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32,629 results for “Datasets”
Dataset to Manuscript: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Marcus Schiedung et al. (Biogeosciences)
<p>Dataset to manuscript: Schiedung, M., Bellè, S.-L., Sigmund, G., Kalbitz, K., and Abiven, S.: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Biogeosciences, https://doi.org/10.5194/bg-17-6457-2020, 2020.</p> <p>All parameters and variables are described in "Var_names" files.</p>
Dataset to Manuscript: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Bellè et al. 2021 (Biogeosciences)
<p>Dataset to manuscript: Bellè, S-L., Berhe, A., Hagedorn, F., Santin, C., Schiedung, M., van Meerveld, I. and Abiven, S.: Key drivers of pyrogenic carbon redistribution during a simulated rainfall event, Biogeosciences, https://doi.org/10.5194/bg-2020-361, 2021. </p> <p>All parameters and variables are described in the "var_names" file.</p>
ChinaHighNO₂: Daily Seamless 1 km Ground-Level NO₂ Dataset for China (2019–Present)
<p>ChinaHighNO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level NO<sub>2</sub> dataset for China <strong>from 2019 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.93, a root-mean-square error (RMSE) of 4.89 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 3.48 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighNO<sub>2</sub> dataset in your scientific research, please cite the following references (Wei et al., EST, 2022; Wei et al., ACP, 2023):</p> <ul> <li> <p>Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EST-2022.pdf">Ground-level NO<sub>2</sub> surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence</a>. <em>Environmental Science & Technology</em>, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834</p> </li> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> </ul> <p><strong>Note that the ChinaHighNO<sub>2 </sub>dataset is also available for periods prior to 2019, but at a spatial resolution of 10 km:</strong></p> <p> all (including <strong>daily</strong>) data for the years <strong>2008–2018 </strong>is accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4641542">https://doi.org/10.5281/zenodo.4641542</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Dataset of "Cobalt and nickel doped WSe2 as efficient electrocatalysts for water splitting and as cathodes in hydrogen evolution reaction PEM water electrolysis"
<p>Efficient electrocatalysts are crucial for water splitting and fuel cells. Using cheap alternatives that can improve reaction kinetics is essntial for advancing fuel cell technology. Although, tungsten diselinide (WSe2) is promising for electrocatalysis is not fully explored, especially in oxygen evolution and in applications such as polymer electrolyte membrane water electrolyzer.<br>In this work, we used a simple approach to dope WSe2 with cobalt and/or nickel atoms. The doped material was subsequently tested for hydrogen evolution reaction and oxygen evolution reaction. Accordingly, the two electrocatalysts are highly active and stable, affording low overpotentials comparable to those of noble metals. The effective introduction of heteroatoms causes the retention of coordination vacancies, furnishing active catalytic sites that enhanced electrocatalytic performance both in activity and charge transfer. Moreover, both doped materials show excellent performance and stability as cathode electrocatalysts in the polymer electrolyte membrane water electrolyzer with great promise for real-world applications.</p>
Dataset of "Electronic structure and defect states in bismuth and antimony sulphides identified by energy-resolved electrochemical impedance spectroscopy"
Understanding the nature of the defects in the absorber materials, namely point defects, their formation mechanism and the contribution to the properties is essential for the photovoltaic device performance improvement. They are one the reasons why chalcogenide-based solar cells do not yet meet expected high power conversion efficiencies. Here we identify and present energy distribution of defects in Bi2S3 and Sb2S3, and their (SbxBi(100-x))2S3 alloys (with x = 0, 10, 33, 50, 67, 90, 100 at% Sb content) chalcogenides, being explored for emerging photovoltaic applications as they are earth-abundant and highly absorbing in the visible light range. We show that their density of states (DOS) and related parameters can be obtained experimentally by energy-resolved electrochemical impedance spectroscopy (ER-EIS) in a technically simple and quick way, where ER-EIS data are well correlated with theoretical DFT calculations. ER-EIS reveals that in Bi2S3 there are only shallow defects at CBM. In Sb2S3, ER-EIS reveals also midgap states which can be the cause of low electrical conductivity of Sb2S3. We also explain the discrepancy in the reported values of ionisation potentials and the bandgaps of the Bi- and Sb-chalcogenides. Dominant sulphur vacancy defect was identified in Bi- and Sb-chalcogenides whereas in ternary (SbxBi(100-x))2S3 system, merely 10 at.% of Bi transforms the midgap sulphur defects to shallow ones. This provides novel strategy for healing the midgap defects in Sb2S3, which is crucial for boosting the PV performance and tuning the electrical conductivity in Sb2S3.
