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7,355 results for “experiments”
FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment
<p>To assess the feasibility of producing FAIR data via the integration of a controlled vocabulary, an ontology, and an ELN, this dataset demonstrates the implementation of a tribological experiment while accounting for as many details as possible. The showcase experiment had a lubricated pin-on-disk arrangement, ran at 15 N normal load and a velocity range of 20 to 170 mm/s. With this dataset, we hope to provide a possible blueprint for FAIR data publication in experimental tribology.</p> <p><a href="http://www.nature.com/articles/s41597-022-01429-9">https://www.nature.com/articles/s41597-022-01429-9</a> - Garabedian, N.T., Schreiber, P.J., Brandt, N., Greiner, C., et al.</p> <p>Quick start with the dataset in README.txt (<em>included in the newest version of the dataset</em>)</p> <p>Abstract: Generating FAIR research data in experimental tribology. Sci Data 9, 315 (2022). Digital solutions for the generation of FAIR (Findable, Accessible, Interoperable and Reusable) data and metadata in experimental tribology are currently lacking, despite the looming challenge of integrating cutting-edge data science techniques – a promising scientific route for any field that often relies on phenomenology and empiricism. Additionally, the broad interdisciplinarity of tribology is probably a main contributing factor for the lack of community-wide data and metadata standards, and the heavy reliance on custom workflows and equipment. This paper, first, outlines a sample framework for scalable generation of FAIR data, and second, delivers a showcase FAIR data package for a pin-on-disk tribological experiment. The resulting curated data, consisting of 2,008 key-value pairs and 1,696 logical axioms, is the result of (1) the close collaboration with developers of a virtual research environment, (2) crowd-sourced controlled vocabulary, (3) ontology building and (4) numerous – seemingly – small-scale digital tools. Thereby, this paper demonstrates a collection of scalable non-intrusive techniques that extend the life, reliability and reusability of experimental tribological data beyond typical publication practices.</p> <p><a href="http://youtu.be/xwCpRDnPFvs">https://youtu.be/xwCpRDnPFvs</a> - Generating FAIR Research Data in Experimental Tribology - Get Scientific Results Ready for ML</p> <p><a href="https://doi.org/10.5281/zenodo.5720626">https://doi.org/10.5281/zenodo.5720626</a> - FAIR Data Package of a Tribological Showcase Pin-on-Disk Experiment</p> <p><a href="https://doi.org/10.5281/zenodo.5720198">https://doi.org/10.5281/zenodo.5720198</a> or <a href="https://github.com/nick-garabedian/TriboDataFAIR-Ontology">https://github.com/nick-garabedian/TriboDataFAIR-Ontology</a> or <a href="https://fairsharing.org/3597">https://fairsharing.org/3597</a> - TriboDataFAIR Ontology</p> <p><a href="https://doi.org/10.5281/zenodo.5720218">https://doi.org/10.5281/zenodo.5720218</a> or <a href="https://github.com/nick-garabedian/SurfTheOWL">https://github.com/nick-garabedian/SurfTheOWL</a> - SurfTheOWL</p> <p><a href="https://kadi4mat.iam-cms.kit.edu/">https://kadi4mat.iam-cms.kit.edu/</a> - Kadi4Mat Virtual Research Environment and Electronic Lab Notebook </p>
Peat characteristics, microbial PLFA, and fungal and actinobacterial sequences from Lakkasuo peatland drainage experiment, year 2004
<p>We analysed the response of microbial communities, characterized by phospholipid fatty acids (PLFAs), and fungal and actinobacterial communities, characterized by PCR-DGGE fingerprinting and direct sequencing, to changing hydrological conditions at three different sites in the boreal peatland complex Lakkasuo in southern Finland. Additionally, several peat characteristics were measured. The experimental design involved undrained controls as well as short-term (3 years) and long-term (43 years) water-level drawdown. The sites were, in their undrained state, a herb-rich sedge fen, a sedge fen, and a bog with hummock-lawn-hollow microtopography.</p> <p>Codes are explained in the Notes sheets of the Excel files. The contents of the csv files are identical to the corresponding Excel file data sheets.</p> <p>Please check the decimal separator! Comma is used in Finland, and that may have been carried over. All commas in data columns are decimal separators.</p>
