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opencc-by-4.0Jun 2024View details →

glenglat: Global englacial temperature database

<p>Open-access database of englacial temperature measurements compiled from data submissions and published literature. It is developed on <a href="https://github.com/mjacqu/glenglat">GitHub</a> and published to <a href="https://doi.org/10.5281/zenodo.11516611">Zenodo</a>. This version (1.0.0) of the dataset is described in the following publication:</p> <blockquote> <p>Mylène Jacquemart, Ethan Welty, Marcus Gastaldello, and Guillem Carcanade (2025). glenglat: A database of global englacial temperatures. Earth System Science Data 17(4): 1627–1666. <a href="https://doi.org/10.5194/essd-17-1627-2025">https://doi.org/10.5194/essd-17-1627-2025</a></p> </blockquote> <h2>Dataset structure</h2> <p>The dataset adheres to the Frictionless Data <a href="https://specs.frictionlessdata.io/tabular-data-package">Tabular Data Package</a> specification. The metadata in <code>datapackage.json</code> describes, in detail, the contents of the tabular data files in the <code>data</code> folder:</p> <ul> <li><code>source.csv</code>: Description of each data source (either a personal communication or the reference to a published study).</li> <li><code>borehole.csv</code>: Description of each borehole (location, elevation, etc), linked to <code>source.csv</code> via <code>source_id</code> and less formally via source identifiers in <code>notes</code>.</li> <li><code>profile.csv</code>: Description of each profile (date, etc), linked to <code>borehole.csv</code> via <code>borehole_id</code> and to <code>source.csv</code> via <code>source_id</code> and less formally via source identifiers in <code>notes</code>.</li> <li><code>measurement.csv</code>: Description of each measurement (depth and temperature), linked to <code>profile.csv</code> via <code>borehole_id</code> and <code>profile_id</code>.</li> </ul> <p>For boreholes with many profiles (e.g. from automated loggers), pairs of <code>profile.csv</code> and <code>measurement.csv</code> are stored separately in subfolders of <code>data</code> named <code>{source.id}-{glacier}</code>, where <code>glacier</code> is a simplified and kebab-cased version of the glacier name (e.g. <code>flowers2022-little-kluane</code>).</p> <h3>Supporting information</h3> <p>The folder <code>sources</code>, available on <a href="https://github.com/mjacqu/glenglat">GitHub</a> but omitted from dataset releases on <a href="https://doi.org/10.5281/zenodo.11516611">Zenodo</a>, contains subfolders (with names matching column <code>source.id</code>) with files that document how and from where the data was extracted.</p> <h2>Tables</h2> <p>Jump to: <a href="#source"><code>source</code></a> · <a href="#borehole"><code>borehole</code></a> · <a href="#profile"><code>profile</code></a> · <a href="#measurement"><code>measurement</code></a></p> <h3><a name="source"></a><code>source</code></h3> <p>Sources of information considered in the compilation of this database. Column names and categorical values closely follow the Citation Style Language (CSL) 1.0.2 specification. Names of people in non-Latin scripts are followed by a latinization in square brackets (e.g. В. С. Загороднов [V. S. Zagorodnov]) and non-English titles are followed by a translation in square brackets. The family name of Latin-script names is wrapped in curly braces when it is not the last word of the name (e.g. Emmanuel {Le Meur}, e.g. {Duan} Keqin) or the name ends in two or more unabbreviated words (e.g. Jon Ove {Hagen}). The family name of a Chinese name (and of the latinization) is wrapped in curly braces when it is not the first character.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>id</code> (required)</td> <td>string</td> <td>Unique identifier constructed from the first author's lowercase, latinized, family name and the publication year, followed as needed by a lowercase letter to ensure uniqueness (e.g. Загороднов 1981 → zagorodnov1981a).</td> </tr> <tr> <td><code>author</code></td> <td>string</td> <td>Author names (optionally followed by their ORCID or contact email in parentheses) as a pipe-delimited list.</td> </tr> <tr> <td><code>year</code> (required)</td> <td>year</td> <td>Year of publication.</td> </tr> <tr> <td><code>type</code> (required)</td> <td>string</td> <td>Item type.