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30,948 results for “Profiler”
Supplementary Material for "Advancing quantum technology workforce: industry insights into qualification and training needs" and "Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles"
<p>This is a file collection as supplementary material for the paper <em>Advancing quantum technology workforce: industry insights into qualification and training needs, <a href="https://doi.org/10.1140/epjqt/s40507-024-00294-2">doi 10.1140/epjqt/s40507-024-00294-2</a>.</em> It consists of:</p> <ol> <li>Interview guide: questions and more as guideline for the interviews conducted for the industry needs analysis documented in the publication.</li> <li>Interview transcript extracts: anonymised phrases from the interviews that are given as quotes (in a shortened/liguistically smoothed out form) in the publication as well as further phrases that are refered in the results sections of the publication.</li> <li>Dataset of the follow-up survey</li> </ol> <p>The results of this study were also used to update the <a href="https://doi.org/10.5281/zenodo.10976836" target="_blank" rel="noopener">European Competence Framework for Quantum Technologies Version 2.5</a>, which is documented in <em>Extending the European Competence Framework for Quantum Technologies: new proficiency triangle and qualification profiles, <a href="https://doi.org/10.1140/epjqt/s40507-024-00302-5">doi 10.1140/epjqt/s40507-024-00302-5</a></em>. In an additional sheet, the three draft versions of qualification profile descriptions (v2.1, v2.2, v2.3) are provided.</p>
Greenhouse gas profiles from the 2021 HEMERA-TWIN balloon launch
<p>The dataset contains mixing ratio observations of o long-lived greenhouse gase carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and (SF6) from the HEMERA TWIN gonodola launch from Kiruna (Sweden) on 12. August 2021. Profile data for CO2 and CH4 from AirCore sampling and CH4 data from the PICO IR laser spectrometer cover altitudes from the PBL to 32km. 14 cryogenic air samples collected between 14 and 31 km altitude have been analysed for all four gases.</p>
Supplementary materials for: Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria through shallow electrical resistivity profiling
<p>Supplementary materials for the paper Imaging the Devene fault system beneath the Iskar floodplain in Bulgaria, submitted to Review of the Bulgarian Geological Society </p> <p>We used shallow electrical resistivity profiling to image the Nivyanin fault zone from the Devene fault system in NW Bulgaria. We aimed to verify whether a portion of<br>the Devene fault system has affected Quaternary fluvial deposits. The Supplementary materials contain the coordinates (WGS84) of measuring sensors and resistivity data in Boundless Electrical Resistivity Tomography (BERT) file format. The file bert.cfg.txt is the configuration file for running BERT software to obtain the resistivity model in figure 1c in paper.</p>
Accepted Artifact for Profiling and Optimizing Java Streams
<p>The accepted artifact for the journal article "Profiling and Optimizing Java Streams" published in Volume 7, Issue 3 of <a href="https://programming-journal.org/"><em>The Art, Science, and Engineering of Programming</em></a>.</p> <p>Please refer to the README.md to work with the artifact and reproduce results from the article.</p>
Data set for publication: Determination of Virulence-Associated Genes and Antimicrobial Resistance Profiles in Brucella Isolates Recovered from Humans and Animals in Iran Using NGS Technology
<p>This dataset includes information on resistance profiling, as well as antimicrobial resistance (AMR) genes and virulence-related factors that were identified in <em>Brucella</em> isolates recovered from humans and animals in different regions of Iran using classical phenotyping and next-generation sequencing (NGS) technology.</p>
Snowpack temperature profile dataset from a boreal forest watershed in eastern Canada.