Characterisation of Social Vulnerability to the environmental hazard of heat in Logroño, and the surrounding La Rioja region in Spain, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for Logroño, and the surrounding La Rioja region, Spain. The input variables used in this dataset come from the national census data for Spain and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Characterisation of Social Vulnerability to the environmental hazard of flooding in Cork City and County, Ireland, derived from national census and EU Copernicus datasets.
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Cork, Ireland. The input variables used in this dataset come from the national census data for Ireland and EU Copernicus data.</p> <div> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p> </div>
Characterisation of Social Vulnerability to the environmental hazard of heat in Milan, derived from national census and EU Copernicus datasets
<p>This dataset includes all input information for indicators that were used to derive social vulnerability and the overall processed data of the social vulnerability index score for the region of Milan, Italy. The input variables used in this dataset come from the national census data for Italy and EU Copernicus data.</p> <p>The social vulnerability indicators used in these datasets are based on research including a review of existing literature and the interpretation of factors affecting social vulnerability. Interpretation of some indicators are contestable and open to debate.</p>
Dataset of "Towards 2D van der Waals Entropy Mixture MX2 (M=Mo,W; X=S,Se,Te) for Hydrogen Evolution Electrocatalysis"
<p>High-entropy alloys have emerged as a class of materials, offering unique properties due to their irregular and randomized arrangement of multiple elements in an ordered lattice. This concept has been extended to two-dimensional (2D) van der Waals materials, including transition metal dichalcogenides (TMD), which exhibit promising applications in electrocatalysis. In this work, we have explored the synthesis of entropy mixture crystals (TMDmix) involved the chemical vapor transport of five individual elements, Mo and W as metal elements, S, Se, and Te as chalcogenide elements, resulting in a crystalline structure with a controlled composition Mo0.56W0.44(S0.33Se0.35Te0.32)2, with an estimated ΔSmix of 0.96R. When observed along the [001] zone axis, STEM HAADF images indicate the presence of the different crystal phases of the 2D TMDs (1T, 2H, and 3R). Our findings demonstrate the potential of the entropy TMDmix materials as catalysts for the hydrogen evolution reaction, as an alternative to noble metal-based catalysts. To maximize the potential of TMDmix, we chose the chemical exfoliation with the resulting material being subdivided into size groups, big and small according to their lateral size. In acidic medium, the lowest overpotential of 127 mV and Tafel slope of 79 mV/dec were obtained for the exfoliated sample with a small lateral size (exf-TMDsmall).</p>
Dataset of "Tuning the morphology and energy levels in organic solar cells with metal- organic framework nanosheets"
<p>Metal-organic framework nanosheets (MONs) have proved themselves to be useful<br>additives for enhancing the performance of a variety of thin film solar cell devices. However,<br>to date only isolated examples have been reported. In this work we take advantage of the<br>modular structure of MONs in order to resolve the effect of their different structural and<br>optoelectronic features on the performance of organic photovoltaic (OPV) devices. Three<br>different MONs were synthesized using different combinations of two porphyrin-based ligands<br>meso-tetracarboxyphenyl porphyrin (TCPP) or tetrapyridyl-porphyrin (TPyP) with either zinc<br>and/or copper ions and the effect of their addition to polythiophene-fullerene (P3HT-PCBM)<br>OPV devices was investigated. The power conversion efficiency (PCE) of devices was found to<br>approximately double with the addition of MONs of Zn2(ZnTCPP), but was unchanged with<br>the addition of Cu2(ZnTPyP) and halved upon the addition of Cu2(CuTCPP) compared to<br>devices without nanosheets. Our analysis