<br>- article-journal: Journal article<br>- book: Book (if the entire book is relevant)<br>- chapter: Book section<br>- document: Document not fitting into any other category<br>- dataset: Collection of data<br>- map: Geographic map<br>- paper-conference: Paper published in conference proceedings<br>- personal-communication: Personal communication between individuals<br>- speech: Presentation (talk, poster) at a conference<br>- report: Report distributed by an institution<br>- thesis-phd: Doctor of Philosophy (PhD) thesis<br>- thesis-msc: Master of Science (MSc) thesis<br>- webpage: Website or page on a website</td> </tr> <tr> <td><code>title</code> (required)</td> <td>string</td> <td>Item title.</td> </tr> <tr> <td><code>url</code></td> <td>string</td> <td>URL (DOI if available).</td> </tr> <tr> <td><code>language</code> (required)</td> <td>string</td> <td>Language as ISO 639-1 two-letter language code.<br>- da: Danish<br>- de: German<br>- en: English<br>- es: Spanish<br>- fr: French<br>- ja: Japanese<br>- ko: Korean<br>- ru: Russian<br>- sv: Swedish<br>- zh: Chinese</td> </tr> <tr> <td><code>container_title</code></td> <td>string</td> <td>Title of the container (e.g. journal, book).</td> </tr> <tr> <td><code>volume</code></td> <td>integer</td> <td>Volume number of the item or container.</td> </tr> <tr> <td><code>issue</code></td> <td>string</td> <td>Issue number (e.g. 1) or range (e.g. 1-2) of the item or container, with an optional letter prefix (e.g. F1) or part number (e.g. 75pt2).</td> </tr> <tr> <td><code>page</code></td> <td>string</td> <td>Page number (e.g. 1) or range (e.g. 1-2) of the item in the container, with an optional letter prefix (e.g. S1).</td> </tr> <tr> <td><code>version</code></td> <td>string</td> <td>Version number (e.g. 1.0) of the item.</td> </tr> <tr> <td><code>editor</code></td> <td>string</td> <td>Editor names (e.g. of the containing book) as a pipe-delimited list.</td> </tr> <tr> <td><code>collection_title</code></td> <td>string</td> <td>Title of the collection (e.g. book series).</td> </tr> <tr> <td><code>collection_number</code></td> <td>string</td> <td>Number (e.g. 1) or range (e.g. 1-2) in the collection (e.g. book series volume).</td> </tr> <tr> <td><code>publisher</code></td> <td>string</td> <td>Publisher name.</td> </tr> </tbody> </table> <h3><a name="borehole"></a><code>borehole</code></h3> <p>Metadata about each borehole.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>id</code> (required)</td> <td>integer</td> <td>Unique identifier.</td> </tr> <tr> <td><code>source_id</code> (required)</td> <td>string</td> <td>Identifier of the source of the earliest temperature measurements. This is also the source of the borehole attributes unless otherwise stated in <code>notes</code>.</td> </tr> <tr> <td><code>glacier_name</code> (required)</td> <td>string</td> <td>Glacier or ice cap name (as reported).</td> </tr> <tr> <td><code>glims_id</code></td> <td>string</td> <td>Global Land Ice Measurements from Space (GLIMS) glacier identifier.</td> </tr> <tr> <td><code>location_origin</code> (required)</td> <td>string</td> <td>Origin of location (<code>latitude</code>, <code>longitude</code>).<br>- submitted: Provided in data submission<br>- published: Reported as coordinates in original publication<br>- digitized: Digitized from published map with complete axes<br>- estimated: Estimated from published plot by comparing to a map (e.g. Google Maps, CalTopo)<br>- guessed: Estimated with difficulty, for example by comparing <code>elevation</code> to a map (e.g. Google Maps, CalTopo)</td> </tr> <tr> <td><code>latitude</code> (required)</td> <td>number [degree]</td> <td>Latitude (EPSG 4326).</td> </tr> <tr> <td><code>longitude</code> (required)</td> <td>number [degree]</td> <td>Longitude (EPSG 4326).</td> </tr> <tr> <td><code>elevation_origin</code> (required)</td> <td>string</td> <td>Origin of elevation (<code>elevation</code>).<br>- submitted: Provided in data submission<br>- published: Reported as number in original publication<br>- digitized: Digitized from published plot with complete axes<br>- estimated: Estimated from elevation contours in published map<br>- guessed: Estimated with difficulty, for example by comparing location (<code>latitude</code>, <code>longitude</code>) to a map of contemporary elevations (e.g. CalTopo, Google Maps)</td> </tr> <tr> <td><code>elevation</code> (required)</td> <td>number [m]</td> <td>Elevation above sea level.</td> </tr> <tr> <td><code>mass_balance_area</code></td> <td>string</td> <td>Mass balance area.<br>- ablation: Ablation area<br>- equilibrium: Near the equilibrium line<br>- accumulation: Accumulation area</td> </tr> <tr> <td><code>label</code></td> <td>string</td> <td>Borehole name (e.g. as labeled on a plot).</td> </tr> <tr> <td><code>date_min</code></td> <td>date (%Y-%m-%d)</td> <td>Begin date of drilling, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).