Improving the Developer Experience with a Low-Code ProcessModelling Language: Companion site
<p>This companion site contains additional data to complement the paper:</p> <p><em><strong>Henriques, H., Lourenço, H., Amaral, V., and Goulão, M. (2018). Improving the developer experience with a low-code process </strong></em><em><strong>modelling</strong></em><em><strong> language. In ACM/IEEE 21st International Conference on Model Driven Engineering Languages and Systems (MODELS 2018), Copenhagen, Denmark. ACM. https://doi.org/10.1145/3239372.3239387</strong></em></p> <p><strong>Abstract</strong></p> <p><strong>Context</strong><strong>: </strong>The OutSystems Platform is a development environment composed of several DSLs, used to specify, quickly build and validate web and mobile applications. The DSLs allow users to model different perspectives such as interfaces and data models, define custom business logic and construct process models.</p> <p><strong>Problem</strong><strong>: </strong>TheDSL for process modelling (Business Process Technology (BPT)), has a low adoption rate and is perceived as having usability problems hampering its adoption. This is problematic given the language maintenance costs.</p> <p><strong>Method:</strong> We used a combination of interviews, a critical review of BPT using the “Physics of Notation” and empirical evaluations of BPT using the System Usability Scale (SUS)and the NASA Task Load indeX (TLX), to develop a new version ofBPT, taking these inputs and Outsystems’ engineers culture into account.</p> <p><strong>Results: </strong>Evaluations conducted with 25 professional soft-ware engineers showed an increase of the semantic transparency on the new version, from 31% to 69%, an increase in the correctness of responses, from 51% to 89%, an increase in the SUS score, from 42.25 to 64.78, and a decrease of the TLX score, from 36.50 to 20.78. These differences were statistically significant.</p> <p><strong>Conclusions:</strong> These results suggest the new version of BPT significantly improved the developer experience of the previous version. The end users background with OutSystems had a relevant impact on the final concrete syntax choices and achieved usability indicators.</p> <p> </p> <p><strong>Contents</strong></p> <p>This companion site provides a permanent link for additional data to the supported paper.</p> <p>This repository includes:</p> <ul> <li>Surveys and Questionnaires used in the evaluation reported in the paper <ul> <li>Survey on OutSystems BPT notations (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/survey.pdf">survey.pdf</a>)</li> <li>Prototype Symbol Set Questionnaire (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/PrototypeSymbolSetQuestionnaire.pdf">PrototypeSymbolSetQuestionnaire.pdf</a>)</li> <li>Original BPT Evaluation (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/languages.png">languages.png</a>)</li> <li>Usability Evaluation (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/sus.png">sus.png</a>)</li> <li>Cognitive Effort Evaluation (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/tlx.png">tlx.png</a>)</li> <li>Testing environment screenshot (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/Testing%20Environment%20Screenshot.png">Testing Environment Screenshot</a>)</li> </ul> </li> <li>Statistics <ul> <li>SUS and NASA TLX <ul> <li>Descriptive statistics (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXDescriptiveStats.pdf">SUSTLXDescriptiveStats.pdf</a>)</li> <li>Normality tests (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXNormalityTests.pdf">SUSTLXNormality.pdf</a>)</li> <li>Correlation test (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXCorrelation.pdf">SUSTLXCorrelation.pdf</a>)</li> <li>Scatterplot (<a href="https://zenodo.org/api/files/68bdc7fa-684a-496d-ab63-d956271f1f7d/SUSTLXScatterPlot.pdf">SUSTLXScatterplot.pdf</a>)</li> </ul> </li> </ul> </li> </ul> <p> </p>
FULFILL dataset - housing policy acceptability - framing experiment Latvia
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Latvia in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
FULFILL dataset - housing policy acceptability - framing experiment Italy
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Italy in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
FULFILL dataset - housing policy acceptability - framing experiment France
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in France in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