<p>This dataset presents snow temperature profiles from nine different boreal forest sites in eastern Canada collected over two consecutive winters, 2016-17 and 2017-18. The dataset includes snowpack temperature profiles, snow depth, air temperature, and soil temperature. In addition, the two flux towers provided us with the additional heat and water vapour fluxes. We also present data extracted from intensive snow coring and snowpit surveys, conducted on a weekly and bi-weekly basis. Additionally, stable water isotope data collected from individual snowpack are presented here. </p> <p>To learn more about the additional information about the data, please read the "Readme.txt" file. </p>
Discrimination of Classical and Atypical BSE by a Distinct Immunohistochemical PrPSc Profile
<p>Bovine spongiform encephalopathy (BSE) is a fatal neurodegenerative disease in cattle belonging to the group of transmissible spongiform encephalopathies. Hallmark of the disease is the accumulation of the pathological prion protein (PrPSc) in the brain. Classical BSE (C-type) and two atypical BSE forms (L- and H-type) are known, and can be discriminated by biochemical characteristics. The data presented here underline that immunohistochemistry can also be used to identify type-specific PrPSc profiles which can be used for discriminatory purposes. For this brain samples from 21 cattle, intracerebrally inoculated with C-, H-, and L-type BSE, were used as well as three orally C-type BSE infected animals. Using six brain regions distinct lesion (H&E staining) and PrPSc profiles were determined. While the neuroanatomical distribution of lesions and the PrPSc accumulation were highly consistent between the groups, the topographic and cellular PrPSc profile revealed characteristic pattern for the different BSE types.</p>
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em> </em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users’ profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> </p>
Minimal data set for "Cohort profile: The ENTWINE iCohort Study, a multinational longitudinal web-based study of informal care"
<p><strong>Title:</strong></p> <p>Minimal Data Set for the Reproduction of Findings in "Elayan et al., Cohort Profile: The ENTWINE iCohort Study, a Multinational Longitudinal Web-Based Study of Informal Care".</p> <p> </p> <p><strong>Study Summary:</strong></p> <p>The data sets provided herein are derived from the ENTWINE iCohort Study, a multinational web-based cohort study employing an intensive longitudinal design. The study integrates a two-wave panel survey (baseline and 6-month follow-up) with optional weekly diary assessments. The cohort comprises caregivers and care recipients from nine countries: the United Kingdom, the Netherlands, Italy, Sweden, Israel, Germany, Greece, Poland, and Ireland. The study aimed to examine the influence of personal, psychological, social, economic, and geographic factors on caregiving experiences.</p> <p>Participants were eligible if they met the following criteria: 1) residency in a participating country; 2) capability to respond to surveys in English, Swedish, German, Dutch, Italian, Greek, Hebrew, or Polish; 3) access to the internet and ability to use it; 4) at least 18 years of age; 5) self-declared cognitive and physical capacity to complete the surveys; 6) either providing care to an adult (aged ≥ 18 years) with a chronic health condition, disability, or other care need, or receiving care from an adult due to similar conditions.</p> <p>The detailed methodology and results of the study can be found in the associated manuscript. For the complete survey questionnaires, please refer to: Morrison V, Zarzycki M, Vilchinsky N, Sanderman R, Lamura G, Fisher O, et al. A Multinational Longitudinal Study Incorporating Intensive Methods to Examine Caregiver Experiences in the Context of Chronic Health Conditions: Protocol of the ENTWINE-iCohort. Int J Environ Res Public Health. 