</td> </tr> <tr> <td><code>date_max</code></td> <td>date (%Y-%m-%d)</td> <td>End date of drilling, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).</td> </tr> <tr> <td><code>drill_method</code></td> <td>string</td> <td>Drilling method.<br>- mechanical: Push, percussion, rotary<br>- thermal: Hot point, electrothermal, steam<br>- combined: Mechanical and thermal</td> </tr> <tr> <td><code>ice_depth</code></td> <td>number [m]</td> <td>Starting depth of continuous ice. Infinity (INF) indicates that only snow, firn, or intermittent ice was reached.</td> </tr> <tr> <td><code>depth</code></td> <td>number [m]</td> <td>Total borehole depth (not including drilling in the underlying bed).</td> </tr> <tr> <td><code>to_bed</code></td> <td>boolean</td> <td>Whether the borehole reached the glacier bed.</td> </tr> <tr> <td><code>temperature_uncertainty</code></td> <td>number [°C]</td> <td>Estimated temperature uncertainty (as reported).</td> </tr> <tr> <td><code>notes</code></td> <td>string</td> <td>Additional remarks about the study site, the borehole, or the measurements therein as a pipe-delimited list. Sources are referenced by <code>source.id</code>. Quality concerns are prefixed with '[flag]'.</td> </tr> <tr> <td><code>curator</code></td> <td>string</td> <td>Names of people who added the data to the database, as a pipe-delimited list.</td> </tr> <tr> <td><code>investigators</code></td> <td>string</td> <td>Names of people and/or agencies who performed the work, as a pipe-delimited list. Each entry is in the format 'person (agency; ...) {notes}', where only person or one agency is required. Person and agency may contain a latinized form in square brackets.</td> </tr> <tr> <td><code>funding</code></td> <td>string</td> <td>Funding sources as a pipe-delimited list. Each entry is in the format 'funder [rorid] > award [number] url', where only funder is required and rorid is the funder's ROR (https://ror.org) ID (e.g. 01jtrvx49).</td> </tr> </tbody> </table> <h3><a name="profile"></a><code>profile</code></h3> <p>Date and time of each measurement profile.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>borehole_id</code> (required)</td> <td>integer</td> <td>Borehole identifier.</td> </tr> <tr> <td><code>id</code> (required)</td> <td>integer</td> <td>Borehole profile identifier (starting from 1 for each borehole).</td> </tr> <tr> <td><code>source_id</code> (required)</td> <td>string</td> <td>Source identifier.</td> </tr> <tr> <td><code>measurement_origin</code> (required)</td> <td>string</td> <td>Origin of measurements (<code>measurement.depth</code>, <code>measurement.temperature</code>).<br>- submitted: Provided as numbers in data submission<br>- published: Numbers read from original publication<br>- digitized-discrete: Digitized with Plot Digitizer from discrete points of depth versus temperature<br>- digitized-continuous: Digitized with Plot Digitizer from a continuous data source (e.g. line plot of depth versus temperature)</td> </tr> <tr> <td><code>date_min</code></td> <td>date (%Y-%m-%d)</td> <td>Measurement date, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).</td> </tr> <tr> <td><code>date_max</code> (required)</td> <td>date (%Y-%m-%d)</td> <td>Measurement date, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).</td> </tr> <tr> <td><code>time</code></td> <td>time (%H:%M:%S)</td> <td>Measurement time.</td> </tr> <tr> <td><code>utc_offset</code></td> <td>number [h]</td> <td>Time offset relative to Coordinated Universal Time (UTC).</td> </tr> <tr> <td><code>equilibrium</code></td> <td>string</td> <td>Whether and how reported temperatures equilibrated following drilling.<br>- true: Equilibrium was measured<br>- estimated: Equilibrium was estimated (typically by extrapolation)<br>- false: Equilibrium was not reached</td> </tr> <tr> <td><code>notes</code></td> <td>string</td> <td>Additional remarks about the profile or the measurements therein as a pipe-delimited list. Sources are referenced by <code>source.id</code>. Quality concerns are prefixed with '[flag]'.</td> </tr> </tbody> </table> <h3><a name="measurement"></a><code>measurement</code></h3> <p>Temperature measurements with depth.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>borehole_id</code> (required)</td> <td>integer</td> <td>Borehole identifier.</td> </tr> <tr> <td><code>profile_id</code> (required)</td> <td>integer</td> <td>Borehole profile identifier.</td> </tr> <tr> <td><code>depth</code> (required)</td> <td>number [m]</td> <td>Depth below the glacier surface.</td> </tr> <tr> <td><code>temperature</code> (required)</td> <td>number [°C]</td> <td>Temperature.</td> </tr> </tbody> </table>

