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Calibrated Relocations for the TXAR Catalog (2009–2016)
<p>This dataset contains the relocated earthquake catalog for the Southern Delaware Basin, as described in the published research paper titled <strong>"Insights into Temporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations from the TXAR Catalog (2009–2016)"</strong>.</p> <p>The earthquake relocation was performed using <strong>Hypocentroidal Decomposition</strong> technique, which provided improved spatial resolution for 73 events of magnitude 1.5 or greater in the TXAR catalog. The virtual hypocentroid used for relocating the TXAR catalog events was inverted from a core cluster of 116 post-2020 events recorded by the Texas Seismological Network. This relocated catalog includes key hypocentral parameters for each event—latitude, longitude, and depth—along with origin time, associated uncertainty estimates, and magnitude.</p> <p>This dataset serves as a critical resource for understanding the temporal and spatial patterns of induced seismicity related to anthropogenic activities, such as shallow fluid injection, in the Southern Delaware Basin before the operation of local seismic networks in the region. It is well-suited for use in further seismic hazard assessments, modeling studies, and comparisons with other induced seismicity datasets.</p> <h3>Citation:</h3> <p>Asiye Aziz Zanjani, Heather R. DeShon, Vamshi Karanam, Alexandros Savvaidis; <strong>Insights into Temporal Evolution of Induced Earthquakes in the Southern Delaware Basin Using Calibrated Relocations from the TXAR Catalog (2009–2016)</strong>. <em>The Seismic Record</em>, 2024; 4(2): 140–150. DOI: <a href="https://doi.org/10.1785/0320240011" target="_new" rel="noopener">https://doi.org/10.1785/0320240011</a></p>
Fifty years of firn evolution on Grigoriev ice cap, Tien Shan, Kyrgyzstan
<p><strong>README Grigoriev data</strong></p> <p><strong>Overview</strong></p> <p>The Grigoriev data collection consists of the following files, which are briefly explained further below.<br>From a relatively large number of files and for clarity, we provide mainly those files which have been directly<br>used in the generation of figures contained in Machguth et al. (2024). While the use in figure<br>creation was the main selection criteria, the files have not been truncated to data shown in the figures. <br>The files contain more information than shown in the figures. A few files have been added for completeness although<br>not used to create figures (see below).</p> <p>The data sets provided in this repository are listed in the following. Most of these tables contain relatively raw data. <br>The suggested citations are added in brackets. Please also check Table 1 in Machguth et al. (2024) for potential further references.</p> <p>- 1990_GRG_90_H1-BETA.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H1-CHM.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H1-STRAT.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H2-CHM.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_GRG_90_H2-STRAT.xlsx (Arkhipov et al., 1996; Thompson et al., 1997)<br>- 1990_H1-H2_2018_Grigoriev_MI-decadal.xlsx (Arkhipov et al., 1996; Thompson et al., 1997; Machguth et al., 2024)<br>- 2001_GRG_01_S1-EE.xlsx (Arkhipov et al., 2004; Mikhalenko et al., 2005)<br>- 2001_metals.pdf (Usubaliev, 2003)<br>- 2003_GRG03-S1-EE_001.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_GRG03-S2-EE 001.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_pits.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_temperature_density.xlsx (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2003_temperature_logger_data.xls (Mikhalenko et al., 2005; Kutuzov, 2005)<br>- 2018_density_stratigraphy_field_and_PSI_by_centimeter.xlsx (Machguth et al., 2024)<br>- 2018_PSI_dating_20230517.xlsx (Eichler et al., 2020; Machguth et al., 2024)</p> <p><br><strong>Detailed Information</strong></p> <p>1990_GRG_90_H1-BETA.xlsx: refers to Core 1 1990 (labelled H1 probably for "Hole 1"). Unknown to what the 1991 data refer, likely a repeat measurement.