2022;19. doi: <a href="https://doi.org/10.3390/ijerph19020821">10.3390/ijerph19020821</a></p> <p> </p> <p><strong>Data files:</strong></p> <p>The repository contains the following data files:</p> <ol> <li>"cg_minimal_dataset" (available in dta, sav, rds, and xlsx formats): This is a minimal data set containing de-identified and processed data derived from the ENTWINE iCohort Caregiver Baseline Survey. The variables present in this data set are detailed in the associated codebook, "cg_minimal_dataset_codebook".</li> <li>"cr_minimal_dataset" (available in dta, sav, rds, and xlsx formats): This is a minimal data set containing de-identified and processed data derived from the ENTWINE iCohort Care Recipient Baseline Survey. The variables present in this data set are detailed in the associated codebook, "cr_minimal_dataset_codebook".</li> </ol>
PITS Apparent Depth Profiles for Mars Global Cave Candidate Catalog (MGC3) Features
<p>Apparent depth profiles calculated by the Pit Topography from Shadows (PITS) tool for the majority of the features in the Mars Global Cave Candidate Catalog (MGC<sup>3</sup>). PITS is a Python framework for automatically calculating apparent depth profiles for Martian and Lunar pits from just a single cropped satellite image. These images can also be single- or multi-band, such as in the case of the Mars Reconnaissance Orbiter (MRO) HiRISE camera. You can learn more about PITS by reading its <a href="https://academic.oup.com/rasti/article/2/1/492/7241547">journal article</a> in RAS Techniques and Instruments, going to its <a href="https://github.com/dlecorre387/Pit-Topography-from-Shadows/">GitHub repository</a> or reading the following <a href="https://www.danlecorre.com/post/first-paper-published">post</a>.</p> <p>Since not all catalogued cave candidates on Mars will be pits, PITS has so far been applied to the following MGC<sup>3</sup> subcategories:</p> <ul> <li>Atypical Pit Craters (APCs).</li> </ul> <p>With plans to extend this to:</p> <ul> <li>Lava tube skylights,</li> <li>small rimless pits,</li> <li>generic, amorphous pits,</li> <li>and polar pits.</li> </ul> <p>This totals 123 apparent depth profiles in CSV format, which have been derived automatically by PITS for 88 APCs. Therefore, these profiles can be plotted as the user prefers, and/or used in combination with other data to reveal more about this particular APC on the surface of Mars.</p> <p>Each depth profile's CSV file is named according to the HiRISE Reduced Data Record Version 1.1. (RDRV11) that it was calculated upon (e.g. ESP_011386_2065_RED_profile.csv for the red-band version of the HiRISE image ESP_011386_2065). Where there are multiple MGC3 APCs contained within a single image, the file names are numbered generally from the most northern to southernmost, or most westerly to easterly. ESRI shapefiles for the location of all APCs in each HiRISE image have been provided in polygon (containing the extents used to crop the larger HiRISE product) and point format in order to give context in these intances. </p> <p>As the headers suggest, the first four columns represent the shadow length (<em><span class="math-tex">\(L\)</span></em>), apparent depth (<em><span class="math-tex">\(h\)</span></em>), and the upper/lower bounds of <span class="math-tex">\(\Delta h\)</span>, respectively, before they have been corrected for non-zero emission angles (<span class="math-tex">\(\varepsilon\)</span>) at the time of image acquisition. Whereas the latter four columns represent the same quantities after <span class="math-tex">\(\varepsilon\)</span>-correction. How this correction is derived and applied is explained in the PITS journal article linked above.</p>
Characterization of investments profiles on the energy transition for european citizens
