Find research datasets worth reusing

opencc-by-4.0Jul 2019View details →

Data set supporting journal article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019

This data set contains evapotranspiration data obtained by a conventional eddy covariance set-up and a low-cost eddy covariance set-up as described in the research article: Markwitz, C. and Siebicke, L.: "Low-cost eddy covariance: a case study of evapotranspiration over agroforestry in Germany", Atmos. Meas. Tech., 2019. The data set contains all necessary data needed to replicate figures and analysis presented in the research article. The data sets are sorted and named according to the figure the data were used for.

opencc-by-4.0Jun 2019View details →

Data Supporting The role of dopant segregation on the oxygen vacancy distribution and oxygen diffusion in CeO2 grain boundaries

Data supporting the article "The role of dopant segregation on the oxygen vacancy distribution and oxygen diffusion in CeO2 grain boundaries, accepted for publication in the Journal of Physics: Energy (<a href="https://doi.org/10.1088/2515-7655/ab28b5">doi.org/10.1088/2515-7655/ab28b5</a>). Data includes inputs for molecular dynamics simulations using the DL_POLY classic code.Simulations can be rerun from the inputs provide.  Input data required for the generation of the grain boundary structures is included and uses the METADISE code.

opencc-by-4.0Aug 2019View details →

Supporting data: The seasonal origins of streamwater in Switzerland

These data support the analysis conducted in "The seasonal origins of streamwater in Switzerland", accepted for publication in Geophysical Research Letters (<a href="https://doi.org/10.1029/2019GL084552">https://doi.org/10.1029/2019GL084552</a>) by Scott T. Allen, Jana von Freyberg, Markus Weiler, Gregory R. Goldsmith, James W. Kirchner. Information on the study sites, sampling procedures, and analysis can be found in the main text and supporting information files. Here we provide the data used: Data Table S1. Annual precipitation and discharge isotope values, precipitation sine fit parameters, and typical isotope ratios of summer and winter precipitation.  Data Table S2. Streamwater grab sample SOIq values with matched daily discharge rates. See the Methods section for further details.

opencc-by-4.0Aug 2019View details →

Data supporting the "Health Libraries Sharing Game"

This dataset provides the necessary files to reproduce the "Health libraries sharing game". This game was created for the workshop "Health libraries: sharing through gaming" held in Basel on Wednesday the 19th of June 2019, as part of the EAHIL conference. Inspired by the game "Bucket of Doom", this game aims to help health librarians address challenging professional situations (based on real situations experienced by the authors). The players will have to be creative to overcome each challenge.  The game is composed of cards that include possible situations to resolve, some tools, as well as resources available to solve those questions. The cards are accompanied by a "How to play" file explaining the rules of the game and a "Name sheet" file with the combination of funny names that participants can choose at the beginning of the game. The README.txt file describes the files and the content of the different folders of the dataset. Please consult the following article for further information about the creation of the game: Gómez-Sánchez, A., Kerdelhue, G., Isabel-Gómez, R., González-Cantalejo, M., Iriarte, P., & Muller, F. (2019). Health libraries: sharing through gaming. Journal of EAHIL, 15(3), 8-11. https://doi.org/10.32384/jeahil15329

Hourly U.S. Building Electricity Use, Cost, and Emissions Baselines to Support Time-Sensitive Analyses of Energy Efficiency and Flexibility Measures