</p> <p>1990_GRG_90_H1-CHM.xlsx: Chemistry Core 1 1990.</p> <p>1990_GRG_90_H1-STRAT.xlsx: Stratigraphic information Core 1 1990</p> <p>1990_GRG_90_H2-CHM.xlsx: Chemistry Core 2 1990</p> <p>1990_GRG_90_H2-STRAT.xlsx: Stratigraphic information Core 2 1990</p> <p>1990_H1-H2_2018_Grigoriev_MI-decadal.xlsx: This table we calculated from the 1990 tables as well as the 2018 data for the purpose of visualizing<br> decadal means in MIs (Fig. 7). Decadal dating of the 1990 cores was done based on the bomb horizon of 1963 (Thompson et al., 1993, 1997), <br> decadal picks from Thompson et al. (1993) and personal communication by Lonnie Thompson (email 19 June 2023).</p> <p>2001_GRG_01_S1-EE.xlsx: 2001 core, stable water isotope ratios, firn temperatures, percentage of infiltration ice, stratigraphy</p> <p>2003_GRG03-S1-EE_001.xlsx: 2003 51m and 22.6m cores, 51m core was drilled thermally, 22.6m core mechanically. For the latter similar data as for 2001</p> <p>2003_GRG03-S2-EE 001.xlsx: 2003 21.3m core. Reduced amount of measured parameters compared to e.g. 2001 core. </p> <p>2003_pits.xlsx: Stratigraphy and density measured in a series of snow pits in 2003.</p> <p>2003_temperature_density.xlsx: Density and temperature measured in 2003 22.6m core. Comparison of T_ice at 4440 m a.s.l. to 1962 core (Dikikh, 1965)</p> <p>2003_temperature_logger_data.xls: Firn temperatures measured through a thermistor chain during 3 days in June 2003. Data from 14 June have been used for Fig. 5.</p> <p>2018_density_stratigraphy_field_and_PSI_by_centimeter.xlsx: 2018 core stratigraphy and density. This is a somwhat outdated file which shows the data per centimetre.<br> The file compares the two measurements of density (only the one from the laboratory was used in Machguth et al., 2024). <br> Also contains visually observed dust layers (not shown in Machguth et al., 2024)</p> <p>2018_PSI_dating_20230517.xlsx: Complete data from the analysis of the 2018 core.</p> <p><br><strong>Bibliography</strong></p> <p>Arkhipov, S. M., Mikhalenko, V. N., & Thompson, L. (1996). Struktura i stratigrafiya deyatel’nogo sloya lednika Grigor’eva na Tyan’-Shanye (Structure and stratigraphy of the active layer <br>of the Griroriev glacier in the Tjan-Shan). Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 80, 68–83.</p> <p>Arkhipov, S. M., Mikhalenko, V. N., Kunakhovich, M. G., Dikikh, A. N., and Nagornov, O. V.: Termicheskiy reshim, uslovija l’doobrazovanija i akkumulatsija na lednike Grigor’eva (Tyan’-<br>Shan), v 1962–2001 gg. (Thermal regime, types of ice formation and accumulation on the Grigoriev glacier (Tien Shan), 1962–2001), Materialy Glyatsiologicheskikh Issledovaniy (Data<br>of Glaciological Studies), 96, 77–83, 2004.</p> <p>Eichler, A., Kronenberg, M., Brütsch, S., Rüthi, M., Heule, M., Schwikowski, M., et al. (2020). Chernobyl horizon in a Central Asian ice core. <br>Annual Report 2019 - Laboratory of Environmental Chemistry - PSI, 31.</p> <p>Kutuzov, S. S.: Prostranstvennie izmenenija i stroenie lednikov vnutrennogo Tyan’-Shanya za poslednie 150 let (Spatial changes and structure of the glaciers of the inner Tien Shan over the last 150<br>years), Master’s thesis, Lomonossov State University, Moskva, 2005.</p> <p>Machguth, H., Eichler, A., Schwikowski, M., Brütsch, S., Mattea, E., Kutuzov, S., et al. (2024). Fifty years of firn evolution on Grigoriev ice cap, Tien Shan, Kyrgyzstan. <br>The Cryosphere, 18(4), 1633–1646. https://doi.org/10.5194/tc-18-1633-2024</p> <p>Mikhalenko, V. N., Kutuzov, S. S., Fayzrakhmanov, F. F., Nagornov, . B., Thompson, L. G., Kunakhovich, M. G., Arkhipov, S. M., Dikikh, A. N., and Usubaliev, R.: Sokrashhenie oledenenija<br>Tyan’-Shanja v XIX – nachale XXI vv.: rezul’taty kernovoro burenija i izmerenija temperatury v skvazhinakh (Glacier recession in the Tien Shan from the XIX to the beginning of the XXI century:<br>results from ice core drilling and borehole temperature measurements), Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 98, 175–182, 2005.