<ul> <li><strong>Name</strong>: Characterization of investments profiles on the energy transition for european citizens</li> <li><strong>Summary</strong>: The dataset contains: (1) surveyee consent form for the study, (2) different scenarios about the energy transition, (3) determinant factors about those scenarios, (4) socioeconomic description of the surveyee, (5) investment decisions, (6) and household characterization/description. </li> <li><strong>License</strong>: cc-BY-SA</li> <li><strong>Acknowledge</strong>: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li><strong>Disclaimer</strong>: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European commission (Ec). EASME or the Ec are not responsible for any use that may be made of the information contained therein.</li> <li><strong>Collection Date</strong>: 22/07/2022</li> <li><strong>Publication Date</strong>: 15/10/2023</li> <li><strong>DOI</strong>: 10.5281/zenodo.4455198</li> <li><strong>Other repositories:</strong></li> <li><strong>Author</strong>: University of Deusto</li> <li><strong>Objective of collection</strong>: This data was originally collected to analyze quantitatively the decisions of everyday people in relation to their energy consumption and their reactions to specific political interventions.</li> <li><strong>Description:</strong> The dataset contains a CSV file file containing data collected from a survey about energy consumption investments. The fields that can be found for each entry are (1) Different scenarios about the energy transition and reactions to those scenarios, (money spent on energy investments, decisions about scenarios, actions taken under a blackout, etc.) (2) Determinant factors about the chosen scenarios in the previous question, which include different choices that could affect your decision about a scenario (3) socioeconomic information about the user (age, country of residence, studies), (4) estimation of the prices of various technologies related to the energy transition and (5) descriptive statistics about the household living situation (gender of user, people living in household, yearly rent, average savings per month, type of house, size of house) and also includes questions about climate change expertise. Next you can found a description of each field in the dataset <ul> <li><strong>Section 1 - Scenarios for energy transition.</strong> <ul> <li><strong>ID90.</strong> Rank in order of priority, from top to bottom, in which scenario you will be willing to live or to contribute/invest to make it possible. </li> <li><strong>ID36, ID38, ID43, ID44, ID72. </strong>Percentage of money people are willing to spend/save out of their income per scenario</li> <li><strong>ID191, ID192</strong>.. Amount of money people would spend based on an assumed case.</li> <li><strong>ID191, ID192. </strong>Priority service provision in case of Intermittent energy service. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority for you and 10 stars means it is absolutely necessary for you.</li> <li><strong>[ID325, ID326, ID327, ID328, ID329, ID330, ID331, ID332, ID333, ID334, ID335, ID336, ID337, ID338, ID339, ID340, ID341, ID133, ID242]</strong>. Priority service provision in case of <em>Intermittent energy service</em>. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[<strong>ID251, ID256, ID257, ID292, ID293, ID294, ID295, ID296, ID297, ID298, ID299, ID301, ID302, ID303, ID304, ID305, ID306, ID250, ID251</strong>]. Priority service provision in case of <em>full </em><em>black-outs</em>. Rating energy services from 0 to 10 stars, where 0 stars means it is extremely low priority and 10 stars means it is absolutely necessary.</li> <li>[<strong>ID141, ID5, ID147</strong>]. Used for statements that best represent survey responder</li> </ul> </li> <li><strong>Section 2 - Determinants (factors).