opencc-by-4.0Jul 2024View details →

Dataset of "Thermal Stability of Valuable Metals in Lithium-Ion Battery Cathode Materials: Temperature Range 100-400 °C"

<p>Lithium is crucial in lithium-ion batteries (LIBs), serving as a main component of the electrolyte and cathode. Elements such as cobalt, nickel, and manganese are also vital for high performance, energy density, and stability. This study aimed to examine the behaviour of end- of-life cathode material (LiNi0.6Mn0.2Co0.2O2) and its valuable metals after exposure to temperatures between 100 and 400 °C, comparing it with untreated material. The lithium content cannot be reliably determined by conventional analytical methods, so inductively coupled plasma optical emission spectroscopy (ICP-OES) was chosen for this purpose. For ICP-OES measurements, samples were dissolved in different solvents for a specified time, and the concentrations of lithium, nickel, manganese, and cobalt were measured. From the measured values, their theoretical yields were calculated. Due to the annealing at given temperatures and subsequent dissolution, this step can be considered as the first stage of the pyrometallurgical- hydrometallurgical process used in battery recycling. The study was complemented by further analyses to monitor the effect of annealing temperatures on the properties of the material. Based on the results, it was found that the highest theoretical yield in this temperature range was for material annealed at 400 °C and dissolved in 20% nitric acid for 4 hours.</p>

opencc-by-4.0Jan 2024View details →

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2021-01-01 to 2021-12-31 [L2]