</p> <p>Thompson, L. G., Mosley-Thompson, E., Davis, M., Lin, P. N., Yao, T., Dyurgerov, M., & Dal, J. (1993). “Recent warming” ice core evidence from tropical ice cores with emphasis <br>on Central Asia. Global Planet. Change, 7(1–3), 145–156. https://doi.org/10.1016/0921-8181(93)90046-Q</p> <p>Thompson, L. G., Mikhalenko, V., Mosley-Thompson, E., Durgerov, M., Lin, P. N., Moskalevsky, M., et al. (1997). Ice core records of recent climatic variability: Grigoriev and It-Tish ice caps <br>in Central Tien Shan, Central Asia. Materialy Glyatsiologicheskikh Issledovaniy (Data of Glaciological Studies), 81, 100–109.</p> <p>Usubaliev, R. A. (2003). Khimitcheskoe zagryaznenie lednikov Tyan’-Shanya (na primere lednika Grigor’eva) (Chemical pollution of Tien Shan glaciers (on the example of Grigoriev Glacier)). <br>Izvestija Natsional’noy Akademii Nauk Kirgizskoy Respubliki (News of the National Academy of Sciences of the Kyrgyz Republic), 4, 154–160.</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>
Correspondence of the natural oscillation frequencies of perforated plates depending on the type of holes, plate material and thickness, type of fixing (CCCS or CSCS)
<p>The method involved the analysis of oscillations of base plates: solid non-perforated and with round holes, as well as perforated plates with holes of complex geometry in the form of a five-petal epicycloid.</p> <p>As a result of the modeling (Abaqus), the natural oscillations frequencies of the studied plates were obtained depending on the type of perforation, material, thickness and type of their fixing. The use of different materials (steel and aluminium) showed an insignificant influence on the natural oscillation frequency of the plates. It was found that the plate thickness has the greatest influence (31.85– 33.35%), the following are the hole parameters: partition width between holes; pitch between hole centers.</p> <p>Analysis of the results showed that the natural vibrations of plates with holes of complex geometry differ by up to 7% compared to plates with basic round holes. </p>
Natural frequency of oscillations of a solid surface (without holes) and perforated sieve with holes of complex geometry in the shape of five-petal epicycloid
<p>The experimental determination of the structural function of the frequency response consists in identifying the natural frequencies of oscillation of the test surfaces, for which the laboratory equipment was developed, and the following methodology was used. </p> <p>To determine the structural function of the frequency response, it is necessary to obtain two data channels: the input force and the corresponding response of the test object (test surface). In impact measurement, the input force is provided by a modal impact hammer, and the output response of the test object (test surface) is measured using an accelerometer.<br>The basic elements of the scheme are a special impact pulse type hammer PCB 084A17 for creating excitations (oscillations); cables for signals transmission; accelerometer sensor PCB 352V10 with highly sensitive piezoelectric elements for fixing oscillations; signal amplifier SIEMENS model SCADAS Mobile; computer with Simcenter Testlab 2019.1 software for processing and visualization test results.</p> <p>The study was conducted according to the following algorithms:<br>1. Test setup: boundary conditions; determination of test scheme and parameters; frequency range; determination of excitation source and force level.<br>2. Testing: installation and control of accelerometers; object excitation and frequency response measurement; check of measurement quality and coherence.<br>3. Post-test: modal curve fitting; validation of the modality against the assurance criterion and modal synthesis.<br>The research was carried out using the following algorithm. </p> <p>The perforated surface prototype was rigidly fixed to the prefabricated frame. With this type of fixation, the investigated surface at the periphery is fixed and unable to move.<br>The surface of the prototype was marked by overlaying a coordinate grid with the specified step.<br>This data of natural frequency of oscillations of a solid surface under various modes, which are obtained experimentally. The obtained oscillation frequencies are needed to determine the difference between the construction of a solid plate and a perforated surface with holes of complex geometry.</p>
