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10,553 results for “measurements”
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Werthmannstrasse (FRWRTM) from 2023-01-01 to 2023-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10.</p> <ul> <li>Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2023.</li> <li>Average, minimum and maximum in-canopy 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 `FRWRTM_2023_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Werthmannstrasse (FRWRTM) from 2022-01-01 to 2022-12-31 [L2]
<p>Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10.</p> <ul> <li>Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2022.</li> <li>Average, minimum and maximum in-canopy 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 `FRWRTM_2022_AirTemperature_MetaData.txt`.</p> <p>Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.</p>
Methane concentrations and oxidation rates in land-terminating glacial runoff: measurements from three glacial rivers and a paraglacial lake in Iceland and a literature review
<div> <p>This dataset contains methane measurements from Icelandic lakes and rivers during the summer of 2018 and 2019. This includes data from net methane oxidation assays with sediment and overlying water from one paraglacial lake and one glacial river, and surface methane concentration data from grab samples in 3 glacial streams and 15 Icelandic lakes (1 of which is paraglacial). The dataset also contains methane concentration data from a synthesis of relevant aquatic ecosystems, used to compare against the original measurements collected. </p> </div> <div> <p>Data and Literature Review Synthesis is supplement to Strock et al. 2024 <em>Oxidation is a potentially significant methane sink in land-terminating glacial runoff</em> published in Nature Scientific Reports. </p> <div> <p>This study was funded by: National Geographic Society Changing Polar Systems grant (CP4-162R-18); In-kind support from the U.S. Geological Survey; Dickinson College Research and Development; Churchill Exploration Fund at Dickinson College </p> </div> </div>
Dataset on full ultrasonic guided wavefield measurements of a CFRP plate with fully bonded and partially debonded omega stringer
<p>The fourth dataset dedicated to the <a href="http://openguidedwaves.de/">Open Guided Waves</a> platform presented in this work aims at a carbon fiber composite plate with an additional omega stringer at constant temperature conditions. The dataset provides full ultrasonic guided wavefields. </p> <p>A chirp signal in the frequency range 20-500 kHz and Hann windowed tone-burst signal with 5 cycles and carrier frequencies of 16.5 kHz, 50 kHz, 100 kHz, 200 kHz and 300kHz are used to excite the wave. The piezoceramic actuator used for this purpose is attached to the center of the stringer side surface of the core plate.</p> <p><br> Three scenarios are provided with this setup: (1) wavefield measurements without damage, (2) wavefield measurements with a local stringer debond and (3) wavefield measurements with a large stringer debond. The defects were caused by impacts performed from the backside of the plate. As result, the stringer feet debonds locally which was verified with conventional ultrasound measurements.</p> <p>The dataset can be used for benchmarking purposes of various signal processing methods for damage imaging.</p> <p>The detailed description of the dataset is published in Data in Brief Journal [3].</p>
BIP! DB: A Dataset of Impact Measures for Research Products
<h2>Overview</h2> <p>This dataset contains citation-based impact indicators (also referred as <em>measures</em>) for ~296M distinct persistent identifiers (PIDs) that correspond to various types of research products (publications, datasets, software, and other products).</p> <p>The calculated indicators are organized into categories based on the aspect of impact they capture. </p> <h3>Influence indicators</h3> <p>Reflect the "total" impact of a research product; how established it is in general.</p> <ul> <li><strong><em>Citation Count:</em></strong> The total number of citations of the product, the most well-known influence indicator.</li> <li><strong><em>PageRank score:</em> </strong>An influence indicator based on the PageRank (Page et al., 1999), a popular network analysis method. PageRank estimates the influence of each product based on its centrality in the whole citation network. It alleviates some issues of the Citation Count indicator (e.g., two products with the same number of citations can have significantly different PageRank scores if the aggregated influence of the products citing them is very different - the product receiving citations from more influential products will get a larger score). </li> </ul> <h3>Popularity indicators</h3> <p>Capture the "current" impact of a research product; how popular it currently is.