Find research datasets worth reusing

opencc-by-4.0Oct 2021View details →

EUV optical constants data set

Dataset of optical constants in the extreme ultraviolet (EUV) spectral range, including 13.5nm, obtained from reflectivity measurements.

opencc-by-4.0Sep 2021View details →

Data for "Modelling soil carbon stocks following reduced tillage intensity: a framework to estimate decomposition rate constant modifiers for RothC-26.3, demonstrated in north-west Europe"

Dataset of paired observations of conventional tillage (CT) with no tillage (NT) and reduced tillage (RT) from studies in temperate oceanic regions of Western Europe, extracted from a recent systematic review (Jordon et al. preprint, see DOI below). R code of modelling framework to estimate tillage rate modifiers (TRM) for simulating adoption of RT and NT using RothC-26.3, and meta-estimates of TRM across studies.

opencc-by-4.0May 2022View details →

Data and Workflow to: Three-dimensional buoyant hydraulic fracture growth: constant release from a point source (Möri and Lecampion, (2022))

This upload contains the relevant scripts, notebooks, and datasets to reproduce the numerically obtained results of the Journal article "Three-dimensional buoyant hydraulic fracture growth: constant release from a point source" by Möri and Lecampion, (2022).

opencc-by-4.0Aug 2021View details →

Lithium-ion battery charge and discharge testing data - current, voltage, soc, ta - at constant levels of power

This dataset helped in the composition of a battery testing and modelling validation, of a lithium-ion battery. The data has the charge and discharge testing acquisition data - current, voltage, soc, ta - at constant levels of power.

opencc-by-4.0Nov 2022View details →

Training data for neural network-based determination of nematic elastic constants

Neural network training data packets (intensities_{i}.csv, K1K3_{i}.csv), each consisting of 1000 training data pairs, used in a machine learning-based method for determination of Frank elastic constants of nematic liquid crystals, experimental measurements of time-dependent light intensities (experimental_time_{i}.csv, experimental_intensity_{i}.csv), diode spectrum data (diode_lbd.csv, diode_w.csv). These data sets are associated with the paper <a href="https://www.nature.com/articles/s41598-023-33134-x">[Zaplotnik et al. SciRep, 2023]</a> This is supplementary material for a Jupyter Notebook uploaded on <a href="https://zenodo.org/record/7368828">Zenodo</a>.

opencc-by-4.0Jun 2023View details →

Dataset for "Light Scalar Meson and Decay Constant in SU(3) Gauge Theory with Eight Dynamical Flavors"

Decoding File Names: Consider the file name f8l24t48b48m00889_S0.csv.  We will break down the meaning of the various pieces of the filename <ul> <li>"f8" means 8 Dirac flavors.</li> <li>"l24t48" means 243×48 lattice.</li> <li>"b48" means beta=4.8, related to the inverse bare gauge coupling.</li> <li>"m00889" means fermion mass m=0.00889.</li> <li>"S" means flavor-singlet scalar meson. Other options are "P" for flavor non-singlet pseudoscalar meson and "C" for flavor non-singlet scalar meson.</li> <li>"0" an integer from 0 to 4 proportional to the squared length of the spatial momentum vector of the correlation function.</li> </ul> Columns of the CSV files: Each line of the CSV file should contain 41 entries, separated by commas. Refer to the Eq. (8) which defines model A in the accompanying paper to understand the physical interpretation of these parameters. <ol> <li>Model number: 1 is model A, 2 is model B, 3 is model C.</li> <li>nmax: the number of non-oscillating states in the fit.</li> <li>jmax: the number of oscillating states in the fit.</li> <li>tmin: the minimum t value used in the fit.</li> <li>tmax: the maximum t value used in the fit.</li> <li>𝜒2 of the fit.</li> <li>\(\log\ p\left(\left.M\right|D\right)\): log of model probability used in Bayesian model averaging.</li> <li>fit value for c0 (model A) or \(\overline{c}_0\) (model B).</li> <li>fit error for c0 (model A) or \(\overline{c}_0\) (model B).</li> <li>fit value for c1.</li> <li>fit error for c1.</li> <li>fit value for c2.</li> <li>fit error for c2.</li> <li>fit value for c3.</li> <li>fit error for c3.</li> <li>fit value for c4.</li> <li>fit error for c4.</li> <li>fit value for \(c_1^\prime\).</li> <li>fit error for \(c_1^\prime\)</li> <li>fit value for \(c_2^\prime\).</li> <li>fit error for \(c_2^\prime\).</li> <li>fit value for \(c_3^\prime\).</li> <li>fit error for \(c_3^\prime\).</li> <li>fit value for \(c_4^\prime\).</li> <li>fit error for \(c_4^\prime\).</li> <li>fit value for \(\log(E_2 - E_1)\).</li> <li>fit error for \(\log(E_2-E_1)\).</li> <li>fit value for \(\log(E_3-E_2)\).</li> <li>fit error for \(\log(E_3-E_2)\).</li> <li>fit value for \(\log(E_4-E_3)\).</li> <li>fit error for \(\log(E_4-E_3)\).</li> <li>fit value for \(\log(E_2^\prime - E_1^\prime)\).</li> <li>fit error for \(\log(E_2^\prime - E_1^\prime)\).</li> <li>fit value for \(\log(E_3^\prime - E_2^\prime)\).</li> <li>fit error for \(\log(E_3^\prime - E_2^\prime)\).</li> <li>fit value for \(\log(E_4^\prime - E_3^\prime)\).</li> <li>fit error for \(\log(E_4^\prime - E_3^\prime)\).</li> <li>fit value for \(\log(E_1)\).</li> <li>fit error for \(\log(E_1)\).</li> <li>fit value for \(\log(E_1^\prime)\).</li> <li>fit error for \(\log(E_1^\prime)\).</li> </ol>