These data underpin an analysis of the time-sensitive impacts of energy efficiency and flexibility measures in the U.S. building sector using Scout (<a href="https://scout.energy.gov">scout.energy.gov</a>), a reproducible and granular model of U.S. building energy use developed by the U.S. national labs for the U.S. Department of Energy's Building Technologies Office. The analysis applies sub-annual adjustments to U.S. baseline building energy use, cost, and emissions in order to characterize how these metrics vary across hour of the day, season, and geographic region in the U.S. building sector. These adjustments are based on daily energy load, price, and emissions shapes from various data sources and are used to re-apportion baseline energy, cost, and emissions totals from <a href="https://www.eia.gov/outlooks/aeo/data/browser/%20/%20%7b%20/%20# \ }/?id=2-AEO2018 \ { \ & \ }cases=r ef2018 \ { \ & \ }sourcekey=0">EIA's Annual Energy Outlook (AEO) Reference Case projections</a> across all hours of a year. The resulting sub-annual baselines are specified by building sector, end use, region, and season and can be used in analyses of building efficiency and flexibility measures to quantify their time-sensitive impacts at the national scale. Analyses of these data demonstrate that energy efficiency measures continue to show strong value under a time-sensitive framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency. The data uploaded below include CSV files that show hourly energy use, cost, and emissions totals for the U.S. building sector as well as by end-use, region, and season. An additional CSV includes residential and commercial price intensities (USD/quad) for all hours of the day based on different time-of-use (TOU) rate data from the U.S. Utility Rate Database (URDB). Further detail on each of these CSVs is given below: <ul> <li>'TSV_baseline_totals.csv': this file shows hourly total energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030. It presents these estimates in Quads (source), Quads (site), and TWh (site). For the cost totals, it presents two estimates for each year and building sector, including one using the median TOU rate from the URDB and one using the average retail rate for the corresponding building sector. For converting source energy to site, total delivered electricity and electricity-related losses data for the residential and commercial sector are drawn from <a href="https://www.eia.gov/outlooks/aeo/data/browser/#/?id=2-AEO2018&sourcekey=0">AEO Summary Table A2</a>.</li> <li>'TSV_baseline_end-use.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential buildings in 2018 and 2030 broken out by building end-use. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region.csv': this file shows hourly energy, cost, and emissions estimates for commercial and residential space heating and cooling end uses in 2018 and 2030 for each <a href="https://www.eia.gov/consumption/residential/maps.php">American Institute of Architects (AIA) climate zone</a>. It presents totals in terms of both source and site energy as above and presents cost totals based on the median TOU rate for each building sector from the URDB.</li> <li>'TSV_baseline_region_season.csv': this file shows a similar disaggregation of the data as ‘TSV_baseline_region.csv’, but it further disaggregates results by season. The seasonal definitions are as follows: 'intermediate' (October to November; March to April), 'winter' (November to February), and 'summer' (May to September).</li> <li>'TSV_annual_price_intensities.csv': this file presents annual hourly price intensities for the commercial and residential building sectors in 2018 and 2030 based on different TOU rate data from the URDB. Three different rate structures are included for each building sector, and these are the 5th, 50th, and 95th percentile of all existing commercial and residential TOU rates in the URDB in terms of their peak to off-peak price ratio.</li> </ul>

Virtual tide gauges for predicting relative sea level rise supporting data

Data and results from the publication   Hawkins R., Husson L., Choblet G., Bodin T. and Pfeffer J.,   "Virtual tide gauges for predicting relative sea level rise",   JGR: Solid Earth,   2019 (submitted)   Software available from  https://github.com/rhyshawkins/TransTessellate2D/

Research data supporting for "Characterization of recovery onset by subgrain and grain boundary migration in experimentally deformed polycrystalline olivine"

Abstract: To apprehend plate tectonics and the dynamics of the lithosphere–asthenosphere boundary, composed principally of olivine, we need to understand the mechanisms that control plastic deformation of olivine in the relevant temperature domain. After more than 50 years of laboratory studies and investigations on natural rocks, the interplay of several key parameters (e.g. temperature, pressure, vacancy concentration, dislocation densities, grain size, strain rate) controlling polycrystalline olivine plasticity remains difficult to assess. Here, we study four olivine polycrystals, which have been deformed in axial compression under a confining pressure of 300MPa, at 1273 or 1473 K. Despite significant differences in mechanical properties (stress–strain curves), previous characterization by scanning (SEM) and transmission electron microscopy (TEM) did not reveal significant differences in dislocation microstructures which could explain these contrasted behaviours. We have undertaken automatic crystallographic orientation mapping (ACOM) analyses in TEM to increase the spatial resolution of characterization compared to previously obtained electron backscatter diffraction maps to further decipher the microstructures at nanoscale. With this novel technique applied to olivine, a noticeable difference in the onset of microstructural recovery has been identified between specimens deformed at 1273 and 1473 K. The microstructures of the olivine polycrystals deformed at 1473K exhibit numerous curved grain and subgrain boundaries, advocating for recovery by boundary migration. In contrast, the microstructures of the olivine polycrystals deformed at 1273K have significantly fewer subgrain boundaries and show more straight boundaries (i.e. closer to an equilibrium microstructure) than in the specimen deformed at 1473 K. Characterization by ACOM-TEM has permitted the identification of the onset of recovery, which is led by boundary migration even for very low macroscopic finite strains.

