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1,620 results for “springs”
Raw Data and Scripts for manuscript submitted to Oikos as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in a Subalpine Grassland Ecosystems'
<p>Raw Data and Scripts for manuscript submitted to PCI as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in Subalpine Grassland Ecosystems'</p>
GeoERA RESOURCE CHAKA data set which contains time series of precipitation and discharge of springs in the CHAKA pilot areas (D5.5)
<p>Dataset which contains time series of precipitation and discharge of springs in the pilot areas of the CHAKA work package of the GeoERA RESOURCE project. The file contains precipitation and spring discharge data of 16 pilot areas in the Karst & Chalk work package. A description of the application of the dataset for the characterisation of the typology of karst systems in given in the D5.3 deliverable of GeoERA RESOURCE of which the pdf is provided. Further information about the CHAKA results can be assessed though the webservices of the European Geological Data Infrastructure (EGDI). </p>
NASA's Airborne Topographic Mapper (ATM) ground calibration data for the Arctic Spring campaign 2019
<p>The Airborne Topographic Mapper (ATM) was a scanning lidar developed and used by NASA for observing the Earth’s topography for several scientific applications, foremost of which was the measurement of changing Arctic and Antarctic ice sheets, glaciers and sea ice. ATM measured topography to an accuracy of better than 5 centimeters by incorporating measurements from GPS (global positioning system) receivers and inertial navigation system (INS) attitude sensors.</p> <p>This particular data set was used in a publication by Studinger <em>et al.</em>, 2022 (<a href="https://doi.org/10.5194/tc-16-3649-2022">https://doi.org/10.5194/tc-16-3649-2022</a>) that developed methods for estimating water depths of supraglacial lakes on the Greenland Ice Sheet.</p> <p>In pressurized aircraft the transmitted laser pulse travels thru the aircraft’s optical window close to the scan mirror. The optical delay fiber that is necessary to separate the transmit pulse and window reflection as well as other system components introduce a laser time-of-flight range bias that needs to be determined from ground calibration measurements. This data set includes ATM waveform data from the T6 and T7 lidars, as well as the true ranges and a MATLAB® function to read ground test waveform data.</p> <p><strong><strong>See also:</strong> </strong></p> <p>NASA's Airborne Topographic Mapper (ATM) ground calibration data for waveform data products: <a href="https://doi.org/10.5281/zenodo.7225936">https://doi.org/10.5281/zenodo.7225936</a></p> <p>User guide for NASA's Airborne Topographic Mapper HDF5 Waveform Data: Products:<a href="https://doi.org/10.5281/zenodo.7246097"> https://doi.org/10.5281/zenodo.7246097</a></p> <p>Collection of MATLAB® functions for working with ATM (Airborne Topographic Mapper, laser altimetry data products in HDF5 waveform format: <a href="https://github.com/mstudinger/ATM-waveform-tools">https://github.com/mstudinger/ATM-waveform-tools</a></p> <p>Airborne Topographic Mapper (ATM) Bathymetry Toolkit (MATLAB® functions): <a href="https://doi.org/10.5281/zenodo.6341229">https://doi.org/10.5281/zenodo.6341229</a></p>
Sensitivity experiment data using the CHASER chemical transport model for investigation of lower-tropospheric spring ozone enhancement over Hanoi
<p>This is the data from the numerical model experiment for investigating the relative importance of different emission source regions on the spring ozone enhancement in the lower troposphere over Hanoi, Vietnam. The details of the investigation are written in the paper by Ogino et al. (2022, Journal of Geophysical Research, Atmosphere, in revision).