Find research datasets worth reusing

opencc-by-4.0Dec 2023View details →

Knowledge base for NBS for water treatment and stormwater management

Five tables containing: <ol> <li>nbs_catalog.csv: A catalogue of nature-based solutions for wastewater treatment and stormwater management. For each solution there is information on its performance, types of water, cobenefits, barriers and cost.</li> <li>sci_publications.csv: A list of scientific publications focused on one or several technologies of the above catalogue.</li> <li>sci_publications_treatment_details: For solutions for water treatment, a second table containing data about treatment performance extracted from previous scientific publications.</li> <li>description_nbs_catalog.csv: Descriptors for the catalogue.</li> <li>description_sci_publications_treatment_details.csv: Descriptors for the treatment performance data.</li> </ol> The most updated version of each table can be queried from https://snappapi-v2.icradev.cat/

opencc-by-4.0Sep 2024View details →

Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output without snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC

The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 without snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured an ideal cold-air pool formation followed by a interuption by a wind disturbance around midnight. Simulation output for the same night, but with snow cover is also available (Rauchöcker et al., 2024a). The temperature evolution of the measurements agreed much better with the simulation with snow cover and otherwise the same model setting compared to the simulation without snow cover (Rauchöcker et al. 2024d). Also available in a different dataset is output from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period. <h3>WRF Simulation Output</h3> This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al.,  2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021).  WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020. Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the simulation without snow cover. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file namelist.input that was used to generate the simulation results. Standard WRF output can be found in wrfout_40m_jan16_nosnow. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in windout_40m_jan16_nosnow. These variables were contained in the  unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in tend_40m_jan16_nosnow.nc.

opencc-by-4.0Aug 2024View details →

Movilidad académica en la trayectoria académica SNII-2023 de México. Datos Abiertos

Base de datos abiertos sobre movilidad en la trayectoria académica de los miembros del Sistema Nacional de Investigadoras e Investigadores (SNII) de México del año 2023. Se trata de una base de datos construida con información pública sobre el SNII en México, la fuente de información es el Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT) de este país. La información ha sido obtenida a través de la Plataforma Nacional de Transparencia, cuya solicitud de información pública cuenta con el Folio 330010923000951. Para más información sobre el acceso a la información pública se puede consultar el sitio web de dicha plataforma: <a href="https://www.plataformadetransparencia.org.mx/Inicio">https://www.plataformadetransparencia.org.mx/Inicio</a> Por tratarse de una base de datos construida con información pública, se ha publicado como una base de datos abiertos y se han anonimizado los registros borrando los nombres de las investigadoras e investigadores, así como el CVU que ha sido reemplazado por un identificados numérico diferente. Esta base de datos concierne a un total de 41367 miembros de este sistema. Esta base de datos y su análisis (en proceso) busca aportar elementos para el mapeo y la caracterización de la movilidad académica a lo largo de la formación y la trayectoria de las investigadoras e investigadores del SNII. La fuente de información concierne el padrón del SNII actualizado al año 2023, información pública sobre los grados académicos y las instituciones de obtención, así como información pública sobre la movilidad durante la trayectoria de los miembros del SNII (estancias posdoctorales, sabáticas, y otras). La construcción de esta base de datos se enmarca en el proyecto de investigación "Indicadores sobre la ciencia y la tecnología en el contexto de la Ciencia Abierta", cuya clave es IN302623 y financiamiento por la DGAPA-UNAM.

Dataset of "Nickel-cobalt spinel-based oxygen evolution electrode for zinc-air flow battery"

Following dataset provides all measured data that were collected on nickel (Ni) based electrodes for the oxygen evolution reaction. The electrodes were following: nickel (Ni) pristine mesh (PM), catalysed mesh (CM), nickel pristine foam (PF), catalysed foam (CF). Catalyst was NiCo2O4. Firstly, the catalysed electrodes were prepared and characterized by SEM, EDS and XRD. The electrodes were characterized in three different arrangements: in electrolysis non-flow arrangement, in a flow electrolysis cell and in ZAFB according to the manuscript.