opencc-by-4.0Feb 2024View details →

The data for "Reconnaissance with JWST of the J-region Asymptotic Giant Branch in Distance Ladder Galaxies: From Irregular Luminosity Functions to Approximation of the Hubble Constant"

Data used for "Reconnaissance with JWST of the J-region Asymptotic Giant Branch in Distance Ladder Galaxies: From Irregular Luminosity Functions to Approximation of the Hubble Constant" by Siyang Li, Adam G. Riess, Stefano Casertano, Gagandeep S. Anand, Daniel M. Scolnic, Wenlong Yuan, Louise Breuval, and Caroline D. Huang. Magnitudes provided are after correcting for foreground extinction and crowding bias.

opencc-by-4.0Mar 2024View details →

A data set on "Utilizing Constant Energy Difference between sp-Peak and C 1s Core Level in Photoelectron Spectra for Unambiguous Identification and Quantification of Diamond Phase in Nanodiamonds"

The data set to paper:  Utilizing Constant Energy Difference between sp-Peak and C 1s Core Level in Photoelectron Spectra for Unambiguous Identification and Quantification of Diamond Phase in Nanodiamonds Oleksandr Romanyuk1,*, &Scaron;těpán Stehlík1,2, Josef Zemek1, Kateřina Aubrechtová Dragounová1,3 and Alexander Kromka1 1 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10, 162 00 Prague, Czech Republic 2 New Technologies—Research Centre, University of West Bohemia, Univerzitní 8, 306 14 Pilsen, Czech Republic 3 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, 115 19 Prague, Czech Republic * corresponding author: romanyuk@fzu.cz Data manager: Kristýna Dostálová: dostalovak@fzu.cz Date of data collection: 1. 1. 2024 - 15. 03. 2024 All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective Figure to which the data belong is provided in high resolution.  The data are in the following formats:  Figure 1: tiff, csv Figure 2: tiff, csv Figure 3: tiff, csv Figure 4: tiff, csv Figure 5: tiff, csv Data acquistion and processing is provided in the Experimental part in the publication: DOI:10.3390/nano14070590

opencc-by-4.0Jun 2024View details →

Occasional and constant exposure to dietary ethanol shortens the lifespan of worker honey bees