Reuse streams at UK waste facilities (HWRCs)
<div> <p>The UK recycles thousands of working and repairable devices every week at its waste facilities. But fixing or reusing them instead could reduce waste, lower emissions and save households money. </p> <p>Through a citizen science project, we investigated what reuse streams are currently available at household waste and recycling centres (HWRCs) and the challenges of making reuse more widely available.</p> <p>This dataset contains records for all HWRCs across the UK and details of any publicly-communicated reuse streams at each site. It is accompanied by a narrative report. Last updated October 2024.</p> </div>
Dataset of "Hydrogen Evolution Reaction Activity in Mo₂TiC₂Tₓ MXene Derived from Mo₂TiAlC₂ MAX Phase: Insights from Compositional Transformations"
<p>MAX phases represent a crucial building block for the synthesis of MXenes, which constitute an intriguing class of materials with significant application potential. This study investigates the catalytic properties of Mo₂TiAlC₂ MAX phase and the corresponding Mo₂TiC₂Tₓ MXene for hydrogen evolution reaction (HER). Characterization by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS) revealed that despite the presence of secondary phases, the HER catalytic activity is primarily influenced by the MAX phase and its derived MXene. Interestingly, the catalytic activity of the MXene improves over time, attributed to the formation of MoO₂ as identified by XPS. This work enhances the understanding of MXene-based materials for electrochemical applications, highlighting crucial structural and chemical transformations that optimize their performance in sustainable energy technologies.3D structure of lanthanum strontium manganite and yttria-stabilized zirconia composites is predicted based on conductivity measurements using Monte Carlo 3D equivalent circuit network approach. Validation experimental impedance spectra; scanning electron micrographs; cross sections of model simulation or prediction (MSP).</p>
Dataset of "Selective Precipitation of REE-Rich Aluminum Phosphate with Low Lithium Losses from Lithium Enriched Slag Leachate"
<p>Currently, recycling of spent lithium-ion batteries is carried out using mechanical, pyrometallurgical and hydrometallurgical methods and their combination. The aim of this article is to study a part of pyro-hydrometallurgical processing of spent lithium-ion batteries which includes lithium slag hydrometallurgical treatment and refining obtained leachate. Lithium slag intended for leaching experiments contains 3,68 % of Li; 11,02 % of Al; 1,17 % of Co; 1,71 % of Cu and other metals in minority content. Leaching step was realized via dry digestion that is an effective method capable of transferring over 99% of the present metals such as Li, Al, Co, Cu and others to the leachate. The highest content in leachate reached Al (2666 µg/mL) and Li (2239 µg/mL). Extraction of metals from leachate can be conducted using various methods, with precipitation being the most used. In this work, the influence of two types of precipitation agent (NaOH, Na3PO4) on precipitation efficiency of Al and Li losses was investigated. It was found that the precipitation of aluminium with NaOH can result in the co-precipitation of lithium, causing total lithium losses up to 40 %. As suitable precipitating agent for complete Al removal from Li leachate with a minimal loss of lithium (less than 2 %), crystalline Na3PO4 was determined under following condition: pH = 3, 400 rpm, 10 minutes, room temperature. Analysis confirmed that, in addition to aluminium, the precipitate also contains REE La (3.4%), Ce (2.5%), Y (1.3%), Nd (1%) and Pr (0.3%), which selective recovery will be the subject of further study.</p>
Arctic Coastal Change