</p> <p><strong>Experiment description</strong></p> <p>We performed sensitivity experiments using the global chemical-transport model, CHASER (Sudo et al., 2002) with T42 horizontal resolution (approximately 2.8 degrees longitude × 2.8 degrees latitude) and 32 vertical layers from the surface up to 10 hPa in sigma coordinate. The two-hourly model outputs interpolated onto the constant pressure levels at 1000, 990, 970, 930, 870, 790, 700, 610, 530, 460, 400, 350, 300, 260, 230, 200, 176, 153, 133, 116, and 100 hPa were used in this study. Note that the updated model, MIROC-Chem (Miyazaki et al., 2017; Watanabe et al., 2011), includes more detailed chemical processes for both troposphere and stratosphere. Nevertheless, CHASER already includes the most important chemical processes in the NOx-CO-Ozone reactions and can be used to evaluate the impact of NOx emissions on ozone productions. In addition, the simulated ozone performance, as well as ozone response to NOx emissions, are comparable between CHASER and MIROC-Chem (Miyazaki et al., 2020). Thus, the results should not be sensitive to the choice of model.</p> <p>The surface emissions of major ozone precursors, such as carbon monoxide (CO), nitrogen oxide (NOx), and nonmethane hydrocarbons, were included in the model based on the published emission inventories (the Emission Database for Global Atmospheric Research (EDGAR) version 4.2 (EC-JRC/PBL, 2011), the monthly Global Fire Emissions Database (GFED) version 3.1 (van der Werf et al., 2010), and monthly mean Global Emissions Inventory Activity (GEIA) (Graedel et al., 1993)). We employed daily NOx and CO emissions that were optimized using the assimilation of satellite NO2 and CO measurements, where the a priori emissions were constructed based upon bottom-up emission inventories (Miyazaki et al., 2015; 2017). These emissions, including both anthropogenic and biomass burning components, used were obtained from the Tropospheric Chemistry Reanalysis version 1 (TCR-1, Miyazaki et al., 2015) and enabled us to evaluate the emission impacts for individual sources.</p> <p>In the sensitivity experiments, we eliminated the emissions of ozone precursors from the following three source regions: the Indian subcontinent, the northern Indochina Peninsula, and southern China. We conducted spin-up calculations with the optimized emissions for all regions (i.e., standard emissions) from January 1st to the end of February in each year for 10 years from 2005 to 2014. Then, we performed four types of experiments from March 1st to 21st: the control experiment with the standard emissions, and the three sensitivity experiments with the elimination of emission from the above-mentioned three regions, namely the Indian subcontinent, the northern Indochina, the southern China experiments. Because of the non-linear chemistry, the cumulative response from the sensitivity calculations can be different from the total ozone response in the control simulation to some extent as shown by the HTAP modeling works (Turnock et al., 2018; Wild et al., 2012). Nevertheless, they provided important information on the relative contributions of emission sources from different regions. The results of the sensitivity experiments will be compared with the control experiment to investigate the relative contributions of individual emission sources to the ozone enhancement over Hanoi.</p> <p><strong>Files</strong></p> <ul> <li>O3_Fullyear_[YYYY].nc: The 2-hourly data of ozone mixing ratio obtained in the control experiment from January 1 to December 31 in year [YYYY] from 2005 to 2014.</li> <li>[Param]_March_[YYYY].nc: The 2-hourly data obtained in the sensitivity experiment from Mar 1 to 21 in every year [YYYY] from 2005 to 2014. [Param] is one of the following: <ul> <li>O3_Control: Ozone mixing ratio in the control experiment</li> <li>O3_IndianSubcontinent: Ozone mixing ratio in the Indian Subcontinent experiment</li> <li>O3_NorthernIndochina: Ozone mixing ratio in the northern Indochina experiment</li> <li>O3_SouthernChina: Ozone mixing ratio in the southern China experiment</li> <li>CO: Carbon monoxide</li> <li>T: Temperature</li> <li>U: Zonal wind</li> </ul> </li> <li>CO_Emission.nc and NOx_Emission.nc: The monthly mean CO and NOx emissions from the surface used in the model experiments.</li> </ul> <p><strong>Contact</strong></p> <p>Shin-Ya Ogino<br> Japan Agency for Marine-Earth Science and Technology (JAMSTEC)<br> E-mail: ogino-sy@jamstec.go.jp</p>