indicates that there are three different mechanisms<br>by which MONs can influence the photoactive layer – light absorption, energy level alignment,<br>and morphological changes. Analysis of external quantum efficiency, UV-vis photoelectron<br>spectroscopy data found that MONs have similar effects on light absorption and energy level<br>alignment. However, atomic force and Raman microscopy studies revealed that the nanosheet<br>thickness and lateral size are crucial parameters in enabling the MONs to act as beneficial<br>additives resulting in an improvement of the OPV device performance. We anticipate this<br>study will aid in the design of MONs and other 2D materials for future use in other light<br>harvesting and emitting devices.</p>
Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from various reanalysis datasets
<h1>Dataset Description</h1> <p>Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from several reanalysis datasets. This dataset is the result of an extension of the Jenkinson-Collison circulation type classification to the entire globe, including a modification of its original formulation for the southern hemisphere.</p> <p>A modified version of the IPCC-AR6 Reference Regions that excludes the intertropical range where the method is not applicable is also included, as used in the reference paper for global assessment.</p> <p>Further details in <a href="https://doi.org/10.1007/s00382-022-06658-7" target="_blank" rel="noopener">https://doi.org/10.1007/s00382-022-06658-7 </a></p> <h2>Note for version 1.1.0</h2> <p>This version corrects an issue in the previous release, which was incorrectly labeled as <em>version 0.1</em>. That version was incomplete due to the omission of previously existing files, and should be considered <strong>incomplete</strong>. Version 1.1.0 restores all original files alongside the newly added one, ensuring the dataset is now complete and consistent. We apologize for any inconvenience this may have caused and appreciate your understanding.</p>
OHHR – The Oldenburg Hearing Health Record [Dataset]
<p><strong>Description of the dataset</strong></p> <p>The Oldenburg Hearing Health Record (OHHR) provides a publicly accessible dataset that can be used to advance hearing health research. It includes a constellation of data collected from 581 participants (aged 18–86 years<em>; </em>255 female; <em>Mean age = 67.31 years; SD = 11.93</em>) between 2013 and 2015 at the Hörzentrum Oldenburg in collaboration with the Cluster of Excellence "Hearing4all". The data was anonymized in accordance with the General Data Protection Regulation (GDPR; Regulation (EU) 2016/679). Each participant was assigned a unique identifier to maintain anonymity while enabling multivariate individualized analyses. </p> <p>All the different data types are listed below:<br><br><strong>Subjective Measures</strong></p> <ul> <li>Home Questionnaire</li> <li>SF-12 Health Survey</li> <li>Technology Readiness Questionnaire</li> <li>Anamnesis</li> </ul> <p><strong>Audiological Tests</strong></p> <ul> <li>Pure Tone Audiometry</li> <li>Adaptive Categorical Loudness Scaling</li> <li>Digit Triplet Test (Speech Reception Threshold in Noise: Screening)</li> <li>Göttingen Sentence Test (Speech Reception Threshold in Noise)</li> </ul> <p><strong>Cognitive Measures</strong></p> <ul> <li>DemTect</li> <li>WortSchatz</li> </ul> <p><strong>Demographic Information</strong></p> <ul> <li>Socio-economic data</li> <li>Scheuch-Winkler Index (calculated)</li> </ul> <p><strong>Supporting Documentation</strong></p> <p><strong>MethodsDescription.rtf/.pdf:</strong> Provides detailed explanations of data type and collection procedures.<br><strong>data.zip/metadata</strong><strong>: </strong>Includes schema and description files for all data tables.</p> <p>A supporting paper was published on Scientific Data:</p> <div> <div> <p>Jafri, S., Berg, D., Buhl, M. <em>et al.</em> The Oldenburg Hearing Health Record (OHHR). <em>Sci Data</em> <strong>12</strong>, 1546 (2025). https://doi.org/10.1038/s41597-025-05884-y</p> </div> </div>
Dataset for "Methodology of Evaluating the Activation Energy of Oxygen Reduction Reaction on Pt-based Electrodes"