<p><strong>General description</strong><br>The Arctic Coastal Change image collection consists of multitemporal coastal land cover data for the entire Arctic coast for the period 1984–2023. Coastal land cover is divided into land, water, and ice. Land cover is averaged over five-year time-steps, starting from 1984–1988 and ending in 2019–2023. The dataset has been produced using the full Landsat collection in Google Earth Engine (namely, Landsat 5, Landsat 7 and Landsat 8).</p> <p>This is considered a beta version, as the paper describing the data processing is currently under review.</p> <p><strong>Data structure</strong><br>The pan-Arctic dataset has been divided into 24 large tiles. The tiles are described in the accompanying vector file '<em>ACC_tiles.geojson</em>' and distributed as zip files. The dataset consists of one image for each of the eight time-steps, indicated with the image property 'timestep'. Each image has two bands: 1) land cover and 2) the number of valid observations. The spatial resolution of the dataset is 30 meters.</p> <p>The pixel and property values indicate following:</p> <p>Band: '<em>landcover</em>'<br>1 = land<br>2 = water<br>3 = ice<br>NA = not enough valid satellite data for reliable land cover classification or outside 10-km coastal zone</p> <p>Band: '<em>observations</em>'<br>1 = 5–9<br>2 = 10–14<br>3 = 15–29<br>4 = 30 or more<br>NA = less than 5 valid observations</p> <p>Property: '<em>timestep</em>'<br>1984 = time-step 1984–1988<br>1989 = time-step 1989–1993<br>1994 = time-step 1994–1998<br>1999 = time-step 1999–2003<br>2004 = time-step 2004–2008<br>2009 = time-step 2009–2013<br>2014 = time-step 2014–2018<br>2019 = time-step 2019–2023</p>
Nucleation-Limited Kinetics of GaAs Nanostructures Grown by Selective Area Epitaxy: Implications for Shape Engineering in Optoelectronics Devices
<p>This dataset corresponds to the following manuscript: </p> <p>Zendrini, M., Dubrovskii, V., Rudra, A., Dede, D., Fontcuberta i Morral, A., Piazza, V. “Nucleation-Limited Kinetics of GaAs Nanostructures Grown by Selective Area Epitaxy: Implications for Shape Engineering in Optoelectronics Devices” <em>ACS Applied Nano Materials 7,16 (2024):</em> 19065–19074</p> <p>DOI: <a href="http://doi.org/10.1021/acsanm.4c02765">doi.org/10.1021/acsanm.4c02765</a></p> <p>The dataset contains raw SEM images in .tif format for all the arrays of nanowires and nanomembranes discussed in the paper. The dataset also contains the AFM scans in .xyz format for all the arrays of nanowires and nanomembranes. The data for the morphological analysis are extracted from the SEM images and the AFM scans and they are collected in two separate .txt files for NWs and NMs.</p>
SERENA EJPSOIL SK SOIL Erosion ErosionControl
<div> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <div> <p><span><span>The present data was prepared according to the </span><span>methodology</span><span> of SERENA soil erosion control cookbook</span><span> for the territory of </span><span>Slovakia</span><span>. </span><span>The map of soil loss by water erosion (soil threat</span><span>) </span><span>was based on the </span><span>RUSLE model.</span> <span>or the soil erosion control, the difference between the erosion map without vegetation (C-factor = 1) and the erosion map with vegetation was calculated.</span></span><span> </span></p> </div> </div> <div> <p>The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. </p> </div> <div> <p>To create the soil loss map we used theese data: </p> </div> <div> <p> R factor - we used data from 100 automatic rain stations on minute rainfall for about 10-year period (national dataset) </p> </div> <div> <p>K factor – we used the source proposed in the cookbook from ESDAC dataset: Soil Erodibility (K- Factor) High Resolution dataset for Europe </p> </div> <div> <p>LS factor – we used the source proposed in the cookbook from ESDAC dataset: LS-factor (Slope Length and Steepness factor) for Slovakia </p> </div> <div> <p>C factor – we used LPIS database-this has information about crops on agricultural soil. We have values of C factor for all crops. </p> </div> <div> <p>P factor – we used the source proposed in the cookbook from ESDAC dataset: P factor for Slovakia. This map has values about 0.99 for Slovakia, so P-factor does not have much effect on the resulting erosion. </p> </div> <div> <p>The delivered map was prepared in GeoTIFF format in the resolution of 500 * 500 m. </p> </div>