Labeled songs of domestic canary M1-2016-spring (Serinus canaria)
<p><strong>Labeled songs of domestic canary M1-2016-spring (Serinus canaria)</strong></p> <p><em>J. Giraudon*<sup>123</sup>, N. Trouvain*<sup>123</sup>, A. Cazala<sup>4</sup>, C. Del Negro<sup>4</sup>, X. Hinaut<sup>123</sup></em></p> <p><sup>1</sup> Inria Bordeaux Sud-Ouest, France</p> <p><sup>2</sup> LaBRI, Bordeaux INP, CNRS, UMR 5800, France</p> <p><sup>3</sup> Institut des Maladies Neurogégénératives, Université de Bordeaux, CNRS, UMR 5293, France</p> <p><sup>4 </sup>Paris-Saclay University, UMR 9197 CNRS, Paris-Saclay Institute of Neuroscience, France </p> <p><em>* these authors participated equally to this work.</em></p> <p><strong>General information</strong></p> <p>This dataset contains ~3h of labeled songs (459 songs) of one male canary (called M1) recorded between May 24th and June 15th 2016. Songs were recorded in a sound-isolation chamber using a RODE M3 microphone, an external sound card for microphone amplification (M-Audio Fast Track Ultra 8R), and the software Sound Analysis Pro 2011 (SAP). SAP parameters were set with conservative thresholds (software threshold to 4-6) in order to record the initiation of canary's songs which can be low in volume.</p> <p>Songs were hand labelled by one human expert using Audacity. They were then checked and corrected by another human expert assisted by an automated program based on recurrent neural networks (see References).</p> <p><strong>Dataset description</strong></p> <p>Canary songs are labeled using 27 different identified syllable classes + 1 "call" class identifying simple off-song calls + 1 "TRASH" class for irrelevant sounds (very rare vocalizations or non-bird sounds) + 1 "SIL" class for silence between vocalizations. Songs are annotated at the phrase level: a phrase consists of a repetition of a single syllable type and each phrase type is assigned a label.</p> <p>Annotations are provided in CSV format in the "M1-2016-spring_csv_annotations.zip" archive. There is one file per song, containing:</p> <ul> <li>a "wave" column indicating the song's audio filename;</li> <li>"start" and "end" columns indicating the temporal delimitation of the label from the begining of the song, in seconds;</li> <li>a "syll" column indicating the labels.</li> </ul> <p>Annotations are also provided in <a href="https://manual.audacityteam.org/man/importing_and_exporting_labels.html">Audacity TXT format</a> in the "M1-2016-spring_audacity_annotations.zip" archive. There is one file per song, containing three tabulation-separated columns. The first two column indicates the temporal delimitation (start and end) of the phrase from the begining of the song. The thrid one contains the associated label. Annotations filenames match corresponding song audio filename.</p> <p>Songs are provided in WAV format (44kHz sampling rate) in the "M1-2016-spring_audio.zip" archive. There is one file per song: audio filenames match corresponding annotation filenames.</p> <p><strong>References</strong></p> <p>This dataset was used in:</p> <p>N. Trouvain, X. Hinaut (2021) Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs. HAL preprint <a href="https://hal.inria.fr/hal-03203374">⟨hal-03203374⟩</a></p>
Pre-Preg (PP) Manufacturing and Spring-in monitoring through FBGs, DCs and 3D CMM measurements
<p>ELADINE project is aiming to implement a numerical tool that can reduce reoccurring costs of low-volume production in composite manufacturing of primary structural elements and thus reducing overall manufacturing effort and carbon emissions. A<strong> primary goal of this project is to eliminate tolerance non-compliancy in the manufactured structures caused by natural and unavoidable post-manufacturing distortions, typical for composite materials</strong>. These distortions might render otherwise qualitative components unusable due to their final geometry.</p> <p>Objectives of the Numerical model validation are:</p> <ul> <li>To understand the dominant factors which affects the spring-in phenomenon.