</p> <ul> <li><strong><em>RAM score:</em></strong> A popularity indicator based on the RAM (Ghosh et al., 2011) method. It is essentially a Citation Count where recent citations are considered as more important. This type of "time awareness" alleviates problems of methods like PageRank, which are biased against recently published products (new products need time to receive a number of citations that can be indicative for their impact).</li> <li><strong><em>AttRank score:</em></strong><strong> </strong>A popularity indicator based on the AttRank (Kanellos et al., 2020) method. AttRank alleviates PageRank's bias against recently published products by incorporating an attention-based mechanism, akin to a time-restricted version of preferential attachment, to explicitly capture a researcher's preference to examine products which received a lot of attention recently.</li> </ul> <h3>Impulse indicators</h3> <p>Measure the initial momentum that a research product received right after its publication.</p> <ul> <li><em><strong>Incubation Citation Count (3-year CC):</strong> </em>This impulse indicator is a time-restricted version of the Citation Count, where the time window length is fixed for all products and the time window depends on the publication date of the product, i.e., only citations 3 years after each product's publication are counted.</li> </ul> <h3>FIeld-weighted indicators</h3> <p>Capture the impact of a research product relative to the average performance in its field, accounting for differences in citation practices across disciplines.</p> <ul> <li><strong>Field-Weighted Citation Impact (FWCI):</strong> A field-weighted indicator that measures how a research product performs compared to the global average in its research field. An FWCI of 1.0 indicates that the product is cited exactly as expected for similar publications in the same field; values above 1.0 indicate above-average impact, while values below 1.0 indicate below-average impact.</li> <li><strong>3-year FWCI:</strong> A time-restricted version of the FWCI that considers citations received within the first three years after publication. By limiting the citation window, this indicator captures the early relative impact of a research product, providing insight into how quickly it gains influence in its field.</li> </ul> <p>In our analysis, the expected number of citations for each research product is computed by <em>grouping them by concept, publication year, and product type and then averaging the citations within each group</em>. </p> <p><em>More details about the aforementioned impact indicators, the way they are calculated and their interpretation can be found <a href="https://bip.imsi.athenarc.gr/site/indicators">here</a> and in the respective references (Kanellos et al., 2019).</em></p> <h2>Indicator calculation levels</h2> <p>The impact indicators are calculated in two levels:</p> <ul> <li><strong>PID level: </strong> assuming that each PID corresponds to a distinct research product. Currently PIDs are DOIs, PMCIDs, and PMIDs.</li> <li><strong>OpenAIRE-id level: </strong>leveraging PID synonyms based on OpenAIRE's deduplication algorithm (Manghi et al., 2020) - each distinct article has its own OpenAIRE id.</li> </ul> <h2>Impact classes</h2> <p>Each researcj product is also assigned an impact class, reflecting its percentile rank among all products in the dataset: </p> <table style="border-collapse: collapse; width: 100%; height: 39.1876px;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;"><strong>Class</strong></td> <td style="height: 19.5938px;"><strong>Percentile</strong></td> <td style="height: 19.5938px;"><strong>Description</strong></td> </tr> <tr style="height: 19.5938px;"> <td style="height: 19.5938px;">C1</td> <td style="height: 19.5938px;">Top 0.01%</td> <td style="height: 19.5938px;">Exceptional impact</td> </tr> <tr> <td>C2</td> <td>Top 0.1%</td> <td>Very high impact</td> </tr> <tr> <td>C3</td> <td>Top 1%</td> <td>High impact</td> </tr> <tr> <td>C4</td> <td>Top 10%</td> <td>Good impact</td> </tr> <tr> <td>C5</td> <td>Rest 90%</td> <td>Remaining products</td> </tr> </tbody> </table> <h2>File structure</h2> <p>For each calculation level (PID / OpenAIRE-id) we provide five (5) compressed CSV files (one for each measure/score provided). The structure of the files differs slightly depending on the level:</p> <ul> <li> <p><strong>PID-level files:</strong> Each line follows the format:<br><code>identifier <tab> identifier_type <tab> score <tab> class</code></p> </li> <li> <p><strong>OpenAIRE-id-level files:</strong> These files contain the keyword "openaire_ids" in the filename. Each line follows the format:<br><code>identifier <tab> score <tab> class</code></p> </li> </ul> <p><em>The parameter setting of each measure is encoded in the corresponding filename. For more details on the different measures/scores see our extensive experimental study (Kanellos et al., 2019) and the configuration of AttRank in the original paper (Kanellos et al., 2020).