Raw Data on Extracellular Particles in 613 Human and 163 Canine Diluted Plasma and Blood Samples Assessed by Interferometric Light Microscopy
<p><span>Extracellular nanoparticles (EPs) are cellular fragments. After being released in cell exterior, they become mediators of the cell-cell interaction. Their characterization in bodily fluids may reflect the clinical status of the organism. Here we present data on the number density <em>n</em> and hydrodynamic diameter <em>D</em><sub>h </sub>of EPs assessed directly in diluted plasma and blood by using a recently developed technique, Interferometric Light Microscopy (Romolo et al., 2022). The data are presented in the attached Table. </span></p> <p><span>We collected 613 blood and plasma samples from human patients with Inflammatory Bowel Disease (IBD) taken into tubes with trisodium citrate and ethylenediaminetetraacetic acid (EDTA) anticoagulants and 163 blood and plasma samples from canine patients with Brachycephalic Obstructive Airway Syndrome (BOAS). </span><span>The human study was conducted in accordance with the Declaration of Helsinki, and approved by the National Medical Ethics Committee of the Republic of Slovenia (0120-271/2022/4; KME 27 July 2022). All procedures in the animal study complied with the relevant Slovenian government regulations (Animal Protection Act, Official Gazette of the Republic of Slovenia, No. 43/2007). The animal study was approved by the Animals in Experiments Welfare Commission of the Veterinary Faculty, University of Ljubljana, approval number 18-3/2022-1. </span><span>Information regarding sample preparation is documented in the MIBlood-EV reports.</span></p> <div> <div> <div><span><a name="_msocom_1"></a></span></div> </div> </div>
Dataset: Electrolyte-dependent deposition morphology on magnesium metal utilizing MeMgCl, Mg[B(hfip)4]2 and Mg(HMDS)2–2AlCl3 electrolytes
<p>This is a collection featuring the data generated and used within the paper: 'Electrolyte-dependent deposition morphology on magnesium metal utilizing MeMgCl, Mg[B(hfip)4]2 and Mg(HMDS)2–2AlCl3 electrolytes'. The deposition behavior of two state-of-the-art electrolytes, magnesium tetrakis(hexafluoroisopropyloxy)borate (Mg[B(hfip)~4~]~2~) in dimethoxyethane (DME) and magnesium bis(hexamethyldisilazide) with two equivalents of aluminum chloride (Mg(HMDS)~2~-2AlCl~3~) in tetrahydrofuran (THF) was investigated. Using symmetric flooded magnesium-magnesium cells with different electrolyte concentrations and current densities the deposition process was monitored optically in-situ by a video microscope. The depositions were characterized by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) and compared to depositions from methylmagnesium chloride (MeMgCl) in THF, known for its dendritic growth. In this work, MeMgCl showed unidirectional growth and for the harshest applied conditions, mossy depositions, but no branching dendrites as reported in previous literature. Mg[B(hfip)~4~]~2~ and Mg(HMDS)~2~-2AlCl~3~ did not show the formation of dendrites or a dendrite preform but also did not result in a desired smooth layer but in spherical depositions. For the Mg[B(hfip)~4~]~2~ electrolyte, the influence of magnesium borohydride (Mg(BH~4~)~2~) as an additive was additionally tested resulting in a more planar growth.</p>
Data for "Breaking the Paywall: The role of Open Journal System as key Open Science infrastructure"