<p>High temperature proton-exchange membrane fuel cell (HT-PEMFC) technology is widely studied alternative to current energy conversion technologies based on fossil fuels. Compared to solid oxide fuel cells (SOFCs), HT-PEMFCs allow more flexibility and demand less operation control due to their lower temperature. On the other hand, HT-PEMFCs show an advantage over low-temperature PEMFCs in terms of less demand on the purity of the H2 used, the possibility to recover the generated heat, lower water management requirements, and easy heat management. One of the critical limitations of HT-PEMFC operation is a slow kinetics of the cathodic reduction of O2 (ORR) due to presence of H3PO4 which ensures proton conductivity in the system. Electrochemical dynamic methods such as cyclic voltammetry or linear sweep voltammetry (LSV) can be used to determine the kinetic parameters of ORR. These measurements can provide information on the Tafel slope and exchange current density (jex) of the ORR. However, performing these measurements under conditions relevant for HT-PEMFC operation is challenging due to presence highly concentrated H3PO4 and elevated temperature. First, determination of the kinetic parameters requires correct assessment of equilibrium potential of ORR (EORR). The value of the EORR is generally influenced by the activity (fugacity) of the reactants and products and the temperature, a discussion of the appropriate standard states of the components is also necessary. Second, the relationship between the jex and the reaction rate constant (k°), necessary for calculation of activation energy ( ), must be known. It includes consideration of the likely reaction mechanism. In this paper, the methodology for appropriate determination of was developed and used for estimation of of ORR from LSV curves measured on commercially available Pt/C catalyst under HT-PEMFC relevant conditions. In particular, the measurements were carried out using a rotating glassy carbon rod disk electrode (RRE) in purified 98 wt.% H3PO4 (as electrolyte) at temperatures of 120, 140, 160, 180 °C. Though the treatment was developed in context of ORR and HT-PEMFC, the approach is generally applicable to any electrochemical reaction.</p>
HD-SIM-RBV: a synthetic dataset with model-based simulations of blood volume changes during hemodialysis
<p>The HD-SIM-RBV dataset is a synthetic (model-based) dataset generated to enable the study of blood volume (BV) or relative blood volume (RBV) changes during hemodialysis (HD).</p> <p>The dataset includes the profiles of BV changes during a standard 4-hour HD session simulated using a lumped-parameter, physiologically-based model of the cardiovascular system and the whole-body water and solute kinetics in 5,000 virtual patients with randomly adjusted values of 90 physiological parameters.</p> <p>For each of the 90 selected parameters, a random value was drawn from a normal distribution with the mean equal to the baseline value used originally in the model (with a few exceptions) and the standard deviation (SD) assumed at the level of 10%, 20%, or 40% of the baseline value, depending on the nature of the given parameter and the likelihood of its variation in the population (for some parameters, SD was set below 10% - see Parameters.xlsx). Only values within ±2SD from the mean were accepted. </p> <p>Ultrafiltration was set randomly within ±1 L from the assigned fluid overload. All other parameters as well as dialysis settings were kept constant for all virtual patients (at the levels used in our previous work - see the references below).</p> <p> </p> <p>When using the dataset, please cite the associated conference paper:</p> <p>Pstras L, Waniewski J. A Model-Based Dataset for In-Silico Exploration of the Patterns of Relative Blood Volume Changes During Hemodialysis. 2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, 149-150, 2023, doi: 10.1109/IEEECONF58974.2023.10404528.</p>
Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Luke) and Geological Survey of Finland (GTK)
