyond the range normally experience by shortgrass steppe communities were applied from 1971 through 1975, plant densities were sampled through 1977, and then sampling resumed in 1982, with sampling frequencies changing from annually to every other year. The initial sampling from 1970 to 1974 showed that the water and water plus nitrogen treatments had the strongest effect on plant community structure, both treatments increased biomass, and exotic weed species were noted on the water plus nitrogen treatment. Later sampling from 1982 to 1991 showed a ten-fold increase in exotic weed species on the water plus nitrogen plots as compared to the controls (Milchunas and Lauenroth 1995), a community change that has persisted on this site due to a chronic elevation of soil nitrogen caused by a plant tissue/soil organic matter feedback mec
Concentration of dissolved inorganic carbon (DIC) and del 13C isotope value for lakes and rivers on North Slope from Brooks Range to Prudhoe Bay, Arctic LTER 1988 to 1989.
Concentration of dissolved inorganic carbon (DIC) and del 13C isotope value for lakes and rivers on North Slope from Brooks Range to Prudhoe Bay, Arctic LTER 1988 to 1989.
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