</strong> Questions used to rate (from 0 to 100) factors that may influence the decision-making process contributing to make an ideal scenario possible. <ul> <li><strong>ID100</strong> Risk profile</li> <li><strong>ID101</strong> Added value</li> <li><strong>ID102</strong> Self-Satisfaction</li> <li><strong>ID103</strong> Technical Fit</li> <li><strong>ID104</strong> Own competence</li> <li><strong>ID105</strong> Knowledge</li> <li><strong>ID106</strong> Cost-Efficiency</li> <li><strong>ID107</strong> Safety</li> <li><strong>ID108</strong> Trust</li> <li><strong>ID109</strong> Autarky</li> <li><strong>ID110</strong> Legal</li> <li><strong>ID111</strong> Climate Protection</li> <li><strong>ID112</strong> Wellbeing</li> <li><strong>ID113</strong> Coziness</li> <li><strong>ID114</strong> Rights and Duties</li> <li><strong>ID115</strong> Peer-Pressure</li> <li><strong>ID116</strong> Socialising</li> <li><strong>ID117</strong> Support</li> <li><strong>ID118</strong> Agreement</li> <li><strong>ID119</strong> Brag</li> <li><strong>ID120</strong> Fun</li> <li><strong>ID121</strong> Novelty</li> <li><strong>ID122</strong> Trends</li> <li><strong>ID123</strong> Authority</li> <li><strong>ID124</strong> Own Significance</li> <li><strong>ID125</strong> Poseur</li> <li><strong>ID2</strong> Frugality</li> <li><strong>ID3</strong> Environmental concerns</li> <li><strong>ID31</strong> Adherence</li> <li><strong>ID52</strong> Commitment</li> <li><strong>ID97</strong> Profits</li> <li><strong>ID99</strong> Credit Score</li> </ul> </li> <li><strong>Section 3 - “Socio-economic” description. </strong>Questions about the socio-economic information of the survey respondents for data stratification. The indentation represents the dependency of questions and whether this data was asked <ul> <li><strong>ID164</strong> Understanding of questions</li> <li><strong>ID300</strong> Country of residence</li> <li><strong>ID137</strong> Age</li> <li><strong>ID178</strong> Highest level of education</li> <li><strong>ID136</strong> Willingness to provide data on the investment decision (respond apply for -Investment decision section)</li> </ul> </li> <li><strong>Section 4 - Investment decision</strong>. Questions about specific prices of potential purchases-decisions related to four scenarios (respondent's lifestyle) <ul> <li>Appliances <ul> <li><strong>ID42</strong> Affordable cost of a Regular refrigerator</li> <li><strong>ID45</strong> Energy efficient refrigerator costs</li> <li><strong>ID50</strong> Willingness to purchase an energy efficient refrigerator <ul> <li><strong>ID65</strong> Why no</li> <li><strong>ID66</strong> affordable cost of an energy efficient option</li> <li><strong>ID67</strong> Years to amortize an efficient option</li> </ul> </li> </ul> </li> <li>Insulation <ul> <li><strong>ID47</strong> Affordable cost of updating to a state of the art insulation on the facade</li> <li><strong>ID56</strong> Willingness for paying/invest <ul> <li><strong>ID74</strong> Why no?</li> <li><strong>ID20</strong> affordable cost of an energy efficient option</li> <li><strong>ID34</strong> Years to amortize an energy efficient option</li> </ul> </li> </ul> </li> <li>Energy Generation <ul> <li><strong>ID68</strong> Affordable cost of a solar photovoltaic system</li> <li><strong>ID76</strong> Willingness for paying/invest <ul> <li><strong>ID84</strong> Why no?</li> <li><strong>ID132</strong> Affordable cost of a photovoltaic system</li> <li><strong>ID138</strong> Years that amortize a photovoltaic system</li> </ul> </li> </ul> </li> <li>Energy Storage <ul> <li><strong>ID142</strong> Affordable cost of an energy storage system</li> <li><strong>ID146</strong> Willingness for paying/invest <ul> <li><strong>ID181</strong> Why no? </li> <li><strong>ID182</strong> Affordable cost of an energy storage system </li> <li><strong>ID183</strong> Years that amortize an energy storage systems</li> </ul> </li> </ul> </li> <li>Heating <ul> <li><strong>ID140</strong> Affordable cost of a gas boiler</li> <li><strong>ID209</strong> Affordable cost of an energy efficient heating system</li> <li><strong>ID217</strong> Willingness for paying/invest <ul> <li><strong>ID238</strong> Why no?