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland </strong></p><p><strong>Creators: </strong>Larmola T, Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M </p><p>The dataset consists of peat properties in a subset of 16 undrained peatland sites (32 peat samples) in Geological Survey of Finland (GTK) national peatland inventory. These sites were sampled between 2002 and 2017 and the subset selected from GTK peat sample archives. These 16 sites represented two pine-<i>Sphagnum-</i> dominated site types (IR, KR) and two treeless sedge fen types (VSN, RhSN) all in 4 replicates and sampled in 2 depths 20-40, 40-60cm). </p><p><strong>Peat analyses</strong> The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S).</p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola T, Anttila J, Alm J Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description </strong></p><p>ID - Site identifier</p><p>site - undrained peatland (UDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 20: 0-20 cm, 40: 20-40cm, 60: 40-60cm.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin - UDP site type. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p><strong>References</strong></p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p>
Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland, Natural Resources Institute Finland
<p><strong>Dataset on surface peat stoichiometry and physical properties in boreal forestry-drained peatlands in Finland</strong></p><p><strong>Creators: Larmola T, Anttila J, Alm J </strong></p><p>The dataset consists of peat properties in a subsample of 30 drained peatland forests in Finland selected from the permanent sample plots of the 8th National Forest Inventory (systematic sample of plots on drained peatland forests, e.g., Hotanen et al. 2006). The subsample included equally different site types of forestry-drained peatlands of those parts of Finland where drainage for forestry is economically viable (Latitude 60-66 ºN, annual temperature sum > 750 dd). </p><p><strong>The site selection criteria</strong> were average peat layer thickness of over 20 cm, no clear-cut areas, site drained before 1995 and ditching had detectably altered hydrology or vegetation. <strong>Peat analyses</strong> Finnish Forest Research Institute (now Natural Resources Institute Finland) sampled peat cores with a box corer in 2002, samples were analysed for bulk density, archived and remaining samples at depths 20-30, 30-40 cm (total of 58) were analysed in 2021. The peat samples were analyzed for C:H:N:S and ash concentration with Leco 628 CHNS analyzer following standard SFS EN13039 with FINAS accredited adjustments JOK3023. The dry matter content was analyzed after drying the sample at 105 ℃ and ash content based on loss on ignition at 550 ℃. </p><p><strong>Stoichiometric calculations</strong>The O concentration was determined by difference: %O = 100 - % (ash + total C + N + H + S). Atomic ratios of C:N, H:C and O:C were calculated based on the individual sample mass values. The C oxidation state (Cox), the oxidative ratio (OR), and the degree of unsaturation (DU) were calculated following equations in the study by Masiello et al. (2008). The analyses are described in more detail in Turunen et al. (manuscript). </p><p>Related datasets used in the same publication are:</p><p>Larmola, T. Anttila J, Turunen J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M Dataset on surface peat stoichiometry and physical properties in boreal undrained peatlands in Finland, Natural Resources Institute Finland (Version 1) [Dataset]. Zenodo. doi.org/<strong>10.5281/zenodo.10068486</strong></p><p>Turunen J. (2023). Surface peat data, Geological Survey of Finland (Version 1) [Data set]. Zenodo. <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.8434148&data=05%7C01%7Cluke.tuula.larmola%40valtion.mail.onmicrosoft.com%7Cc48ffad4c0e341d0fa5808dbcaff0289%7C7c14dfa4c0fc47259f0476a443deb095%7C0%7C0%7C638326968887189768%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=AyYOxR7Mas2ef8y3wI7oCrzHWSyqBzt%2FJB0CMN%2BJUiU%3D&reserved=0">https://doi.org/10.5281/zenodo.8434148</a></p><p> </p><p><strong>Data column description</strong></p><p>ID - Site identifier</p><p>site - Forestry-drained peatland (FDP) for all rows</p><p>ncoord - North coordinate (latitude), degrees.</p><p>depth - Sampling depth. 30: 20-30 cm, 40: 30-40cm, avg: average of both depths.