</em></p> <h3>Topic-related files</h3> <p>In addition to the main indicator files, the dataset also includes <em>topic-level outputs</em>, providing <em>field-weighted impact indicators</em> as well <em>percentile classes</em> within the associated <em>2nd-level concepts from OpenAlex</em>. </p> <p>Specifically, we associated all research products with their 2nd level concepts from OpenAlex (using only their <em>DOIs</em>); we kept only the three most dominant concepts for each product, based on their confidence score, and only if this score was greater than 0.3.</p> <p>Since currently only the DOIs are used to associate concepts from OpenAlex to research products, all identifiers in these files refer to DOIs. </p> <ul> <li><strong>Topic-specific impact classes file:</strong> Fore each concept and indicator, precentile classes are computed and provided in <code>topic_based_impact_classes.txt</code> in the following format:</li> </ul> <p><code>identifier <tab> concept <tab> pagerank_class <tab> attrank_class <tab> 3-cc_class <tab> cc_class</code></p> <ul> <li><strong>Field-weighted indicator files:</strong> Each line follows the format:<br><code>identifier <tab> concept <tab> score</code></li> </ul> <p><em>Note that to prevent division by zero, the score column is left empty whenever the average score for a specific combination of concept, publication year, and product type equals zero.</em></p> <h2>Data sources</h2> <p>The data used to produce the citation network on which we calculated the provided measures have been gathered from the OpenAIRE Graph v10.5.0, including data from (a) <em>OpenCitations' COCI & POCI dataset</em>, (b) <em>MAG</em> (Sinha et al, 2015; Wang et al., 2019), and (c) <em>Crossref</em>. The union of all distinct citations that could be found in these sources have been considered. </p> <p>Additionally, all topic-related computations are derived from OpenAlex concepts.</p> <h2>Access and Use</h2> <p>Find our Academic Search Engine built on top of these data <a href="https://bip.imsi.athenarc.gr/">here</a>. Further note, that we also provide all calculated scores through <a href="https://bip-api.imsi.athenarc.gr/documentation">BIP! Finder's API</a>. </p> <p><em>Terms:</em> These data are provided "as is", without any warranties of any kind. The data are provided under the CC0 license.</p> <h2>Changelog</h2> <p><strong>v19.1</strong></p> <ul> <li>[major update] Added field-weighted indicators: FWCI and 3-year FWCI.</li> </ul> <p><strong>v19.0</strong></p> <ul> <li>Added PMCID as an additional type of PID.</li> </ul> <p><strong>v15.1</strong></p> <ul> <li>Fixed missing records that were unintentionally omitted in v15.0</li> <li>Ensures all popularity indicators correctly use <code>current_year = 2025</code></li> </ul> <p><strong>v12.0</strong></p> <ul> <li>Added PMIDs as an additional type of PID.</li> </ul> <p><strong>v10.0</strong></p> <ul> <li>[Major update] Introduced deduplication of research products using the latest <a href="https://graph.openaire.eu/docs/graph-production-workflow/deduplication/research-products">OpenAIRE article deduplication algorithm</a>. Each node in the citation network is now a deduplicated product having a distinct OpenAIRE id. <ul> <li>Corrected overcounting of citations caused by multiple versions of the same product.</li> <li>PID-level scores are now derived from deduplicated OpenAIRE nodes.</li> </ul> </li> <li>Added filtering rules described <a href="https://graph.openaire.eu/docs/graph-production-workflow/aggregation/non-compatible-sources/doiboost/#crossref-filtering">here</a> to remove from dataset PIDs with problematic metadata. </li> </ul> <p><strong>v9.0</strong></p> <ul> <li>[Major update] Introduced topic-specific impact classes for PID-identified products based on OpenAlex 2nd-level concepts.</li> </ul> <p><strong>v7.0</strong></p> <ul> <li>[Major update] Added impact class labels (C1-C5) for each procuct, indicating the percentile-bsaed impact levels. <ul> <li>Classes reflect relative position within the global score distribution.</li> </ul> </li> </ul> <p><strong>v5.1</strong></p> <ul> <li>[Major update] Introduced dual-level score computation: PID level and OpenAIRE ID level.</li> </ul>
PsPM-FSS6B: SCR and PSR measurements in a delay fear conditioning task with somatosensory CS and electrical US
<p>This dataset includes skin conductance response (SCR) and pupil size response (PSR) measurements. Also included are CS and US information, keypress responses, keypress response times and key correctness for each of 18 healthy unmedicated participants (10 males and 8 females aged 25.7+/-5.0 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. Simple and complex CS are delivered to the intermediate phalanges of the index and middle fingers of the non-dominant hand. Simple stimuli are stimulations to either index or middle finger, complex stimuli are stimulations of different temporal structure to both index and middle fingers. CS intensity is set to a perceivable but not unpleasant level. US is a train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA between the CS and US is 3.5 s. The ITI is randomly determined on each trial to be 7, 9, or 11 s.</p>
PsPM-LI: SCR, ECG, PSR and respiration measurements in a delay fear conditioning task with auditory CS and electrical US.