FULFILL dataset - housing policy acceptability - framing experiment Germany
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Germany in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
FULFILL dataset - housing policy acceptability - framing experiment Denmark
<p>This dataset represents survey data on sufficiency-oriented housing gathered in the second round of surveys in Denmark in 2023 within the FULFILL project - Fundamental Decarbonisation Through Sufficiency By Lifestyle Changes.</p> <p>As part of Work Package 3 (WP3) in the FULFILL project, we collected quantitative data from five countries: Denmark, France, Germany, Italy and Latvia. In this survey on sufficiency-oriented housing, we recruited a representative sample of approximately 750 to 800 respondents in Denmark, France, Germany and Denmark and around 550 in Latvia, taking into account primarily the individual perspective, added by some questions on the household level.</p> <p>The survey includes a framing experiment presenting two different ways of framing the aim of two sufficiency-oriented policies in the housing sector. In addition, the survey includes data on policy acceptability of these two policy measures and respondents’ preferences for combinations with different other policy measures. Further, the survey also measures socio-economic factors such as age, gender, income, education, household size, life stage, and political orientation. A quantitative assessment of the carbon footprint in the housing domain was also included.</p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output without snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 without snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured an ideal cold-air pool formation followed by a interuption by a wind disturbance around midnight. Simulation output for the same night, but with snow cover is also available (Rauchöcker et al., 2024a). The temperature evolution of the measurements agreed much better with the simulation with snow cover and otherwise the same model setting compared to the simulation without snow cover (Rauchöcker et al. 2024d). Also available in a different dataset is output from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the simulation without snow cover. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16_nosnow</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16_nosnow</em>. These variables were contained in the unprocessed<em> </em>output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16_nosnow.nc</em>.</p>
Sparse camera volumetric video applications. A comparison of visual fidelity, user experience , and adaptability. Supplementary Video
<p>This video is a supplementary video material to the paper "Sparse camera volumetric video applications. A comparison of visual fidelity, user experience , and adaptability". It shows a comparision of five volumetric videos scenes, captured with three different sparse volumetric video applications. This video aims to visualize the difference in fidelity and artifacts that each system expresses.</p>
A blind test on wind turbine wake modelling based on wind tunnel experiments: Phase I – The benchmark case
<p>This data set ("Data files.zip") contains the wind tunnel measurement data from Phase I of the Blind test on wind turbine wake modelling based on wind tunnel experiments organised during the TWEET-IE project (www.tweet-ie.eu).</p> <p>This updated version <strong>replaces</strong> the older versions 1.0.0 (https://doi.org/10.5281/zenodo.10566401), 1.1.0 (https://doi.org/10.5281/zenodo.11370112), 2.0 (https://doi.org/10.5281/zenodo.12188194) and 2.1 (https://doi.org/ 10.5281/zenodo.13918935). In comparison to the previous version 2.1 the data documentation has been updated to follow the template of the TWEET-IE project documents, indicating the Grant Agreement Number with the European Union and the Call Topic of the project.</p> <p>All tests were conducted in the closed-loop, low-speed boundary layer wind tunnel of the Chair of Aerodynamics and Fluid Mechanics at Technische Universität München (TUM). The experiments concerned two wind turbines, aligned with the flow, one downstream of the other, at a distance of 5 diameters. For Phase I, no control was applied to the wind turbine models, which were operating at constant RPM. The turbine models, designed and manufactured by TUM, were instrumented with multiple sensors and actuators and had a diameter of 1.1M. Measurements include velocity, power and loads on the turbines. A detailed description of the experimental set up can be found in the accompanying document ("Data documentation.pdf"). </p> <p>File "Submission procedure.zip" includes the format description and the templates of the output data that should be submitted by the participants in the blind test comparison.</p>