</p><p>type - Site type classification according to the Finnish peatland site type system.</p><p>origin – Origin of the FDP site type at undrained state. I: treed peatland (peat typically Sphagnum-wood), II: treeless peatland (or sparsely treed, peat typically Sphagnum-sedge)</p><p>type_num - Nutrient level according to site type. 1 is the most nutrient rich and 4 is the least.</p><p>Cmol - Molar carbon concentration in the sample</p><p>Hmol - Molar hydrogen concentration in the sample</p><p>Nmol - Molar nitrogen concentration in the sample</p><p>Omol - Molar oxygen concentration in the sample</p><p>Smol - Molar sulphur concentration in the sample</p><p>bd - Bulk density, kg/m3</p><p>cox - C oxidation state</p><p>or - Oxidative ratio</p><p>du - Degree of unsaturation</p><p>hc - H:C ratio</p><p>cn - C:N ratio</p><p>oc - O:C ratio</p><p>n - Number of samples. 2 for averages from both depths, 1 for all other rows.</p><p> </p><p><strong>References</strong></p><p>Hotanen JP, Maltamo M, Reinikainen A (2006) Canopy stratification in peatland forests in Finland. Silva Fennica 40:53–82.</p><p>Masiello CA, Gallagher ME, Randerson JT, Deco RM, Chadwick OA (2008) Evaluating two experimental approaches for measuring ecosystem carbon oxidation state and oxidative ratio, Journal of Geophysical Research 113, G03010, <a href="https://doi.org/10.1029/2007JG000534">https://doi.org/10.1029/2007JG000534</a></p><p>Turunen J, Anttila J, Laine-Petäjäkangas A, Ovaskainen J, Laatikainen M, Alm J, Larmola T 2023. Impacts of forestry drainage on surface peat stoichiometry and physical properties in boreal peatlands in Finland. <i>manuscript.</i></p><p> </p>
Geodesic-BP Dataset
<p>This dataset contains the results of our method <i>Geodesic-BP</i>, presented in <a href="https://arxiv.org/abs/2308.08410">https://arxiv.org/abs/2308.08410</a>. <br>The original rabbit torso model can be found in <a href="https://zenodo.org/record/6340066">https://zenodo.org/record/6340066</a>, on which we based the setup.</p><p>The dataset consists of the final results, optimization results over the 400 iterations and a pseudo-bidomain simulation from the final result. All files are provided in variants of the VTK file format (<a href="https://vtk.org/">https://vtk.org/</a>) The setup/result files are organized as follows:</p><ul><li>result_mesh_init_final.vtu - The biventricular mesh containing both initial and final solution φk</li><li>result_x0_init.vtp - The initial conditions (xi , ti) used in the first optimization iteration</li><li>result_x0_final.vtp - The initial conditions (xi, ti) computed using our optimization algorithm</li><li>ecgs.npz - Numpy-readable (np.load) arrays of ECGs (ecg_init, ecg_final, ecg_target)</li><li>ecgs.vtp - Target and optimized ECGs converted to a Paraview-readable format</li><li>ecg_history.npz - Numpy-readable array of the ECGs over the iterations</li></ul><p>The animation files present allows you to preview the solution in each iteration </p><ul><li>phi_history.xdmf - The solution φk in each iterations (surface only)</li><li>x0_history.xdmf - The initial conditions in each iteration</li><li>ecg_anim.xdmf - The computed ECGs in each iteration</li></ul><p>The files can be easily viewed in VTK-compatible viewers, such as Paraview (<a href="https://www.paraview.org/">https://www.paraview.org/</a>). We additionally provide a Paraview state file (preview.pvsm), which when opened in Paraview automatically creates several views that visualize the data in different views. Simply open Paraview, select File -> Load State, locate the preview.pvsm. In the next prompt (Load State Options) select "Search files under specified directory" and locate the folder with the files, then press OK.</p>
Dataset for: A continuous classification of the 480,000 lakes of the conterminous US based on geographic archetypes