</li> <li>To provide the simulation tool with the required values of the properties that influence on spring-in.</li> <li>To verify the simulation tool ability to predict spring-in for a variety of conditions.</li> <li>To develop a procedure of adapting and embedding sensors (dielectric and fiber optic) to obtain proper, useful and accurate signals of the manufacturing parameters (T, degree of cure, strain).</li> <li>To develop interpretation procedures of the signal/curves of sensors to obtain on-line process monitoring information.</li> </ul> <p>To obtain the data to feed and develop the numerical tool able to estimate the component distortions after its manufacturing, a combination of Fiber Optic Sensors (FOS) based on Fiber Bragg Grating (FBG) technology, Dielectric Curing sensors (DC) and 3D scanning were used to monitor the composite coupon manufacturing and the distortions the days after being demoulded. During the manufacturing process embedded FBGs and DC sensors were used to monitor the coupon temperature and strain distribution and resin curing evolution. After the manufacturing and the demolding, the distortions evolution were monitored by the embedded FBGs and by 3D CMM measurements.</p> <p><strong>In the ELADINE project, the distortion monitoring was made to two Out-of-Autoclave manufacturing technologies: liquid resin infusion and oven cured Pre-Preg (PP)</strong>. For both material systems, slightly curved coupons and C-shaped coupons were the geometries selected as representative for the Skin and spars of the wing box. The Skin coupon was curved panel with a 1475 mm radius (with edge rise of 7,65 mm) that was thought to best replicate the wing profile geometry. The C-spar coupon geometry selected for the study was a non-tapered spar section with two different angle with radius of curvature of 5mm and 12mm. This geometry was chosen to simplify measuring and comparisons with wing demo. Furthermore, three different thickness are studied for the Skin coupons and two for the C-spar coupons which were selected from different zones along the wing. Moreover, a C-spar coupon with variable thickness was studied, as a simulation of the transition between zones with different thickness in the wing.</p> <p><strong>In this dataset, the data obtained from the FBGs, DCs and 3D CMM meassurements during a PP manufacturing process and spring-in distortions monitoring can be found.</strong></p>
Sea ice core temperature and salinity data collected during the 2019 SCALE Spring Cruise
<p>Temperature and salinity profiles of sea ice cores extracted from in situ sea ice floes and lifted pancakes were measured in the Atlantic sector of the Antarctic Marginal Ice Zone during the Southern oCean seAsonal Experiment (SCALE) spring cruise in 2019 (<a href="http://www.scale.org.za">www.scale.org.za</a>) aboard the SA Agulhas II.</p>
Model results: Model-based decision support for the choice of active spring frost protection measures in apple production
<p><strong>Background: </strong></p> <p>Apple producers are dealing with weather related risks affecting their production. One important risk, is the damage of buds or young fruits by late spring frosts. Fruit growers can protect their apple orchards against this risk in various ways. With a probabilistic model (available on Git Hub: <a href="https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection">https://github.com/ChristineSchmitz/Supporting_Information_DA_Frost_Protection</a>, <a href="https://doi.org/10.5281/zenodo.11473204">https://doi.org/10.5281/zenodo.11473204</a>), we want to support the decision between several active frost protection measures. The measures considered in the model are: overhead irrigation, below-canopy irrigation, stationary wind machines, mobile wind machines, tractor-mounted gas heaters, portable gas heaters, candles and pellet heaters.</p> <p>As case studies, we parameterized the model for two German apple production regions (Rhineland and Lake Constance region).</p> <p><strong>Repository content:</strong></p> <p>This repository contains the simulation results of 100,000 Monte Carlo runs with the model.</p> <p>The results are provided as .RDS and .csv files. The .RDS files are suitable to be uses with the Code on Git Hub to follow the Post-Hoc analysis and figure plotting.</p>