<p>This dataset includes pupil size response (PSR), skin conductance response(SCR), electrocardiogram (ECG) and respiration measurements for each of 20 healthy unmedicated participants (8 males and 12 females aged 22.8+/-3.3 years) participating in a classical (Pavlovian) discriminant delay fear conditioning task. (One additional participant in the initial sample in Korn et al. (2017) - but who did not finish the experiment and was not included into the analysis - is not contained in this dataset.) The acquisition data is separated into two sessions which were recorded consecutively with a break of approximately 5 min. CS consist of two sine tones with constant frequency (220 Hz or 440 Hz, 50-ms onset and offset ramp) and last for 6.5 s. US is a 0.5 s train of electric square pulses delivered with a constant current stimulator (Digitimer DS7A, Digitimer, Welwyn Garden City, UK) on participants' dominant forearm through a pin-cathode/ring-anode configuration. SOA betwen the CS and US is 6 s. The ITI is randomly determined on each trial to be 7, 9, or 11 s.</p>
MEaSUREs Greenland Surface Melt Daily 25km EASE-Grid 2.0, Version 1.1.1 (JJA 1980-2022)
<p>This data set offers users a 25 km daily record of surface/near-surface melting on the Greenland Ice Sheet. The presence of melting is determined from brightness temperature data acquired by three satellite-borne microwave radiometers: the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS).</p> <p>Included in this archive are files for the June-July-August (JJA) summer months during 1980-2022, formatted as a separate file for each year.</p> <p>Version 1.1 includes data for 2021-2022 to supplement the original 1980-2020 dataset from version 1.</p> <p>Version 1.1.1 corrects the 2022 file to include data for 2022-08-24 that was missing in version 1.1.</p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the Bare soil at Marquardt, Germany
<p>The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the ATB HYPERNETS site in Marquardt, Germany [52°27'59.40"N, 12°57'35.16"E] (ATGE). It is a subset of the complete data record, consisting of the measurements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 dgrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-XR sensor was installed on 11 Oct 2022 at the top of a 5m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 52.466778°N, 12.959778°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angle.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with a FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with a FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) which were propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full ATGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
How to measure work functions from aqueous solutions - data
<p>Data set pertaining to the article "How to measure work functions from aqueous solutions", <a href="https://doi.org/10.1039/D3SC01740K" target="_blank" rel="noopener">https://doi.org/10.1039/D3SC01740K</a> (Chemical Science <strong>14</strong>, 9574-9588 (2023)). A new protocol for energy referencing of photoemission data from liquids (<a href="https://doi.org/10.1039/D1SC01908B" target="_blank" rel="noopener">https://doi.org/10.1039/D1SC01908B</a>, Chemical Science <strong>12</strong>, 10558-10582 (2021)) is refined towards determining work functions from liquids.<br><br></p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus v2020.10 standard using the NXmpes user contributed format suggested by the Fairmat consortium, see<br>https://www.nexusformat.org/<br>https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br>A few extensions specific to liquid jet-experiments were added to the standard, and are explained in the notes-group on the top level of each file.<br>NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br>* nexpy (distributed with python)<br>* https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>In each NeXus file-entry, two types of spectra are included:<br>1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data').<br>2. As-measured data ('raw').</p> <p>Files with extension .txt are tab-separated ascii-files.</p> <p><br>The following files are provided:</p> <p>Photoemission data pertaining to solute measurements and reference measurements using a gold wire:<br>'Figure 3.h5'<br>'Figure 4.h5'<br>'Figure S1.h5'<br>'Figure S2.h5'<br>Kinetic energies are presented as measured. The scale offset of our spectrometer, determined as E_kin(corrected) = E_kin(measured) + 0.224 eV for data sets 'Figure 3.h5', 'Figure 4.h5' ,'Figure S2.h5', has not been taken into account.</p> <p>Numeric representations of the analysis results shown in the article's figures in graphical form:<br>'Figure 5.txt'<br>'Figure 6B.txt'<br>'Figure 7.txt'<br>'Figure S4B.txt'<br>'Figure S5.txt'</p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