<p>These datasets were used in a journal article with the goal of developing a new geographic classification approach for ~480,000 lakes ≥ 1 ha in the conterminous U.S. based on archetypes defined as endmembers with distinct combinations of climate, hydrologic, geologic, topographic, and morphometric properties. We identified seven lake archetypes; each study lake was then assigned weights for each of the archetypes. The data used to develop the archetypes, archetype weights, and variables used in associated analyses is provided in three data tables. The first includes the lake-specific transformed predictors used to generate the seven archetypes, the weights corresponding to each archetype, the archetype with the maximum weight and the weight of that maximum archetype. The second provides lake-specific raw values for each predictor and for the 19 response variables used to explore aspects of the archetype classification. The final metadata table provides a data dictionary for all columns in the previously mentioned data tables.</p>
Dataset and program scripts for the reproducibility of the hierarchical data structure file. Related to the manuscript entitled: Hierarchical Representation of Measurement Data, Metrological Uncertainty and Metadata for Calibrated Battery Tests
<p>We present an interoperable hierarchical data representation for battery tests, leading to improved scalability of data transmission and enhanced data accessibility and comprehensibility for both human interpretation and machine processing. The hierarchical data format includes the raw trace electrical measurement data, the metrological calibration and uncertainty data, the metadata such as experimental settings, instruments and software versions, as well as post-processed data such as electrochemical model fit parameters. This data representation allows repetition of the battery test under the exact same conditions such that identical results are achieved within defined error bounds. This is in line with the general F.A.I.R. data approach and provides repeatability and traceability in the battery value chain. As an application of the hierarchical data representation, we show the classification of cells as pass/fail being performed with quantitative confidence levels. We demonstrate the complete workflow of establishing the hierarchical data structure for electrochemical impedance spectroscopy (EIS), starting from metrological traceability of the calibration and uncertainty analysis towards the storage of the structured data as a single integrated file that preserves the hierarchical data format.</p>
ChinaHighNO₂: Daily Seamless 10 km Ground-Level NO₂ Dataset for China (2008–2018)
<p>ChinaHighNO<sub>2</sub> is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level NO<sub>2</sub> dataset for China <strong>from 2008 to 2018</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.84, a root-mean-square error (RMSE) of 7.99 µg m<sup>-3</sup>, and a mean absolute error (MAE) of 5.34 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighNO<sub>2</sub> dataset in your scientific research, please cite the following references (Wei et al., ACP, 2023; Wei et al., EST, 2022):</p> <ul> <li> <p>Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2023.pdf">Ground-level gaseous pollutants (NO<sub>2</sub>, SO<sub>2</sub>, and CO) in China: daily seamless mapping and spatiotemporal variations</a>. <em>Atmospheric Chemistry and Physics</em>, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023</p> </li> <li> <p>Wei, J., Liu, S., Li, Z., Liu, C., Qin, K., Liu, X., Pinker, R., Dickerson, R., Lin, J., Boersma, K., Sun, L., Li, R., Xue, W., Cui, Y., Zhang, C., and Wang, J. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EST-2022.pdf">Ground-level NO<sub>2</sub> surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence</a>. <em>Environmental Science & Technology</em>, 2022, 56(14), 9988–9998. https://doi.org/10.1021/acs.est.2c03834</p> </li> </ul> <p><strong>Note that the ChinaHighNO<sub>2</sub> dataset was improved to a 1 km resolution after 2019:</strong></p> <p> all (including <strong>daily</strong>) data for the years after <strong>2019</strong><strong> </strong>are accessible at: <strong><a href="https://doi.org/10.5281/zenodo.4571660">https://doi.org/10.5281/zenodo.4571660</a></strong></p> <p><strong>More CHAP datasets for different air pollutants are available at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
ScienceDex guides
Understand access before you commit
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.