Evaluation of Heracleum sosnowskyi Manden. survivial after snow cover removing in early spring
<p>The results of an experiment on the effect of snow cover removing on the areas occupied by Heracleum sosnowskyi stands in the early spring period. The experimental (impact) and control plots located in the Syktyvkar city suburb (Komi Republic, Russia).</p> <p>Most of calculation were performed in R. Find the file "FrozenHogweed_R_script.r" for calculation reproducing.</p>
Spring Precipitation Amount and Timing Predict Restoration Success in a Semi-Arid Ecosystem Code and Data
<table> <tbody> <tr> <td>The data here is summary data compiled from all years of the project that lead to the publication Spring Precipitation Amount and Timing Predict Restoration Success in a Semi-Arid Ecosystem with the Journal of Applied Ecology and code to analyze these data. Our study was focused on the Northern Great Basin ecosystem. We conducted surveys at 48 sites over the course of five years (2016-2020). All were located on public lands managed by either the Bureau of Land Management, Idaho Department of Lands, or Oregon State Lands Department. We looked at the influence of management, biotic, abiotic and weather variables predicting seedling establishment success, 45 predictor variables in all. Machine learning techniques were used to select most important predictor variables to be used in future work predicting good seedling establishment windows. </td> </tr> </tbody> </table>
Spatial dataset for ecological niche and spatial distribution modeling of Herichthys bartoni (Cichliformes: Cichlidae) in the Media Luna spring, Mexico
<p>Dataset for the endangered endemic cichlid <em>Herichthys bartoni</em> in the Media Luna spring, Mexico. This data includes occurrences records by species life stage (adult, juvenile and fry), in three field sessions corresponding to the summer period, in the years 1999, 2009 and 2019.</p> <p>For more information about the codes where the previous datasets could be used, visit the following repository with URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Likewise, the UC and WDp variables used to run the ecological niche and spatial distribution model, by summer period, can be found in the following repository wirh URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p>
Seamless 30 meter Sentinel-2 L2A Pan-European seasonal cloudless mosaics from winter 2018 to spring 2020
<p>Seasonal composites of <a href="https://roda.sentinel-hub.com/sentinel-s2-l2a/readme.html">Sentinel-2 L2A</a> imagery created as part of the <a href="https://opendatascience.eu/geo-harmonizer/">Geo-harmonizer project</a>, containing median of the blue, green, red, NIR, SWIR1 and SWIR2 bands, as well as pixel counts per season, produced in the ETRS89-extended / LAEA Europe (<a href="https://epsg.io/3035">EPSG:3035</a>) spatial reference system. Mosaics were produced from winter 2017 to spring 2020, with the imaging intervals per season being:</p> <ul> <li>winter: 02/12 of previous year to 20/03</li> <li>spring: 21/03 to 24/06</li> <li>summer: 25/06 to 12/09</li> <li>fall: 13/09 to 01/12</li> </ul> <p>Seamlessness of the composites was achieved through overlapping pixel averaging weighted by distance from the suborbital track.</p> <p>The data are provided as UINT8 values and were scaled with a common threshold (13712) chosen to minimize compression loss across the dataset. Data at the original (UINT16) scale can be obtained as follows:</p> <p><span>\(x_{\text{uint16}} = 13712 {x_{\text{uint8}} \over 254}\)</span></p> <p>For any additional questions regarding the data please contact the authors at <a href="mailto:multione@multione.hr?subject=S2L2A%20Europe%20mosaics">multione[at]multione.hr</a>.</p>
Research Beyond the Lab, Spring Term 2022, Global Health Engineering, ETH Zurich. Raw data and analysis-ready derived data on waste management in public spaces in Zurich, Switzerland.