2018-2020 Laboratory measurements of inorganic carbon accompanied by sensor data measurements of in situ inorganic carbon from the Upper Clark Fork River (Montana, USA)
These data were collected to support the Upper Clark Fork River restoration monitoring project supported by the US NSF Long Term Research in Environmental Biology (LTREB) and Consortium for Research in Environmental Water Systems (CREWS) programs. The LTREB monitoring project consists of monthly and bi-weekly water quality monitoring across a 215-km river restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, heavy metal contamination, organic and inorganic carbon concentrations, and physicochemical parameters. The original analytical intent for these data was to assess the accuracy of calculating the partial pressure of carbon dioxide (pCO2) from electrochemical and spectrophotometric pH along with total alkalinity (AT). These data correspond to two parts: a tank study and a field application. The tank study was a set of controlled laboratory experiments that took place in a well-mixed temperature-controlled tank of freshwater. Data for the tank study are primarily measurements of electrochemical and spectrophotometric pH, AT, electrical conductivity, temperature, and ionic strength. The field application was used to demonstrate the real-world applicability of the tank study results in the Upper Clark Fork River (USGS HUC 17010201) at the Gold Creek site southeast of Missoula, MT, USA. Data from the field application are primarily high frequency measurements of carbon dioxide, pH, temperature, and electrical conductivity. Additional miscellaneous data were collected for quality control. These field data were collected using field deployments of SAMI sensors from Sunburst Sensors (Missoula, Montana, USA). Electrical conductivity data were collected with a HOBO sensor from Onset Computer Corporation (Bourne, Massachusetts, USA).
NEON soil inorganic nitrogen measurements 2017-2020, derived data and code for Earth's Future manuscript
Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial-temporal scales. In a paper submitted for publication in Earth's future, we introduce a continentally distributed, publicly available dataset collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. To overcome methodological challenges and generate a standardized dataset, we produced a derived data version of soil inorganic N pools and net N transformation rate tables, which accounts for nitrite contamination in blanks. This derived dataset is then used to evaluate sources of variation within the NEON sampling design with mixed effects models, and we also compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2).
Time series of in situ Uv-Vis absorbance spectra and high-frequency predictions of total and soluble Fe and Mn concentrations measured at multiple depths in Falling Creek Reservoir (Vinton, VA, USA) in 2020 and 2021
High-frequency measurements of light absorbance were collected at multiple depths in Falling Creek Reservoir (FCR; Vinton, VA, USA) using a s::can Spectrolyser UV-Visible spectrophotometer coupled with a multiplexor pumping system. The system pumps water samples from individual depths into a flow-through cuvette where the UV-vis absorbance spectra of the sample are measured by the spectrophotometer. The system used in our study collected measurements of light absorbance every 2.5 nm wavelengths from 200 nm to 732.5 nm (optical path length of 10 mm) approximately at an hourly time step for seven monitoring depths in the reservoir. Data was collected during two periods; the first deployment (16 October to 9 November 2020) was to observe changes in Fe and Mn concentrations before, during, and after reservoir fall turnover and the second deployment (26 May to 21 June 2021) was to observe the effects of engineered hypolimnetic oxygenation on Fe and Mn concentrations. Partial least squares regression models were developed to generate predictions of total and soluble Fe and Mn concentrations based on the correlation between absorbance spectra and sampling data.