</li> <li><strong>ID239</strong> Affordable cost of a energy efficient option</li> <li><strong>ID241</strong> Years that amortize a heat pumps</li> </ul> </li> </ul> </li> <li>Mobility <ul> <li><strong>ID41</strong> Average kilometers traveled a typical day</li> <li><strong>ID51</strong> Usual travel option</li> <li><strong>ID264</strong> Affordable cost of a diesel or gasoline mid-range brand new car</li> <li><strong>ID265</strong> Affordable cost of a mid-range brand new electric car</li> <li><strong>ID281</strong> Willingness to buy an electric car <ul> <li><strong>ID289</strong> Why no?</li> <li><strong>ID290</strong> Affordable price of an electric car</li> <li><strong>ID291</strong> Years that amortize an electric car</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>Section 5 - Household characterization</strong> <ul> <li><strong>ID127</strong> Selecting an asked value</li> <li><strong>ID189</strong> Type of living area</li> <li><strong>ID202</strong> Gender identity</li> <li><strong>ID1</strong> Those living in the house</li> <li><strong>ID32</strong> Number of inhabitants</li> <li><strong>ID220</strong> Average neat yearly income</li> <li><strong>ID229</strong> Average monthly saving</li> <li><strong>ID240</strong> Type of housing</li> <li><strong>ID249</strong> Owner / co-owner</li> <li><strong>ID255</strong> Usable area of the property (m²)</li> <li><strong>ID263</strong> Insulation level</li> <li><strong>ID270</strong> Climate zone</li> <li><strong>ID86</strong> Level of self-awareness about climate change. On scale of 0-10, where 0 is “climate change does not exist” and 10 is “I am a climate change expert/activist”</li> <li><strong>ID87</strong> Level of awareness of climate change among your peers or relatives, On a scale of 0-10, where 0 is “climate change does not exist” and 10 is “They are climate change experts/activists”</li> <li><strong>ID88</strong> Level of self-awareness about energy transition. On a scale of 0-10, where 0 is “It is the first time I hear about it” and 10 is “I am an expert or activist”</li> <li><strong>ID89</strong> Level of awareness of energy transition among your peers or relatives On a scale of 0-10, where 0 is “It is the first time they hear about it” and 10 is “They are experts or activists”</li> <li><strong>ID190</strong> feedback about survey</li> </ul> </li> </ul> </li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps:</strong> anonymization, data fusion, imputation of gaps.</li> <li><strong>Reuse:</strong> NA</li> <li><strong>Update policy:</strong> No more updates are planned</li> <li><strong>Ethics and legal aspects:</strong> Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a random value hash.</li> <li><strong>Technical aspects</strong>: </li> <li><strong>Other:</strong></li> </ul>
Flume Erosion Testing of Unamended and Organic Matter Amended Soil Samples Using an Acoustic Doppler Profiler, 2021
This data accompanies a publication titled "Soil Amended with Organic Matter Increases Fluvial Erosion Resistance of Cohesive Streambank Soil". Briefly, fluvial erosion testing was conducted on soil samples using an indoor flume channel. Soil samples were previously collected from the riparian zone of a river near Virginia Tech's campus in Blacksburg, VA, USA. The soil was subsequently air-dried and stored until use. Prior to erosion testing, soil samples were amended with varying amounts of organic matter (0%, 1%, and 4% OM by mass), compacted to a bulk density of 0.95 KilogramsPerCubicCentiMeters in growth containers, and allowed to mature in a greenhouse setting for 50 days prior to flume erosion testing. An Acoustic Doppler Profiler (ADP) was used to measure soil erosion and collect three-dimensional velocity data during erosion tests; raw velocity and soil depth data for each sample tested were stored in MATLAB files. Follow testing, the soil remaining from each sample was collected, stored, and analyzed for aggregate stability, soil organic matter (SOM), and extracellular polymeric substances (EPS). Additionally, soil temperature, water temperature, and volumetric water content were also measured prior to or during erosion testing. Data collected from this study, and the accompanying ADP MATLAB files, are presented here.