<p>This repository contains all raw and derived data produced as part of the <a href="https://rbtl-fs22.github.io/website/">ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L)</a> offered in spring term 2022.</p> <p>Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of “Trash in the Public Spaces of Zurich” in collaboration with <a href="https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling.html">Entsorgung & Recycling Zürich (ERZ)</a>, the waste management department at Stadt Zürich.</p> <p>Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R & RStudio, and collaboration and version control with Git & GitHub.</p>
Data for "Revisiting the zonally asymmetric extratropical circulation of the Southern Hemisphere spring using complex empirical orthogonal functions"
<p>Data used in "Revisiting the zonally asymmetric extratropical circulation of the Southern Hemisphere spring using complex empirical orthogonal functions"</p>
2.8M SNPs Chinese Spring RefSeq v2.1 dataset
<p>VCF file of 2,799,166 single nucleotide polymorphism (SNP) markers positioned onto the Chinese Spring reference assembly RefSeq v2.1 developed by the International Wheat Genome Sequence Consortium (IWGSC; Zhu et al., 2021). These SNPs were lifted from the 1,000 wheat exome project, originally positioned onto RefSeq v1.0 (He et al., 2019). The SNP projection from RefSeq v1.0 onto RefSeq v2.1 was accomplished using LiftOff (Shumate and Salzberg, 2021).</p> <p>References</p> <p>He F, Pasam R, Shi F, Kant S, Keeble-Gagnere G, Kay P, Forrest K, Fritz A, Hucl P, Wiebe K, et al: <strong>Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome.</strong> <em>Nature Genetics </em>2019, <strong>51:</strong>896-904.</p> <p>Shumate A, Salzberg SL: <strong>Liftoff: accurate mapping of gene annotations.</strong> <em>Bioinformatics </em>2021, <strong>37:</strong>1639-1643.</p> <p>Zhu T, Wang L, Rimbert H, Rodriguez JC, Deal KR, De Oliveira R, Choulet F, Keeble-Gagnère G, Tibbits J, Rogers J, et al: <strong>Optical maps refine the bread wheat Triticum aestivum cv. Chinese Spring genome assembly.</strong> <em>The Plant Journal </em>2021, <strong>107:</strong>303-314..</p>
An ice-tethered, non-floating Trident Sensors Helix Beacon during SCALE 2019 Spring Cruise
<p><strong>Brief data description</strong></p> <p>A Trident Sensors Helix Beacon identical to the one described in Womack et al. (2022), was deployed on sea ice at latitude 59.47<sup>o</sup> S and longitude 10.89<sup>o</sup> E, on 30 October 2019, as part of the <em>Southern oCean seAsonal Experiment</em> (SCALE; Ryan-Keogh and Vichi, 2022) aboard the SA Agulhas II.</p> <p>The region where the Trident was deployed (Antarctic marginal ice zone) consisted of first-year ice conditions, with an average thickness of 80-90 cm. The device was deployed by hand by three people, lowered by crane from the ship to the ice on a basket cradle.</p> <p>The temporal resolution was approximately four hours. The survival of the Trident depended on staying fixed to the ice floe and its battery life. The Trident recorded GPS position and air temperature, and transmitted data until 2 December 2019, where it sank due to sea-ice melting.</p> <p><strong>Buoy name and raw data:</strong></p> <p>Trident: Unit4.xlsx</p> <p><strong>Related code: </strong>The buoy data has been processed using https://github.com/mvichi/antarctic-buoys/.</p>
Polar Iridium Surface Velocity Profilers (p-iSVP), and standard Iridium Surface Velocity Profilers (iSVP) during SCALE 2019 Winter and Spring Cruises
<p><strong>Brief data description</strong></p> <p>In 2019, winter and spring scientific research expeditions aboard the SA Agulhas II were conducted along the Good-Hope line (0<sup>o</sup> E) to the Antarctic marginal ice zone (MIZ) in the north-eastern Weddell Sea region as part of the <em>Southern oCean seAsonal Experiment</em> (SCALE; Ryan-Keogh and Vichi, 2022).</p> <p>During the winter expedition, three polar Iridium Surface Velocity Profilers (p-iSVPs; MetOcean model) were deployed by the South African Weather Service (SAWS) between 27 July and 28 July 2019. These buoys were analysed in de Vos et al. (2022). The region of deployment consisted of pancake-ice conditions with an average ice thickness of 40-60 cm. The instruments were deployed by hand by three people, lowered by crane from the ship to the ice on a basket cradle. The first buoy (p-iSVP 1) was deployed in water, in between pancake ice floes, while the other two buoys (p-iSVP 2 and p-iSVP 3) were deployed on roughly circular ice floes > 3 m in diameter.