<p>Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level.</p> <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2021.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> <p>For more details read `FRCHEM_2021_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>

opencc-by-4.0Nov 2018View details →

SMAP L1B Brightness Temperatures Arctic

<p>This is a data set of polarised brightness temperatures (TBs) from the L-band (1.4 GHz) passive microwave sensor flying onboard the Soil Moisture Active Passive (SMAP) satellite. The data set was produced to enable a consistent combination of TBs from SMAP with those measured by the SMOS (Soil Moisture and Ocean Salinity) mission.</p> <p>It is based on the version 3 SMAP L1B brightness temperatures (Piepmeier et al., 2016), which are not corrected with respect to solar and cosmic radiation or atmospheric effects. The data are aggregated daily and gridded to a north polar EASE-grid 2.0 (Brodzik et al. 2012) with a grid size of 12.5 km. The data were produced within the framework of the EU Horizon2020 project SPICES and therefore only covers the period from the first available SMAP data to the end of the project (1 April 2015 to 31 May 2018).</p> <p>Within SPICES, SMAP and SMOS data were combined to a homogenized data set, which was then used to estimate sea ice thickness. For details see Schmitt and Kaleschke (2018) and the related data sets of SMOS TBs and SMOS/SMAP sea ice thickness.</p> <p>The files contain the following data fields:<br> <strong>Tbv</strong> - brightness temperatures at vertical polarisation<br> <strong>Tbh</strong> - brightness temperatures at horizontal polarisation<br> <strong>Tbv_std</strong> - weighted standard error of Tbv<br> <strong>Tbh_std</strong> - weighted standard error of Tbh<br> <strong>nmp</strong> - effective number of measurements used for averaging</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>

Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, turbidity, and fluorescent dissolved organic matter at discrete depths in Carvins Cove Reservoir, Virginia, USA in 2020-2025

We monitored water quality in Carvins Cove Reservoir (Roanoke, Virginia, USA; 37.3697 -79.958) with high-frequency (10-minute) sensors in 2020-2025. Carvins Cove Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source. This data package consists of datasets from two separate deployments. First, from July 2020 - August 2021, depth profiles of water temperature were measured on 1-meter intervals using HOBO temperature pendant loggers deployed from 0.1 m below the surface of the reservoir to 10 m depth, and also at 15 and 20 m depth. Additionally, water temperature was measured in the Sawmill Branch inflow at 0.5 m depth using HOBO temperature pendant loggers. Second, from 9 April 2021 - 31 December 2025, depth profiles of water temperature were measured on 1-meter intervals from 0.1 m below the surface of the reservoir to 11 m depth and additionally at 15 and 19 m. A YSI EXO2 sonde measured water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~1.5 m depth. A YSI EXO3 sonde measured water temperature, conductivity, specific conductance, total dissolved solids, dissolved oxygen, and fluorescent dissolved organic matter at ~9 m depth, which corresponds to the depth of a water outtake valve. The thermistors, EXO3 sonde, and pressure sensor were deployed at stationary, fixed elevations (referred to as positions) deployed off of the dam near the water outtake valves. Due to variable water levels in the reservoir, the depths of these sensors varied over time. In contrast, the EXO2 was deployed on a buoy from 2021-2022 and remained at 1.5 m depth as the water level fluctuated. However, in 2023, the buoy disappeared in a storm, and after that the EXO2 was deployed at a stationary elevation as the water level fluctuated around the sensor. The EXO2 was redeployed on the buoy in 2024. The monitoring site's maximum de

openCC (other)Oct 2023View details →

High-frequency dissolved oxygen, water temperature, wind speed, and radiation data; stream and in-lake nutrient concentration data; and daily metabolism and nutrient loading estimates for 16 lakes in North America and Northern Europe.

In lakes, ecosystem structure and processes are influenced by gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP). The rates of these metabolic processes are often controlled by resource availability, which often reflects catchment loads. Although the relationship between catchment loads and in-lake nutrient concentrations may be well defined in specific lakes, we explored how watershed vs. in-lake predictors of metabolism compare across lake types. To do this, we combined stream loads of carbon (C), nitrogen (N), and phosphorus (P) with high frequency in situ monitoring of lake metabolism and in-lake C, N, and P concentrations from 16 lakes spanning a range of latitudes (39 to 64 degrees N), inflowing stream (0 - 6 streams), and trophic status (oligotrophic to eutrophic). The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP, R, and NEP derived from those data. In addition, the data package includes in-lake and stream concentrations of dissolved organic carbon, total nitrogen, and total phosphorus and stream discharge data. The package also includes estimates of daily carbon, nitrogen and phosphorus loading to each lake derived from the stream concentrations and discharge.