The mass of the lowermost stratosphere (LMS): LMS mass calculation and trends in five reanalyses for the time period 1979–2019

Description Python code to calculate the mass of the lowermost stratosphere (LMS) and investigate LMS mass trends with the dynamic linear regression model (DLM, Laine et al. 2014, Alsing 2019) as presented in Weyland et al. (2024). The LMS mass is calculated via a three dimensioal integral, following Appenzeller et al. (1996), given an upper and lower LMS boundary surface (4D pressure fields). Here, the lateral boundary is determined via the intersection of the tropopause with the 350K isentrope (4D pressure field). The upper LMS boundary can be defined by the isentrope according to the potential temperature at the tropical lapse rate tropopause (PPT10mean) or the cold point (PPTcp10mean) or approximated by the 380K isentrope. See Weyland et. al (2024) for further description and context. The mass calculation is performed with calc_LMS_mass.py. The DLM trend analysis is conducted with dlm_LMS_mass.py, using dlm_modules.py. In order to be able to use the provided code, the dlmmc model code has to be downloaded from <a href="https://github.com/justinalsing/dlmmc">https://github.com/justinalsing/dlmmc</a> (Alsing 2019). The neccesary 3D (time, lat, lon) pressure fields to define the LMS boundaries are provided for the time period 1979–20191 from five modern reanalyses: ERA52 (Hersbach et al., 2020), ERA-Interim (Dee et al., 2011), MERRA-2 (Gelaro et al., 2017) and JRA-55 (Kobayashi et al., 2015) and JRA3Q (Kosaka et al., 2024): <ul> <li>lrtp*.nc : <ul> <li>3D (time, lat, lon) pressure, temperature and potential temperature at the WMO lapse rate tropopause for the time period 1979-20191, derived from monthly mean data on pressure levels from the respective reanalysis. The lapse rate detection algorithm closely follows that of Birner et al. (2010), based on the work of Reichler et al. (2003). The lapse rate tropopause can serve as the lower LMS boundary. The potential temperature at the lapse rate tropopause between 10°N-10°S is used to define a &bdquo;dynamic“ upper LMS boundary (PPT10mean).</li> </ul> </li> </ul> <ul> <li>cp*.nc : <ul> <li>3D (time, lat, lon) pressure, temperature and potential temperature at the cold point for the time period 1979–20191 , derived from monthly mean data on pressure levels from the respective reanalysis. The cold point here is defined by the pressure corresponding to a lapse rate of 0K/km. The potential temperature at the cold point between 10°N–10°S is used to define a &bdquo;dynamic“ upper LMS boundary (PPTcp10mean).</li> </ul> </li> </ul> <ul> <li>ppt10mean*.nc : <ul> <li>3D (time, lat, lon) pressure at the isentrope accroding to the potential temperature at the tropical (10°N–10°S) lapse rate tropopause (PPT10mean) for the time period 1979–20191, derived from lrtp*.nc. PPT10mean can be used to define the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>pptcp10mean*.nc : <ul> <li>3D (time, lat, lon) pressure at the isentrope accroding to the potential temperature at the cold point between 10°N-10°S (PPTcp10mean) for the time period 1979–20191, derived from cp*.nc. PPTcp10mean can be used to define the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>p380K*.nc : <ul> <li>3D (time, lat, lon) pressure at the 380K isentrope for the time period 1979–20191, derived from monthly mean data on pressure levels from the respective reanalysis. The 380K isentropic pressure field can be used to approximate the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>p350K*.nc : <ul> <li>3D (time, lat, lon) pressure at the 350K isentrope for the time period 1979–20191, derived from monthly mean data on pressure levels from the respective reanalysis. The 350K isentrope is used to determine the lateral LMS boundaries via its intersection with the tropopause. This intersection approximates the location of the subtropical jet streams and the maximum PV-gradient, marking a transport barrier. It is determined by the sign change of the pressure difference between the tropopause and the 350K isentrope.</li> </ul> </li> </ul> <ul> <li>my_enso_79-19.txt : <ul> <li>Regressor to account for El-Niño/Southern Oscillation for the time period 1979–2019. Source: <a href="https://psl.noaa.gov/enso/mei/">https://psl.noaa.gov/enso/mei/</a>, last accessed: 11 July 2023. The data has been normalized and centered around zero. The use of regressors is optional.</li> </ul> </li> </ul> <ul> <li>my_qbo30_79-19.txt and my_qbo50_79-19.txt : <ul> <li>Regressor to account for the quasi-biennial oscillation at 30 and 50 hPa for the time period 1979–2019. Source: <a href="https://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/qbo.dat">https://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/qbo.dat</a>, last accessed: 11 July 2023. The data has been normalized and centered around zero. The use of regressors is optional.</li> </ul> </li> </ul> <ul> <li>my_SAOD_79-19.txt : <ul> <li>Regressor to account for stratospheric (volcanic) aerosol optical depth for the time period 1979-2019. Source: <a href="https://asdc.larc.nasa.gov/project/GloSSAC/GloSSAC_1.0">https://asdc.larc.nasa.gov/project/GloSSAC/GloSSAC_1.0</a>, last accessed: 11 July 2023. The data has been normalized. The use of regressors is optional.</li> </ul> </li> </ul>   For further details see Weyland et al. (2024). 1Note that the ERA-Interim time series ends in 2018 and that the MERRA-2 time series starts in 1980. 2For the time period 2000–2006, the sub-reanalysis ERA5.1 replaces ERA5, correcting the reanalysis for a cold bias in the lower stratosphere (Simmons et al., 2020).   How to use – example: Assuming you are interested in the LMS mass between a lower boundary (-lb, e.g., the lapse rate tropopause) and an upper boundary (-ub, e.g., the 380K isentrope) in ERA5 for the entire Northern hemisphere (-lat=NH) covering the time period 1979-2019:   <ul> <li> Calculate the respective LMS mass timeseries: $ python calc_LMS_mass.py -lb=lrtp_ERA5.nc -ub=p380K_ERA5.nc -latb=p350K_ERA5.nc -lat=NH -fout=LMS_mass_ERA5_lrtp_p380K_NH.nc Isentropic pressure at 350K (-latb) is required to determine the lateral LMS boundary. The LMS mass time series together with an uncertainty estimate is saved to a netCDF file (-fout), e.g. &bdquo;LMS_mass_ERA5_lrtp_p380K_NH.nc“. </li> </ul>   <ul> <li> Perform a DLM trend analysis for your LMS mass time series, here LMS_mass_ERA5_lrtp_p380K_NH.nc (-mf) : Download the DLM model code (dlmmc) from <a href="https://github.com/justinalsing/dlmmc">https://github.com/justinalsing/dlmmc</a> (Alsing 2019) and save the &bdquo;dlmmc“ folder, containing the DLM modules in your working directory. </li> </ul> $ python dlm_lms_mass.py -mf=LMS_mass_ERA5_lrtp_p380K_NH.nc -s=2000 In this example, the DLM will provide 2000 samples (-s) after an additional 1000 warm-up samples. The DLM time series, containing 2000 samples (-s) per time step, is saved to a netCDF file. The name of the output file can be specifyed with -fout. Default is &bdquo;dlm_“ + mf, i.e. &bdquo;dlm_ LMS_mass_ERA5_lrtp_p380K_NH.nc“ in this example. The function dlm_lms_mass.dlm_lms_mass contains an option to visualize the DLM result (plot=True). Furthermore, it can be specified whether the DLM should be run with regressors (use_regressors=True) or without regressors (use_regressors=False). See the DLM documentation (Laine et al. 2014, Alsing 2019) for further options.   Funding: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742: “The tropopause region in a changing atmosphere”.