Honey bees (Apis mellifera) are one of the most crucial pollinators, providing vital ecosystem services. Their development and functioning depend on essential nutrients and substances found in the environment. While collecting nectar as a vital carbohydrate source, bees routinely encounter low doses of ethanol from yeast fermentation. Yet, the effects of repeated ethanol exposure on bees' survival and physiology remain poorly understood. Here, we investigate the impacts of constant and occasional consumption of food spiked with 1% ethanol on honey bee mortality and alcohol dehydrogenase (ADH) activity. This ethanol concentration might be tentatively judged close to that in natural conditions. We conducted an experiment in which bees were exposed to three types of long-term diets: constant sugar solution (control group that simulated conditions of no access to ethanol), sugar solution spiked with ethanol every third day (that simulated occasional, infrequent exposure to ethanol) and daily ethanol consumption (simulating constant, routine exposure to ethanol). The results revealed that both constant and occasional ethanol consumption increased the mortality of bees, but only after several days. These mortality rates rose with the frequency of ethanol intake. The ADH activity remained similar in bees from all groups. Our findings indicate that exposure of bees to ethanol carries harmful effects that accumulate over time. Further research is needed to pinpoint the exact ethanol doses ingested with food and exposure frequency in bees in natural conditions.

opencc-by-4.0Jun 2024View details →

Constant Busson (b3552)

-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> -- Name: Constant Busson musiXplora-ID: b3552 musiXplora-URI: <a href="https://musixplora.de/mxp/b3552">https://musixplora.de/mxp/b3552</a> Gender: m First Mentioned: 1835 Last Mentioned: 1892 Sectors: Instrumentenbau Professions (Musical): Handzuginstrumentenbauer, Tasteninstrumentenhersteller Other Places of Activity: Paris Portfolio: <table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Harmonika</td><td><a href="https://musixplora.de/mxp/2001482">2001482</a></td></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Harmonium</td><td><a href="https://musixplora.de/mxp/2001575">2001575</a></td></tr></tbody></table> Changelog:   - v0.0.1: Initial Upload.

opencc-by-4.0Sep 2024View details →

Nocturnal leaf respiratory CO2 release in different species measured at constant temperature

RAW data for GCB publication by Dan Bruhn, Martijn Slot, and Lina M Mercado, 'Simple and accurate representation of cumulative night-time leaf respiratory CO2-efflux' Nocturnal leaf respiratory CO2 release in 14 different species measured at constant temperature measured in the field.

opencc-by-4.0Sep 2021View details →

Machine Learning Quantum Reaction Rate Constants

Dataset of 1,517,419 quantum reaction rate constant products \(k^{\text {QM}}(T)Q_{\text R}(T)\) computed from the transmission coefficient for model single and double barrier minimum energy paths. Here \(k^{QM}(T) \) is the quantum reaction rate constant at temperature \(T\) and \(Q_\text{R}(T)\) is the reactant partition function computed with the rigid rotor and harmonic oscillator approximations.This dataset was created for Ref [1] where it was used to train and test a DNN to predict \(\log{k^{\text{QM}}(T)Q_\text{R}(T)}\). Cite as Please cite the following references when using this dataset: [1] E. Komp and S. Valleau, Machine Learning Quantum Reaction Rate Constants, J. Phys. Chem. A, 124:8607–8613, 2020, <a href="https://pubs.acs.org/doi/abs/10.1021/acs.jpca.0c05992">doi: 10.1021/acs.jpca.0c05992</a>. [2] E. Komp and S.Valleau, Machine Learning Quantum Reaction Rate Constants (1.0.0) [Data set], 2020, Zenodo, <a href="https://doi.org/10.5281/zenodo.5510392">https://doi.org/10.5281/zenodo.5510392 </a> Contents Descriptions of entries in the tabular dataset file `QM_kQ.csv`. Please refer to the publication [1] for details. <ul> <li>`mass_au`: Mass of the reactants in atomic units. </li> <li>`width_1_au`: Width of first potential energy barrier in atomic units.</li> <li>`width_2_au`: Width of second (if present) potential barrier in atomic units. For single barriers width_2_au = 0.0.</li> <li>`height_1_au`: Activation energy of first potential energy barrier in atomic units.</li> <li>`height_2_au`: Activation energy of second (if present) potential energy barrier in atomic units. For single barriers height_2_au = 0.0.</li> <li>`dist_au`: For double barriers, absolute value of the difference between the position of the two potential energy maxima along the reaction coordinate in atomic units. For single barriers dist_au = 0.0.</li> <li>`alpha_symm`: Symmetry constant for single barriers, defined as the difference between product and reactant energies in atomic units.</li> <li>`alpha_double`: Symmetry constant for double barriers, defined as the sum of the normalized difference between barrier heights and the normalized difference between barrier widths, unitless.</li> <li>`slope`: Slope of the first reaction barrier along the reaction coordinate in atomic units. Slope values were evaluated numerically from the type of potential energy barrier see Ref [1] SI.</li> <li>`temp_K`: Temperature in Kelvin.</li> <li>`kQ_rate`: Quantum reaction rate constant times reactant partition function in units of 1/ps. </li> <li>`log_kQ_rate`: Natural logarithm of the quantum reaction rate constant times the reactant partition function in units of log(1/ps).</li> </ul>