openCC0Jan 2023View details →

Temperature and concentration of dissolved oxygen in river water measured in the Upper Clark Fork River (Montana, USA) during 2020 and 2021

The LTREB (Long Term Research in Environmental Biology) monitoring project is a portion of the $200 million-dollar (USD) superfund project for ecological restoration of the Upper Clark Fork River (UCFR), associated tributaries, and head water streams including Silver Bow and Warm Springs Creeks. Restoration along the Upper Clark Fork River includes removal of metal-laden floodplain soils, lowering of the floodplain to its original elevation, and re-vegetation of over 70 km of the river's floodplain closest to contaminant sources. The UCFR Long Term Research in Environmental Biology (LTREB) project includes bi-weekly water quality monitoring across a 200-km gradient of heavy metal contamination associated with historic mining. Monitoring includes inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and dissolved and whole-water heavy metal concentrations. The UCFR LTREB monitoring project is conducted within the first 200km of the UCFR and associated tributaries located in western Montana. The monitoring program began in 2017 and will be completed in the year 2023 with potential for funding extension. Surface water samples represented in this data product are collected from six sites on the mainstem of the UCFR. River water is measured at each monitoring site using miniDOT Loggers. Dissolved oxygen (DO) and Temperature (T) are recorded by the sensor at five-, 10-, or 15-minute intervals (as found in the raw data files), then interpolated as needed to five-minute intervals in the product data tables. The analysis-ready data of this dataset represent Quality Assurance and Quality Control (QAQC) -processed DO concentrations from six sites on the mainstem of the UCFR collected in 2020 and 2021.

openCC (other)Jul 2025View details →

Endurance swimming performance and physiology of juvenile Green Sturgeon (Acipenser medirostris) at different temperatures, CA, 2022

This dataset provides information on the endurance swimming performance and physiological responses of juvenile Green Sturgeon (Acipenser medirostris), reared and tested at the University of California, Davis, in 2022. Fish were acclimated to two temperature treatments (13°C and 18°C) for 14 days prior to swimming trials. Endurance tests were conducted at 47–53 days post-hatch (DPH) in modified swim tunnels at fixed water velocities (25–55 cm s⁻¹) to measure time-to-fatigue (End.min), station-holding behavior (Station_holding), and swimming type (Swim.type). Fish morphometrics (e.g., weight, fork length, total length) were recorded before trials. Post-swim physiological analyses included whole-body measurements of cortisol, glucose, lactate, and protein. Tissue homogenates were processed to determine concentrations normalized to fish weight (e.g., Cortisol_ng_g, Glucose_ug_g, Lactate_ug_g). Standard curves showed high assay linearity (R² > 0.98) and low variability (CV < 10%). This dataset contributes to understanding sturgeon endurance and physiological stress under different environmental conditions, providing insights into their resilience to temperature and flow changes relevant to river management and conservation efforts. Variables include: species, developmental stage (DPH), rearing and trial conditions (tank, temperature, velocity), fish morphometrics (weight, fork length, total length), and physiological metrics (cortisol, protein, glucose, and lactate).

Secchi depth data and discrete depth profiles of water temperature, dissolved oxygen, conductivity, specific conductance, photosynthetic active radiation, oxidation-reduction potential, and pH for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025

Discrete depth profiles of water temperature, dissolved oxygen, oxidation-reduction potential, conductivity, specific conductance, and pH were collected with multiple handheld water quality probes and discrete depth profiles of photosynthetically active radiation (PAR) were collected with a LI-COR underwater light meter from 2013 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia. All discrete depth profiles were collected on approximately 1-meter intervals. The data package consists of two datasets: 1) Secchi depth data; and 2) discrete depth profiles of multiple water quality variables measured by handheld sensors. The Secchi data and discrete depth profiles were measured at the deepest site of each reservoir adjacent to the dam, as well as other in-reservoir sites. Handheld sensor measurements were also collected at a gauged weir on the primary inflow tributary, other inflows, and outflows at Falling Creek Reservoir; inflows and outflows at Beaverdam Reservoir; and inflows at Carvins Cove Reservoir. In 2021, YSI handheld data were also collected from a littoral site in Beaverdam Reservoir. In 2025, YSI handheld data were collected monthly from June to October from nine littoral sites around the perimeter of Falling Creek Reservoir. From 2024 - 2025, additional within-reservoir depth profiles were collected in Carvins Cove Reservoir and multiple sites. Data were collected approximately fortnightly in the spring months (March - Ma