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2022-01-01 to 2022-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level. <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2022.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRCHEM_2022_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2023-01-01 to 2023-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level. <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2023.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRCHEM_2023_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2020-01-01 to 2020-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level. <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2020.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRCHEM_2020_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Werthmannstrasse (FRWRTM) from 2021-01-01 to 2021-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10. <ul> <li>Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2021.</li> <li>Average, minimum and maximum in-canopy air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRWRTM_2021_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Werthmannstrasse (FRWRTM) from 2020-01-01 to 2020-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10. <ul> <li>Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2020.</li> <li>Average, minimum and maximum in-canopy air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRWRTM_2020_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Werthmannstrasse (FRWRTM) from 2023-01-01 to 2023-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10. <ul> <li>Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2023.</li> <li>Average, minimum and maximum in-canopy air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRWRTM_2023_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Werthmannstrasse (FRWRTM) from 2022-01-01 to 2022-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban weather station at Freiburg-Werthmannstrasse (FRWRTM, 7.8447ºE, 47.9928, 277 m) using a passively ventilated and shielded temperature and humidity probe (Campbell Scientific Inc., CS 215) operated in a Stevenson Screen 2m above ground level in the vegetated backyard of Werthmannstrasse 10. <ul> <li>Quality controlled in-canopy air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2022.</li> <li>Average, minimum and maximum in-canopy air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRWRTM_2022_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