Figure 22. Stenothoe valida Dana, 1852 in Minute but constant morphological differences within members of Stenothoidae: the Stenothoe gallensis group with four new members, keys to Stenothoe worldwide, a new species of Parametopa and Sudanea n. gen. (Crustacea: Amphipoda)

Figure 22. Stenothoe valida Dana, 1852, from Gulf of Guinea: Gn 2 female = second gnathopod of female, Gn 2ʹ partly enlarged; P 3, 4 = peraeopod 3, 4; Ep 3 = epimeral plate 3; Us = urosome; U 1, 2, 3 = uropods 1, 2, 3; T = telson.

Figure 8. Stenothoe andamanensis n in Minute but constant morphological differences within members of Stenothoidae: the Stenothoe gallensis group with four new members, keys to Stenothoe worldwide, a new species of Parametopa and Sudanea n. gen. (Crustacea: Amphipoda)

Figure 8. Stenothoe andamanensis n. sp., Andaman Islands: P 7 = peraeopod 7; U 1, 2, 3 male resp. female = uropods; T = telson.

Figure 14. Stenothoe himyara n in Minute but constant morphological differences within members of Stenothoidae: the Stenothoe gallensis group with four new members, keys to Stenothoe worldwide, a new species of Parametopa and Sudanea n. gen. (Crustacea: Amphipoda)

Figure 14. Stenothoe himyara n. sp., Red Sea. A 1, 2 = antennae; Mx 1, 2 = first and second maxillae; Md = mandible; Mxp = maxilliped; Gn 2 = second gnathopod female.

Figure 28. Sudanea inopinata n. g. n in Minute but constant morphological differences within members of Stenothoidae: the Stenothoe gallensis group with four new members, keys to Stenothoe worldwide, a new species of Parametopa and Sudanea n. gen. (Crustacea: Amphipoda)

Figure 28. Sudanea inopinata n. g. n. sp. female, Red Sea: A 1, 2 = antennae; Hd = head; Mx 2 = second maxilla; Md, Md' = mandible of both sides; Mxp = maxilliped; Gn 1 = first gnathopod; Gn 2 = second gnathopod.

Figure 4 in Minute but constant morphological differences within members of Stenothoidae: the Stenothoe gallensis group with four new members, keys to Stenothoe worldwide, a new species of Parametopa and Sudanea n. gen. (Crustacea: Amphipoda)

Figure 4. Stenothoe cf. crenulata Chevreux, 1908: male, 3 mm, Pacific Ocean 4°30ʹ N, 137° 10ʹE: A 1, 2 = antenna 1, 2; Gn 2 = male second gnathopod; U 3 = third uropod male; U 1, 2, 3 = uropod 1, 2, 3; T = telson.

Figure 13. Stenothoe clavetta n in Minute but constant morphological differences within members of Stenothoidae: the Stenothoe gallensis group with four new members, keys to Stenothoe worldwide, a new species of Parametopa and Sudanea n. gen. (Crustacea: Amphipoda)

Figure 13. Stenothoe clavetta n. sp., Bermuda: Us = urosome of male and female; U 1, 2, 3 = uropod 1, 2, 3.

Figure 9. Stenothoe clavetta n in Minute but constant morphological differences within members of Stenothoidae: the Stenothoe gallensis group with four new members, keys to Stenothoe worldwide, a new species of Parametopa and Sudanea n. gen. (Crustacea: Amphipoda)

Figure 9. Stenothoe clavetta n. sp., Bermuda: habitus male; Mx 1, 2 = maxilla 1, 2; Md = mandible; Mxp = maxilliped.