Supporting data: "How collective asperity detachments nucleate slip at frictional interfaces"

This repository supports: T.W.J. de Geus, M. Popović, W. Ji, A, Rosso, M. Wyart. How collective asperity detachments nucleate slip at frictional interfaces. Proc. Natl. Acad. Sci. U.S.A. 2019. <a href="https://dx.doi.org/10.1073/pnas.1906551116">doi: 10.1073/pnas.1906551116</a>, <a href="http://arxiv.org/abs/1904.07635">arXiv: 1904.07635</a> In particular, it provides all used data, all codes used to produce this data (including clones to all the used open-source libraries), and simple functions to plot the data. All data and code is free to use under the CC-BY-4 license, but: Please cite the above research article when using code or data (inspired) from this repository (or the open-source projects <a href="https://www.github.com/tdegeus/GooseFEM">GooseFEM</a> and <a href="https://www.github.com/tdegeus/GMatElastoPlasticQPot">GMatElastoPlasticQPot</a>), in addition to this dataset (<a href="https://dx.doi.org/10.5281/zenodo.3477938">doi: 10.5281/zenodo.3477938</a>). (c) T.W.J. de Geus | 2019 | contact: <a href="/Volumes/data/dataset/Geus_PNAS/tom%40gems.me">tom@geus.me</a>, <a href="http://www.geus.me">www.geus.me</a> This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. Contents <ol> <li>In brief</li> <li>Data files</li> <li>Code</li> <li>Plots</li> </ol> 1. In brief All codes (<code>codes/</code>) are written in C++ using a number of open-source libraries (<code>libraries/</code>). All data (<code>data/</code>) is stored in the HDF5 format. All plots (<code>data/</code>) are generated using Python and a number of open-source libraries. All codes are developed and tested on macOS and Linux. The notation used here is consistent with these Unix-based platforms. Windows based compilation and use might differ from the description here. 2. Data files ("data/") The different ensembles (datasets) are included in different directories in <code>data/</code>. They are distinguished through their directory name that comprises the system size (denoted <code>nx=...</code>) and the shape factor of the Weibull distribution from which the yield strains are drawn (denoted <code>weibull=...</code>). Each ensemble consists of a number of realisations of the random yield strains at the frictional interface. Each realisation is stored in a separate file (<code>id=xxx.hdf5</code>). This file serves as input for the event-driven code (<code>code/Run/main.cpp</code>). This code stores the displacement field at the end of each event-driven step (for which it may take significant time for energy to be minimised). With these displacement fields, all other quantities (stress, strain, plastic strain, ...) can be reconstructed. The relevant reconstructed data for the entire ensemble is collected in <code>EnsembleInfo.hdf5</code>. For the manually triggered avalanches at different stresses (and fixed relative strain increment w.r.t. the last system spanning event) only selected output is stored to limit storage usage (<code>code/AvalancheAfterPush...</code>). Please note that the simulations are stopped when an event becomes system spanning to save on computation time, for this case the output thus does not correspond to a state of mechanical equilibrium. By contrast, any simulation that was not system-spanning does correspond to a state of mechanical equilibrium. 2a. Realisation ("data/.../id=xxx.hdf5") See code <code>code/Run/main.cpp</code> and generation <code>code/Generate/generate.py</code> <ul> <li>Mesh (input)   <ul> <li><code>/coor</code>: Nodal coordinates <code>[nnode, ndim]</code> (<code>ndim == 2</code>)</li> <li><code>/conn</code>: Connectivity <code>[nelem, nne]</code> (<code>nne = 4</code>)</li> <li><code>/dofs</code>: Degrees-of-freedom (DOF) per node <code>[nnode, ndim]</code></li> <li><code>/iip</code>: Prescribed DOFs <code>[n_iip]</code>  </li> </ul> </li> <li>Material model (input)   <ul> <li><code>/elastic/elem</code>: Elastic elements <code>[n_elasic]</code></li> <li><code>/elastic/G</code>: Shear modulus <code>[n_elasic]</code></li> <li><code>/elastic/K</code>: Bulk modulus <code>[n_elasic]</code></li> <li><code>/cusp/elem</code>: Elasto-plastic elements <code>[n_cusp]</code></li> <li><code>/cusp/G</code>: Shear modulus <code>[n_cusp]</code></li> <li><code>/cusp/K</code>: Bulk modulus <code>[n_cusp]</code></li> <li><code>/cusp/epsy</code>: Yield strains <code>[n_cusp, n_potentials]</code></li> <li><code>/uuid</code>: Unique identifier for the realisation Note that <code>n_elasic + n_cusp == nelem</code>  </li> </ul> </li> <li>Simulation (input)   <ul> <li><code>/alpha</code>: Background damping coefficient <code>[nelem]</code> (homogeneous)</li> <li><code>/rho</code>: Mass density <code>[nelem]</code> (homogeneous)</li> <li><code>/run/dt</code>: Time-step</li> <li><code>/run/epsd/kick</code>: Size of the strain kick</li> <li><code>/run/epsd/max</code>: Local strain at which to stop  </li> </ul> </li> <li>Output   <ul> <li><code>/completed</code>: Completion signal, emitted when <code>/run/epsd/max</code> was reached locally</li> <li><code>/stored</code>: Stored event-driven step numbers <code>[n_event]</code></li> <li><code>/t</code>: Time at the end of each event-driven step <code>[n_event]</code></li> <li><code>/kick</code>: Strain kick (yes/no) per event-driven step <code>[n_event]</code></li> <li><code>/disp/...</code>: Nodal displacements per event-driven step <code>[nnode, ndim]</code></li> </ul> </li> </ul> 2b. Simulation output ("data/.../EnsembleInfo.hdf5") See code and help <code>code/EnsembleInfo/main.cpp</code>. 2c. Distribution P(x) ("data/.../EnsembleYieldDistance*.hdf5") See code and help <code>code/EnsembleYieldDistance_stressControl/main.cpp</code> and <code>EnsembleYieldDistance_strainControl/main.cpp</code>. 2d. Manual triggering of events ("data/.../AvalancheAfterPush*.hdf5") See code and help <code>code/AvalancheAfterPush_stressControl/main.cpp</code> and <code>code/AvalancheAfterPush_strainControl/main.cpp</code>. 3. Code ("code/") The relevant codes to generate the datasets are referenced above. All non-standard libraries have been cloned under <code>libraries/</code>. Please note that they are subject to evolution: their cloned versions allow one to rerun the code in this dataset, however, for further development one is strongly encouraged to use the latest version. Please check out the development of: <ul> <li><a href="https://github.com/tdegeus/GooseFEM.git">GooseFEM (v0.2.3)</a></li> <li><a href="https://github.com/tdegeus/GMatElastoPlasticQPot.git">GMatElastoPlasticQPot (v0.2.1)</a></li> <li><a href="https://github.com/tdegeus/cpppath.git">cpppath (v0.0.7)</a></li> <li><a href="https://github.com/xtensor-stack/xtensor.git">xtensor (v0.20.8)</a></li> <li><a href="https://github.com/xtensor-stack/xtensor-blas.git">xtensor-blas (v0.16.1)</a></li> <li><a href="https://github.com/xtensor-stack/xtl.git">xtl (v0.6.5)</a></li> <li><a href="https://github.com/xtensor-stack/xsimd.git">xsimd (v7.2.5)</a></li> <li><a href="https://github.com/BlueBrain/HighFive.git">highfive (master)</a></li> <li><a href="https://github.com/docopt/docopt.git">docopt (master)</a></li> <li><a href="https://github.com/fmtlib/fmt.git">fmt (master)</a></li> <li><a href="https://github.com/tdegeus/pyxtensor.git">pyxtensor (v0.0.5)</a></li> <li><a href="https://github.com/tdegeus/GooseMPL.git">GooseMPL (v0.2.24)</a></li> <li><a href="https://github.com/tdegeus/GooseEYE.git">GooseEYE (v0.2.0)</a></li> <li><a href="https://github.com/h5py/h5py.git">h5py (master)</a></li> </ul> To compile code, follow the following structure: <pre>cd code/... mkdir build cmake .. make</pre> Then to run use: <pre>./Run ...</pre> (use <code>./Run --help</code> for help, and/or read the code). For some codes a support function generates commands. They can be generated and run as follows: <pre>python makeJob.py source commands.txt</pre> 4. Plots ("data/.../*.py") Basic plot functions are included with the datasets. Note that all scripts require <code>numpy</code>, <code>matplotlib</code>, <code>h5py</code>, and <code>GooseMPL</code> to be installed. The latter two are included here, the other two are considered standard.