</p> <p>These buoys were expendable devices that recorded GPS position, air and ice temperature, and barometric pressure. The temporal resolution is 30 minutes for p-iSVP 1 and hourly for p-iSVP 2 and p-iSVP 3. The survival of these sensors depended on their battery life, since p-iSVPs can continue to drift in the ocean after ice melting and can be further refrozen in between floes. p-iSVP 1 and p-iSVP 3 continued to transmit data until 15 October 2019. p-iSVP 2 stopped transmitting data on 25 August 2019.</p> <p>During the spring expedition, three standard Iridium Surface Velocity Profilers (iSVPs 4-6; Pacific Gyre model) were deployed by SAWS between 24 October and 28 October 2019 (de Vos et al., 2022). Specifically-designed frames were built around these three iSVPs to allow them to stand securely on the ice, without damaging the non-polar battery, and also to make sure they operated as Lagrangian ice trackers. These buoys were deployed during first-year ice conditions, with an average ice thickness of 80-90 cm. The instruments were deployed with the same protocol as the winter buoys.</p> <p>These buoys recorded GPS position, air temperature and barometric pressure, every hour. Their survival, like the winter p-iSVPs, also depended on their battery life, and therefore it was possible for them to continue to drift after ice melting. The iSVPs transmitted data until 19 December 2019.</p> <p><strong>Buoy names and raw data:</strong></p> <p>p-iSVP 1: 300234067003010-300234067003010-20191015T064320UTC.csv</p> <p>p-iSVP 2: 300234067002060-300234067002060-20191015T064316UTC.csv</p> <p>p-iSVP 3: 300234066992870-300234066992870-20191015T064314UTC.csv</p> <p>iSVP 4: 300234066433050.xlsx</p> <p>iSVP 5: 300234066433051.xlsx</p> <p>iSVP 6: 300234066433052.xlsx</p> <p><strong>Related code: </strong>The buoy data has been processed using https://github.com/mvichi/antarctic-buoys/. </p>
Cue the chorus: Canyon treefrog calling phenology on the falling limb of spring floods and warming nights
Phenology is the timing of life events tied to environmental or abiotic cues. We used autonomous recording units (ARUs) across spring-summer months in 2022 to capture breeding calls from canyon treefrog (Hyla arenicolor). ARUs were placed in perennial and intermittent stream reaches across five Wilderness Areas within the upper Verde River basin in Arizona. We monitored streams by installing stream flow gauges (water level recorders). Treefrogs call at relatively low flow after spring floods. This suggests that stream-dwelling anurans may breed in response to flooding followed by prolonged periods of base flows which could be important for tadpole metamorphosis. Implications for stream regulation suggest maintaining the magnitude and timing of flood pulse events can benefit recruitment of stream-breeding amphibians.
Three synoptic surveys of streams throughout a 48km2 watershed near Toolik Lake, AK in spring (early-June), summer (mid-July), and fall (mid-September) 2011.
To determine temporal and spatial patterns in arctic stream biogeochemistry we conducted three synoptic surveys of streams throughout a 48km2 watershed near Toolik Lake, AK in spring (early-June), summer (mid-July), and fall (mid-September) 2011. During each synoptic survey, we sampled 52 sites within a period of four days to minimize the effect of temporal hydrologic variability. At each site we measured stream temperature, pH, and conductivity and sampled water for solute analysis.
Spring and Fall Leaf Phenology from Coweeta LTER Soil Moisture Sites SM2 & SM4, Coweeta Hydrologic Laboratory, Otto, NC, 2003-2015
Spring vegetative bud break, leaf elongation, fall leaf color, and leaf senescence are monitored at the two scaffold towers located at Project 1040 soil moisture microclimate sites 2 and 4. We have identified a variety of species at the elevation extremes within the Coweeta basin for this yearly monitoring project.
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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.