Meter-Scale Magma-Water Interaction Experiments
<p>These are video and other sensor data of experiments in which "magma" — that is: volcanic rock, re-melted at ca. 1300°C — interacts with liquid water. The experiments aim to better understand the escalation behavior of the processes involved when magma comes into contact with liquid water.</p> <p>The dataset will grow over time as data of new experiments is added.</p> <p><strong>Changes</strong></p> <ul> <li>Version 1.0: Add the <code>pr06</code> experiment.</li> <li>Version 0.11: Add the <code>pr05</code> experiment.</li> <li>Version 0.10: Add the <code>ir16</code> experiment.</li> <li>Version 0.9: Add the <code>ir15</code> experiment.</li> <li>Version 0.8: Add the <code>ir14</code> experiment.</li> <li>Version 0.7: Add the <code>ir13</code> experiment.</li> <li>Version 0.6: Add the <code>ir12</code> experiment.</li> <li>Version 0.5: Add the <code>ir07</code> experiment.</li> <li>Version 0.4: Add the <code>ir06</code> experiment.</li> <li>Version 0.3: Add the <code>ir05</code> experiment.</li> <li>Version 0.2: Add the <code>ir04</code> experiment.</li> <li>Version 0.1: Start with experiment <code>ir03</code>.</li> </ul>
Sparse observations induce large biases in estimates of the global ocean CO2 sink: an ocean model subsampling experiment
<p>Dataset underlying the analysis in Hauck et al., 2023: Sparse observations induce large biases in estimates of the global ocean CO<sub>2</sub> sink - an ocean model subsampling experiment, Philosophical Transactions A</p> <p>Surface ocean partial pressure of CO<sub>2 </sub>(pCO<sub>2</sub>) and air-sea CO<sub>2</sub> flux reconstructions, using two mapping methods (MPI-SOM-FFN, CarboScope) three different sampling masks: SOCAT, SOCAT+SOCCOM, IDEAL (based on bgcArgo, Roemmich et al., 2019).</p> <p>Also, all FESOM-REcoM output fields that were used in the reconstructions are provided.</p> <p>We further provide the three masks that were used for subsampling: SOCAT, SOCAT+SOCCOM, IDEAL (bgcArgo).</p> <p> </p>
SALLO validation experiment
<p>dataset containing psychophysical raw data and psychometric curves' points of subjective equality (PSE) obtained in the left-right discrimination and in the bisection tasks, repeatedly performed in the visual and in the acoustic domains, with the head turned at 45° left (-45°), center (0) and 45° right (+45). The clean dataset also contains the values of guess rate and lapse rate used to fit each psychometric curve.</p>
City of Seattle, Seattle Public Utilities, Marbled Murrelet Habitat Enhancement Experiment 2010, Cedar River Municipal Watershed, King County, WA
This experimental project aims to enhance nesting habitat for the marbled murrelet through active habitat restoration in second-growth forests. The project was conducted within the Cedar River Municipal Watershed (CRMW) in Washington State, with the goal of determining if silvicultural treatments, such as creating canopy gaps and tree topping, can accelerate the growth of tree branches suitable for murrelet nesting. The project was implemented in 2010 at a 75-acre site within CRMW. Treatments included removing surrounding trees to increase canopy openness ("gaps"), topping trees to stimulate branch growth, and combining both methods. The site was specifically chosen for its proximity to the murrelet detections in nearby old growth stands, and site suitability in terms of tree age, species composition, and manageable topography. Data collected focused on tree growth and structure characteristics critical to murrelet nesting. A total of 48 trees received treatments, which were systematically compared to untreated controls to assess outcomes. The initial implementation confirmed logistical feasibility and budget adherence, with plans for monitoring and resampling established for the 2020s. If successful, these techniques could be replicated across various environmental conditions to expand viable nesting habitat for the marbled murrelet, directly supporting conservation objectives outlined in the CRMW Habitat Conservation Plan.