Supporting data for: Persistent contamination of raw milk by Campylobacter jejuni ST-883

Supporting data for the article: "Persistent contamination of raw milk by Campylobacter jejuni ST-883"

opencc-by-4.0Nov 2019View details →

Supporting Information for Publication "Multi-band study of a bi-directional jet occurred in the upper chromosphere"

This supporting information provides three observational movies from SDO/AIA, SDO/HMI, and IRIS. The evolution of the jet and the evolution of the line-of-sight magnetograms in the corresponding region can be tracked in these movies.  The caption of each movie is included in 'AGUSupporting-Information_Movie.pdf'.

Figure 5 in To what extent do pine and oak clear-cut stumps support mite (Acari: Mesostigmata) communities in temperate forests?

Figure 5. Proportional abundance of dominant mite species in CWD and adjacent litter in Scots pine (A) and sessile oak (B) forest.

Figure 6 in To what extent do pine and oak clear-cut stumps support mite (Acari: Mesostigmata) communities in temperate forests?

Figure 6. DCA biplot species data for the different microhabitat of the forest floor. Microhabitat are marked as: tree species [SP - Scots pine and SO - sessile oak] and 5 microhabitat types: [(CWD - coarse woody debris, ecotone, and litter at distances of 0.5, 1.0, and 1.5 m)].

Figure 3 in To what extent do pine and oak clear-cut stumps support mite (Acari: Mesostigmata) communities in temperate forests?

Figure 3. Species accumulation curves (A, B) and proportional abundance (C, D) of Gamasina and Uropodina in litter/soil and CWD (stumps) in Scots pine and sessile oak forest.

Figure 1 in To what extent do pine and oak clear-cut stumps support mite (Acari: Mesostigmata) communities in temperate forests?

Figure 1. Sampling design in sessile oak and Scots pine forest. The oak and pine stumps were sampled in three directions from five microhabitats.

opencc-by-4.0Mar 2016View details →

Fig. 2 in New evidences supporting trophobiosis between populations of Edessa rufomarginata (Heteroptera: Pentatomidae) and Camponotus (Hymenoptera: Formicidae) ants

Fig. 2. Chromatograms of the honeydew contents from Edessa rufomarginata third- and fourth-instar nymphs. The percentages refer to the amount of each sugar in the analyzed sample.

opencc-by-4.0Jun 2020View details →

Fig. 1 in Cultural association and its role in garnering support for conservation: the case of the Mountain Chicken Frog on Dominica

Fig. 1. The Commonwealth of Dominica, and its position in the Caribbean, showing the distribution of Leptodactylus fallax and the study locations (After Adams et al. 2014; IUCN SSC Amphibian Specialist Group 2017).

opencc-by-4.0Dec 2016View details →

Figure 1 in DNA barcoding supports the presence of the cryptic ocellated eagle ray, Aetobatus ocellatus (Myliobatidae), in French Polynesia, South Pacific

Figure 1. - Locations for sampled Aetobatus ocellatus (grey circles) in French Polynesia and for comparative materials (circles in insert: Australia, New Caledonia, Indonesia, India, South Africa, Brazil, Japan, Korea).

opencc-by-4.0Dec 2016View details →

Figure 2 in DNA barcoding supports the presence of the cryptic ocellated eagle ray, Aetobatus ocellatus (Myliobatidae), in French Polynesia, South Pacific

Figure 2. - Neighbour-Joining distance tree (K2p model) of the partial COI sequences (652 bp, 'barcode region') revealing the placement of individuals of Aetobatus ocellatus from French Polynesia within the Aetobatus complex. Specimens are labelled with their BOLD Process ID. Bootstrap values over 75% are indicated above branches.

opencc-by-4.0May 2022View details →

Figs 3-6 in Nest support plants and breeding season of two ibis (Theristicus) species in the Pantanal wetland, Brazil

Figs 3-6. Records with evidences of breeding activities of the Plumbeous Ibis (Theristicus caerulescens) obtained in the Brazilian Pantanal wetland: 3, two adults building a nest at Poconé, MT (photo by Eric Gallardo); 4, an incubating adult at Poconé, MT (photo by Ademir Carletti); 5, an adult and a nestling in a nest at Corumbá, MS (photo by Leonardo Merçon/Instituto Últimos Refúgios); 6, an adult feeding a young on the ground at Poconé, MT (photo by Maria Beatriz Felgar de Toledo). Records were gathered in the WikiAves database.