Seismic profiles, diatom and microfossil assemblages, and radiocarbon ages for constructing a post-glacial sea level curve from Fiordland, New Zealand
Two research cruises (12PL027 and 13PL018) were conducted aboard the University of Otago RV Polaris II in 2012 and 2013 to collect marine sediment cores and 2d seismic data as part of a collaborative research effort to study late-Pleistocene and Holocene environmental change in New Zealand. Data includes 4 Boomer seismic profiles, 4 CHIRP seismic profiles, seismic line GPS tracks, diatom assemblages (raw counts and relative abundances) and microfossil assemblages (raw counts and relative abundances), and radiocarbon ages from 4 marine sediment cores. The data used to construct a post-glacial sea level curve for Fiordland, New Zealand are also included, as well as data from published literature plotted for global comparisons. These data accompany the publication: Dlabola. E.K., Wilson, G.S., Gorman, A.R., Riesselman, C.R., and Moy, C.M. 2015. A post-glacial sea-level curve from Fiordland, New Zealand. Global and Planetary Change, 131, 101-114. https://doi.org/10.1016/j.gloplacha.2015.05.010
Lake Tahoe CTD Profile data
CTD profiles taken at Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Lake Tahoe UV Radiation Profile data
UV and PAR profiles taken at Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Lake Tahoe Particle size distribution (PSD) Profile data
Profiles of particle size distribution (PSD) taken at Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Hourly time series of Ives Lake (Huron Mountains, Marquette County, MI) Water Temperature-Depth Profiles, 2013-2022 (continuing study)
Long-term measurements of lake temperatures are essential to providing insights into local and regional changes in climate since large, still water bodies effectively act as a high-frequency filter. Ives Lake is a 30.7 m deep water body in northern Marquette County, in Michigan's Upper Peninsula. Beginning in 2013, temperature readings have been collected hourly from a string of twelve sensors located near the deepest point of the lake (approximately 46.84874 N lat, 87.84895 W long; identified by sonar survey in 2010 by first author). Measurements are continuing. The purpose of the project is to collect a data record of sufficient duration to determine if water temperatures are warming, and if the dates of autumn lake turnover are shifting toward later in the year.
Missouri reservoir profile data including temperature, depth, and oxygen profiles (1989-2022)
This dataset of limnological profiles starts in 1989 and is from 250 reservoirs in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Physical parameters derived from sensors include temperature and oxygen with depth. After 2017, sondes were upgraded to Yellow Springs Instruments (YSI) EXO3s; profiles include a range of physical, chemical, and biological parameters including depth, conductivity, pH, oxidative-reductive potential (ORP), chlorophyll a, phycocyanin (PC), phycoerythrin (PE), and turbidity. Most of the profiles were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of profiles were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat most of the time, but a few profiles were taken from bridges and docks. The bulk of the data come from the Statewide Lake Assessment Project (SLAP), funded by the Missouri Department of Natural Resources. The profiles are divided into a single file for 1989-2016 and then annual compiled datasets for 2017 and beyond. This data has been quality controlled for basic errors and any data outside of normal factory issued sensor ranges.
The Jefferson Project 2021 water quality data from three vertical profiler stations in Lake George, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2021, The Jefferson Project deployed three vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2021. These vertical profiler stations are named VP_AnthonysNose, VP_HarrisBay, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter or less depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data are transferred in near real-time to an off-site database for monitoring and review. The data provided here have undergone data correction by Jefferson Project researchers.
Missouri reservoir profile data including depth, temperature, oxygen, photopigments, conductivity, pH, turbidity, and oxidative-reductive potential starting in 2023
This dataset of annual limnological profiles starts in 2023 and is from reservoirs in the state of Missouri (MO) in the USA collected by the University of Missouri Limnology Lab. Physical parameters derived from sensors include temperature and oxygen with depth. Sondes used were Yellow Springs Instruments (YSI) EXO3s; profiles include a range of physical, chemical, and biological parameters including depth, conductivity, pH, oxidative-reductive potential (ORP), chlorophyll a, phycocyanin (PC), and turbidity. After May 2024, the turbidity sensor was replaced with a phycoerythrin (PE) sensor. Most of the profiles were collected during the summer months (May-September) when the reservoirs were thermally stratified, but a few were taken during the rest of the year (October-April). The majority of profiles were taken at the deepest point in the reservoir directly up-reservoir of the dam. Sampling was conducted from a boat. The bulk of the data come from the Statewide Lake Assessment Project (SLAP), funded by the Missouri Department of Natural Resources. The profiles are divided into single files for each year. This data has been quality controlled for basic errors and any data outside of normal factory issued sensor ranges.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.