Flume Experiment Testing the Impact of Artificial Streambank Roots on Velocity, Reynold's Shear Stress, and Turbulent Kinetic Energy using an Acoustic Doppler Profiler
The data published here is expected to accompany one publicly available dissertation (Chapter 4 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Impact of Flexible and Rigid Artificial Roots on Stream Hydrodynamics
Nutrient amendment effects on phytoplankton, water chemistry, and cyanotoxins in the 2018 Large-Scale Mesocosm Experiment at the University of Kansas Field Station
This dataset includes water physicochemical parameters, phytoplankton community composition, and cyanobacteria metabolites collected during a 21-day nutrient amendment experiment conducted from 23 July to 13 August 2018 at the University of Kansas Biological Station, Lawrence, KS, United States (39.049674°N, 95.190777°W). The experiment was performed using 18 large-scale, closed-bottom fiberglass tanks (volume: 11,000 L; height: 1.25 m; diameter: 3 m). Three tanks served as ambient controls (CON), while the others received one of the following nutrient treatments: nitrogen only (280 µM) as either ammonium chloride (NH4) or sodium nitrate (NO3); nitrogen (280 µM) plus phosphorus (200 µM) as either ammonium chloride + dipotassium phosphate (NHP) or sodium nitrate + dipotassium phosphate (NOP); and phosphorus only (200 µM) as dipotassium phosphate (P). Each tank received an initial nutrient dose on Day 0.5, followed by weekly additions of 20% of the initial amendment to maintain treatment conditions. All data were quality controlled to correct basic errors and to remove measurements outside the manufacturer’s standard operational ranges.
Invertebrate scavenger data from Tasmanian devil scavenging experiment 2023
We collected data to assess how disease-induced declines of an apex scavenger, the Tasmanian devil (Sarcophilus harrisii), affected carrion use by invertebrate scavengers. We manipulated devil access to pademelon (Thylogale billardierii) carcasses across a gradient of devil density from east to west Tasmania and measured carcass use by invertebrates. We used trap capture rates to estimate abundance of adult and larval carrion beetles (Ptomaphila lacrymosa) and blow fly larvae (Calliphoridae).
Semi-Arid Grassland Nitrogen Addition Experiment: New Mexico, 2018-2021
This dataset contains field and laboratory incubation measurements from a four-year nutrient addition experiment (2018–2021) conducted in three adjacent (<5 km apart) Chihuahuan Desert grasslands near Carlsbad Caverns National Park, New Mexico, USA (32°10′31″N, 104°26′38″W). The three replicate grassland sites were dominated by different grass species: Bouteloua gracilis (“Native Grama” site), Muhlenbergia setifolia (“Native Muhly” site), and Eragrostis lehmanniana (“Invasive Lovegrass” site). The Native Grama and Invasive Lovegrass sites were located on recently deposited alluvial soils classified as Entisols (Ustic Torrifluvents), formed from gravelly alluvium derived from limestone. Soils at the Native Muhly site were classified as shallow Aridisols formed from colluvium and residuum weathered from limestone and dolomite. Plot soils at the Native Grama and Invasive Lovegrass sites were sandy loam, while soils at the Native Muhly site were loam. Pre-treatment soil chemistry (collected May 2018 at 0–5 cm depth) was relatively consistent across sites, with pH ranging from 7.6 to 7.8 and similar inorganic N concentrations. Experimental field plots at each site received annual additions of nitrogen (+2 or +4 kg N ha⁻¹ yr⁻¹ as ammonium nitrate), carbon (+6 g m⁻² as sucrose), or no additions (ambient control). In 2020, a supplemental water treatment was applied only at the Native Grama site to simulate an additional 55 mm of rainfall during the monsoon season. Field data include measurements of soil chemistry (pH, inorganic nutrients, extractable organic C, total N), microbial biomass (C, N, P), extracellular enzyme activities, vegetation cover by functional group, species richness, Shannon diversity indices, and foliar chemistry (%C, %N, C:N ratios). Measurements were collected seasonally (pre-monsoon, monsoon, winter) or annually at peak biomass from 2018 through 2021. Laboratory incubations were conducted to complement field measurements. In 2019, a 30-day nitrogen t
Plant and carbon data, snowmelt manipulation experiment, Rocky Mountain Biological Laboratory (RMBL), 2023
These data are from a 2023 snowmelt manipulation experiment in Vera Meadow at the Rocky Mountain Biological Laboratory. We experimentally advanced the snowmelt date in a montane meadow by approximately 12 days using black shade cloths and assessed the effect on plant and carbon dynamics. We measured net ecosystem exchange, gross primary productivity, and soil respiration using a Li-COR 7500 five times biweekly from June to August, plant community composition using the pin-drop method five times biweekly from June to August, and root biomass nine times using bulk soil cores. Using drone imagery, we measured the Normalized Difference Vegetation Index (NDVI). This data package is completed.
ScienceDex guides
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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.