<h3><strong>Context</strong></h3> <p>This research was conducted within the NSF-SEEKCommons Project, a research initiative dedicated to supporting Open Science and Open Access in disciplinary research. The project has a special interest in understanding the role that critical infrastructure has in supporting open initiatives. The Open Journal System (OJS) serves as a long-standing fundamental piece for Open Access throughout the globe. Hence, it provides valuable information about experiences developing, deploying, and maintaining open technologies. </p> <h3><strong>Methods<br></strong></h3> <div> <div>We used mixed methods for our research, triangulating repository data, installation data, interviews, and documentary analysis. We collected repository data using a report generator (Kopp [2018] 2024) that uses repository metadata to present general statistics about a Git project. The resulting information was manually curated, disambiguated, and annotated to have a homogeneous set of developers with information about their institutional affiliation and country. </div> <div> </div> <div>Names are normalized based on the information in qualitative interviews and by browsing the full-extent commits in the GitHub repository. Other sources for this were the institutional materials (available in current and archived versions of the PKP website), meeting minutes, the user forum, and further project documentation available online. GitHub handles are homologated to their most comprehensive version. For institutional and country affiliation, we resorted to GitHub profiles, PKP documentation and forums, institutional domains available in emails, and researchers' ORCID IDs. </div> </div> <h3><strong>Available files</strong></h3> <ol> <li><strong>Information about the codebase</strong> (number of files, lines of code, and timestamp) organized by <strong>month, quarter, and semester. </strong><br>See file: OJS_GitStats_04-24.csv</li> <li>Information about the historical evolution of the codebase (number of files, lines of code, and timestamp), including <strong>a description of the top committers for each month</strong>. Commiters are described by including their institutional affiliation and country of origin. <br>See file: OJS_DevStats_Institution-Country_1.tsv</li> <li>Information about the <strong>historical evolution of the codebase </strong>focusing on <strong>top committers</strong>, along with their institution and country. This file is formatted to map the co-occurrence of developers and attributes by month between 2004-2024.<br>See file: OJS_DevStats_Institution-Country_2.tsv</li> <li>Selected fields to describe<strong> working and regularly maintained plugins for OJS as of October 2024.</strong> Includes name of the plugin, homepage, description, maintainer, and institutional affiliation. <br>See file: OJS_Plugins_2024_Processed.tsv</li> <li>Details of the aggregated <strong>information</strong> included in <strong>Table</strong> <strong>5</strong> of the article.<br>See file: OJS_Plugins_2024_Table5.tsv</li> <li><strong>Snapshot</strong> to XML information of the <strong>plugin gallery of OJS </strong>(October 21) retrieved from PKP website (Smecher 2024)<br>See file: OJS_Plugins_2024.csv</li> </ol> <h3>Funding</h3> <p><span>The SEEKCommons Project is funded by the U.S. National Science Foundation (NSF), grant #2226425</span></p>
SERENA EJPSOIL PL SOC LOSS SOC CONTENT
<p>General description of SERENA</p> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). Data was prepared based on the DSM approach using QRF algorithm. We used over 40000 points to create maps of SOC content for 2018 and climate change RCP 4.5 and 8.5 scenarios (https://opendap.4tu.nl/thredds/catalog/data2/uuid/e940ec1a-71a0-449e-bbe3-29217f2ba31d/catalog.html) to predict SOC content for 2050.</p>
SERENA EJPSOIL PL GHG NEP
<p>General description of SERENA</p> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). </p> <p><span>The present dataset corresponds to a map of Net Ecosystem Productivity (NEP) for Poland (agricultural areass) as wheat by the Eurocrop 2028 spatial product (d’Andrimont et al. 2021). </span>The map is the result of applying the NEP cookbook developed in SERENA/EJP-Soil to the area of interest (AOI) input data. NEP is expressed for a single 8-day period in early summer as it is the date with the highest value. The NEP value is a 2014 average for the particular 8-day period. <span>NEP was used as an indicator of greenhouse gasses and climate regulation supported by soils. </span></p>