Time series of high-frequency profiles of depth, temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, turbidity, pH, oxidation-reduction potential, photosynthetically active radiation, colored dissolved organic matter, phycocyanin, phycoerythrin, and descent rate for Beaverdam Reservoir, Carvins Cove Reservoir, Falling Creek Reservoir, Gatewood Reservoir, and Spring Hollow Reservoir in southwestern Virginia, USA 2013-2025

Depth profiles of water biogeochemical properties were collected with SeaBird Electronics (SBE) Conductivity, Temperature, and Depth (CTD) profilers from 2013-2025 at five drinking water reservoirs in southwestern Virginia, USA. The study reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the town of Pulaski, Virginia. The dataset consists of CTD depth profiles measured at the deepest site of each reservoir adjacent to the dam as well as other upstream reservoir sites. The profiles were collected approximately fortnightly in the spring months, weekly in the summer and early autumn, and monthly in the late autumn and winter. Beaverdam Reservoir, Carvins Cove Reservoir, and Falling Creek Reservoir were sampled every year in the dataset (2013-2025); Spring Hollow Reservoir was only sampled 2013-2017 and 2019; and Gatewood Reservoir was only sampled in 2016. Data availability differs across years due to additional sensors that have been added or replaced over time. From 2013-2016, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor and an ECO FLNTU sensor for turbidity and chlorophyll. From 2017-2025, profiles were taken with a CTD equipped with an SBE 43 Dissolved Oxygen sensor, an ECO FLNTU sensor for turbidity and chlorophyll, a PAR-LOG ICSW sensor for photosynthetically active radiation, and a SBE 27 pH and ORP (oxidation-reduction potential) sensor. In 2022 and 2023, profiles were also taken with an additional CTD equipped with an SBE 43 Dissolved Oxygen sensor; an ECO Triplet Scattering Fluorescence sensor for

openCC (other)Oct 2025View details →

Continuous stream CO2 and temperature data and sensor calibration grab samples from five NEON sites (CARI, COMO, KING, MART, WALK), August 2021-April 2024.

This package contains: 1) sensor-based measurements of dissolved CO2 concentration and temperature, and 2) dissolved CO2 concentration from grab samples that were used to calibrate the sensor data, collected at five stream sites in the NEON network (CARI- Caribou Creek, AK; COMO- Como Creek, CO; KING- Kings Creek, KS; MART- Martha Creek, WA; and WALK- Walker Branch, TN) between August 2021 - April 2024. The grab sample dataset contains a combination of samples collected by NEON (DP1.20097.001) and additional samples collected by project personnel. All samples were collected using the headspace equilibration method, and dissolved CO2 concentrations were calculated using the 'neonDissGas' R package (https://github.com/NEONScience/NEON-dissolved-gas). The sensor dataset contains CO2 concentrations measured with an eosGP CO2 gas probe, averaged to 15-minute intervals and corrected to align with grab sample concentrations using a site-specific grab versus sensor regression. Due to inaccuracies in the eosGP temperature data, we instead include the temperature data from NEON that was used to convert CO2 between units of ppmv and umol/L (DP1.20053.001 for CARI, KING, MART, and WALK, and data from the multiparameter sonde for COMO). All NEON data used in this data package references the RELEASE-2025 version of each data product (downloaded February 2025).

openCC (other)Jul 2025View details →

Sub-Alpine Lake (>600 m) High-Frequency Water Temperature, DOC (2007-2021), and Weather Station (Fall 2023) Dataset, Maine, USA.