Code for Random Forest models that predict pharmaceutical and water chemistry measurements in Baltimore Ecosystem Study streams
This file contains code to model the relationship between the water chemistry measurements and discharge measured as part of BES routine sampling and the pharmaceuticals measured in WY 2018. We use Random Forest models to predict 1) total (i.e., summed) concentration of the pharmaceuticals for which we screened, 2) total nutrient concentrations (TN & TP), 3) whether or not the antibiotic trimethoprim was detected in a given sample, and 4) whether or not nitrate and TP were above or below environmentally-relevant threshold concentrations. We also use RF models to predict N and P concentrations over a longer period, in order to compare models for nutrients to pharma. Code and analyses here rely on data processed in the file "BESPharma_WY2018.Rmd", published on EDI (doi:10.6073/pasta/610cb67fcbc8982c2af8ed946dce8ea5) and BES water chemistry data published on EDI (doi:10.6073/pasta/ce7f30e6013e003bfe28c5fd7d4aed23 )
In Situ Carbon Dioxide and Methane Flux Measurements Using Opaque Chambers in a Sedge Fen Wetland (US-Los Lost Creek AmeriFlux Site, Wisconsin, Summer 2015)
This dataset contains in situ measurements of carbon dioxide (CO₂) and methane (CH₄) fluxes, collected using opaque closed chambers at the US-Los Lost Creek AmeriFlux fen shrub wetland site in northern Wisconsin during summer 2015. These data were collected to characterize variability of day-time mid-summer methane soil fluxes across the sampling area of the eddy covariance flux tower.
High-frequency measurements of chlorophyll fluorescence, characterizing F.I.Z. bias.
This dataset consists of high-frequency measurements of chlorophyll a fluorescence (Fchl), collected as part of manipulative experiments conducted in Lake George, NY. Experiments were set up to test for the potential of phototactic zooplankton to interfere with Fchl measurements. To test for any bias associated with fluorometer interference by zooplankton (FIZ), fluorometers were placed in the shallows of Lake George, and collected data under various treatment conditions. It was found that excitation light from fluorometers triggered a positive phototactic response during nighttime hours, biasing Fchl data by as much as 31x. Full results gleaned from this dataset can be found in: Moriarty, V.W., Lucius, M.A., Johnston, K.E., Borrelli, J.J., Mattes, B.M., Pezzuoli, A.R., Watson, C.D., Eichler, L.W. and Relyea, R.A. (2021), Fluorometer optical path interference via zooplankton phototaxis: Implications for high‐frequency data collection. Limnol Oceanogr Methods. https://doi.org/10.1002/lom3.10411
Stream temperature and discharge measured each summer for Oksrukuyik Creek at Dalton Road crossing, Arctic LTER, Toolik Field Station, Alaska, 1989-2019
Oksrukuyik Creek stage height and calculated discharge for the summer of 1989 to present. Stream temperature and discharge measured each summer for several streams in the Toolik area. Stream height is converted into stream discharge based on a rating curve calculated from manual discharge measurements throughout the season. The principal investigator in charge of the temperature and discharge measurements is Dr. Breck Bowden. Note: This file combines the previous individual yearly files.
Eddy Flux Measurements, Tussock Station, Imnavait Creek, Alaska - 2005
The Biocomplexity Station was established in 2005 to measure landscape-level carbon, water and energy balances at Imnavait Creek, Alaska. The station is now contributing valuable data to the Arctic Observing Network that was established at two nearby stations. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system.
Eddy Flux Measurements, Tussock Station, Imnavait Creek, Alaska - 2007
The Biocomplexity Station was established in 2005 to measure landscape-level carbon, water and energy balances at Imnavait Creek, Alaska. The station is now contributing valuable data to the Arctic Observing Network that was established at two nearby stations. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system.
Eddy Flux Measurements, Fen Station, Imnavait Creek, Alaska - 2007
In contribution to the Arctic Observing Network, the researchers have established two observatories of landscape-level carbon, water and energy balances at Imnaviat Creek, Alaska and at Pleistocene Park near Cherskii, Russia. These will form part of a network of observatories with Abisko (Sweden), Zackenburg (Greenland) and a location in the Canadian High Arctic which will provide further data points as part of the International Polar Year. This particular part of the project focuses on simultaneous measurements of carbon, water and energy fluxes of the terrestrial landscape at hourly, daily, seasonal and multi-year time scales. These are the major regulatory drivers of the Arctic climate system and form key linkages and feedbacks between the land surface, the atmosphere and the oceans. We will provide a comprehensive description of the state of the regional Arctic system with respect to these variables, its overall regulation and controlling features and its interaction with the global system. In support of these objectives, a 3m eddy covariance station was established on Imnaviat Creek, Alaska. This station has been continuously monitoring carbon dioxide, water vapor, energy fluxes and various micro-meteorological variables.
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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.