SERENA EJPSOIL PL EROSION CONTROL SOIL MASS NOT ERODED
<p>General description of SERENA</p> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). The RUSLE method was used to prepare the attached files. </p>
SERENA EJPSOIL PL EROSION SOIL LOSS
<p>General description of SERENA</p> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). The RUSLE method was used to prepare the attached files. All attached GeoTIFFs were described below:</p> <p>SERENA_EJPSOIL_PL_EROSION__K_factor_2018.tif</p> <p>The result of modelling K factor - soil-erodibility factor </p> <p>SERENA_EJPSOIL_PL_EROSION_C_factor_2018.tif</p> <p>The result of modelling C factor - land cover and management factor</p> <p>SERENA_EJPSOIL_PL_EROSION_LS_factor_2018.tif</p> <p>The result of modelling LS factor - slope length and steepness factor (Source: https://esdac.jrc.ec.europa.eu/themes/slope-length-and-steepness-factor-ls-factor)</p> <p>SERENA_EJPSOIL_PL_EROSION_P_factor_2018.tif</p> <p>The result of modelling P factor - support practices factor (Source: https://esdac.jrc.ec.europa.eu/themes/support-practices-factor)</p> <p>SERENA_EJPSOIL_PL_EROSION_R_factor_2018.tif</p> <p>The result of modelling R factor -erosivity factor (rainfall event's ability to cause soil water erosion)</p> <p>SERENA_EJPSOIL_PL_EROSION_SOIL_LOSS_2018.tif</p> <p>Total soil erosion loss by water modelled for agricultural soils in Poland for 2018 (Map unit: <span>Mg ha<sup>−1</sup> yr<sup>−1</sup></span>)</p> <p>SERENA_EJPSOIL_PL_EROSION_SOIL_LOSS_MAX_EROSION_2018.tif</p> <p>Total maximum soil erosion loss by water modelled for agricultural soils in Poland for 2018 (Excluding C factor) (Map unit: <span>Mg ha<sup>−1</sup> yr<sup>−1</sup></span>)</p>
MyCeno
<h3>Summary</h3> <ul> <li>MyCeno_records.csv = records of fossil fungi.</li> <li>MyCeno_citations.csv = citations for fossil fungi record sources. (‘Citation_IDs’ are linked to ‘Source_of_record_IDs’ and ‘Supporting_literature_IDs’ in the records table).</li> <li>Myceno_column_info.csv = information about columns in the records table.</li> </ul> <p> </p> <h3>Version 2.0 improvements</h3> <ul> <li>Additional fields for taxonomic classification and type of fossil.</li> <li>Filtered to remove unreliable records.</li> </ul>
A large ensemble of CMIP6-based transient climate scenarios for impact assessment in Great Britain.
<p>Climate change impact assessments often require a large ensemble of local-scale transient climate scenarios. Each ensemble member represents plausible long weather series at a local scale. The climate projections from Global Climate Models (GCMs) are difficult to use at local scale due to their coarse spatial and temporal resolution. Moreover, very few projections are usually available for each GCM due to a high computational cost. An alternative approach involves employing a stochastic weather generator to produce a large number of transient scenarios based on the climate projections from GCMs. In a current dataset, transient climate scenarios were generated using the LARS-WG weather generator, based on climate projections from GCMs from the CMIP6 ensemble across 26 representative sites throughout the UK. Each transient scenario spans the period from 2020 to 2090. At each site, 100 transient scenarios were generated for two emission scenarios (SSP2-4.5 and SSP5-8.5) and five selected GCMs from CMIP6 (ACCESS-ESM1-5, CNRM-CM6-1, HadGEM3-GC31-LL, MPI-ESM1-2-LR, and MRI-ESM2-0). The choice of GCMs were based on their performance over northern Europe and their climate sensitivity. The use of a subset of GCMs substantially reduces computational time required for impact assessment, while allowing to quantify uncertainties in impacts related to uncertain future climate. The dataset can be used with impact models in various fields, including, land and water resources, agriculture and food production, ecology and epidemiology, and human health and welfare, when undertaking impact assessment of climate change and decision support for mitigation and adaptation.</p>
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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.