We collected high-frequency surface and bottom water temperature in a set of nine high-elevation lakes in Maine, USA from 2007-2021. High-elevation is defined >600m above sea level. Dissolved organic carbon concentration data for the same time period and set of lakes is modified from Nelson, S.J., R.A. Hovel, J.F. Daly, A.L. Gavin, S. Dykema, and W.H. McDowell. 2021. Northeastern Mountain Ponds Geochemistry Compilation 1978-2019 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/8b51d651da0e0cff8c6ad853ef69ec3b. Air temperature and precipitation data were collected from a weather station deployed in the Mountain Pond watershed in Fall 2023 to aid comparison with low and high resolution PRISM datasets.

openCC (other)Feb 2026View details →

Stage height, discharge, water temperature, pH, conductivity, and dissolved oxygen at Cascade Brook in Black Rock Forest, Cornwall, NY 1998 - 2015

Black Rock Forest established a stream monitoring station in the Cascade Brook watershed in 1998. The station is equipped with a 120-degree sharp-crested v-notch weir and was built to continuously monitor the flow, temperature, pH, conductivity, and dissolved oxygen content of the stream water in Cascade Brook in New York. The Cascade Brook watershed encompasses 135 hectares (334 acres), is generally a southern aspect, and has been little affected by human activity in the last century. The forest is deciduous and dominated by oak species. The station records data hourly, and while water chemistry sensors have been used intermittently through the decades, stage height and discharge has been measured consistently from1998 - 2015.

Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, pressure, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths in Falling Creek Reservoir, Virginia, USA in 2018-2025

We monitored water quality in Falling Creek Reservoir (Vinton, Virginia, USA; 37.30325 -79.8373) with high-frequency (10-minute) sensors in 2018-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. This data product consists of one dataset compiled of depth profiles of water temperature on 1-m intervals from 0.1 to 9 m depth; dissolved oxygen at 5 m and 9 m depth; pressure at 9 m depth; and temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, fluorescent dissolved organic matter, turbidity, and pressure at ~1.6 m depth. The dataset is accompanied by a sensor maintenance log and quality assurance/quality control analysis scripts.

Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths, and water level in Beaverdam Reservoir, Virginia, USA in 2009-2025

We monitored water level and water quality in Beaverdam Reservoir (Vinton, Virginia, USA; 37.31288, -79.8159) with visual observations and high-frequency (10- to 15-minute resolution) sensors in 2009-2025. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of three datasets: 1) bvre-waterlevel_2009_2025.csv, 2) bvre-sensorstring_2016_2020.csv, and 3) bvre-waterquality_2020_2025.csv. 1) bvre-waterlevel_2009_2025.csv contains water level observations of the staff gauge at a platform near the reservoir's dam by both the Western Virginia Water Authority and the Virginia Tech Reservoir Group LTREB field crew. This dataset spans 2009 to 2025, with data collection still ongoing. 2) bvre-sensorstring_2016_2020.csv consists of a water temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by intermittent data collected by a dissolved oxygen logger deployed at 5 m or 10 m. A sonde measuring water temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was additionally deployed at ~1.5 m depth. This dataset spans 2016 to 2020, with no additional data collection beyond the last observation. The third dataset is bvre-waterquality_2020_2025.csv, with data collection still ongoing and an accompanying maintenance log. This dataset contains: a) a temperature string with 13 temperature sensors deployed ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; b) two dissolved oxygen sensors, one in the middle of the string and one sensor above the sediments; and c) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 dataset is also included in this 2020-2025 d

openCC (other)Feb 2023View details →

Summary of soil temperature, moisture, and thaw depth for 14 chamber flux measurements sampled near Arctic LTER shrub sites at Toolik Field Station, Alaska, summer 2012.

Soil temperature at 5cm and 10cm depth, volumetric water content (VWC) and depth of thaw for 14 shrub canopy flux plots measured in vicinity of the Arctic LTER shrub site, Toolik Field Station, AK in 2012.