opencc-by-sa-4.0Jul 2024View details →

Light-regulated gene expression and alternative splicing data from rice seedlings.

This data contains analyzed data from the experiment conducted on rice seedlings under dark and light conditions. Seeds of rice (Oryza sativa spp. japonica cv. Nipponbare) were sown in the dark and germinated on day 2 and continued to grow in the dark for another 6 days. 3 biological replicates of the dark-grown etiolated shoots were harvested on day 8 after sowing. The remaining dark-grown seedlings were exposed to continuous white light at 120 mol/m2/sec for 48 hours or another 2 days (Days 9 and 10 after sowing). Three replicates of the light-treated green-colored seedling samples were harvested at the end of day 10. Harvested samples were frozen in liquid nitrogen and stored at -80C until further processing.

opencc-by-4.0Mar 2024View details →

Dataset for "Methodology for fast testing of carbon-based nanostructured 3D electrodes in vanadium redox flow battery"

Here, we describe a technique for integrating carbon-based rod-like nanomaterials into a vanadium redox flow battery and a methodology for fast nanomaterial performance testing. The technique is based on creating a fixed nanomaterial bed sandwiched between two graphite felt electrodes, forming a 3D flow-through electrode in the battery. Performing various positive and negative control experiments, we show the beneficial effect of a nanostructured bed on the primary battery characteristics obtained from short-term electrochemical experiments. We then characterize carbon nanotubes exhibiting promising electrochemical behavior in vanadium electrolytes, as observed in our previous study. The load curves obtained from charge-discharge steps at various current densities and electrolyte flow rates revealed considerable differences in the performance of the tested materials, with few-walled carbon nanotubes reaching unsurpassable characteristics. Although developed for vanadium redox flow batteries, the method enables testing tube-like and rod-like (nano-)materials as electrodes for other flow battery systems.

Sparse camera volumetric video applications. A comparison of visual fidelity, user experience , and adaptability. Supplementary Video

This video is a supplementary video material to the paper "Sparse camera volumetric video applications. A comparison of visual fidelity, user experience , and adaptability". It shows a comparision of five volumetric videos scenes, captured with three different sparse volumetric video applications. This video aims to visualize the difference in fidelity and artifacts that each system expresses.

opencc-by-4.0Aug 2024View details →

Methane concentrations and oxidation rates in land-terminating glacial runoff: measurements from three glacial rivers and a paraglacial lake in Iceland and a literature review

<div> This dataset contains methane measurements from Icelandic lakes and rivers during the summer of 2018 and 2019. This includes data from net methane oxidation assays with sediment and overlying water from one paraglacial lake and one glacial river, and surface methane concentration data from grab samples in 3 glacial streams and 15 Icelandic lakes (1 of which is paraglacial).  The dataset also contains methane concentration data from a synthesis of relevant aquatic ecosystems, used to compare against the original measurements collected.  </div> <div> Data and Literature Review Synthesis is supplement to Strock et al. 2024 Oxidation is a potentially significant methane sink in land-terminating glacial runoff published in Nature Scientific Reports.  <div> This study was funded by: National Geographic Society Changing Polar Systems grant (CP4-162R-18); In-kind support from the U.S. Geological Survey; Dickinson College Research and Development; Churchill Exploration Fund at Dickinson College  </div> </div>

opencc-zeroOct 2024View details →

Data for: Diminutive temnospondyls from the lower and middle Fremouw Formation (Lower Triassic) of Antarctica

This dataset contains the supporting data for the journal article, "Diminutive temnospondyls from the lower and middle Fremouw Formation (Lower Triassic) of Antarctica." Included are the phylogenetic character matrix (in .nex and .tnt formats) that was analyzed in TNT, the resultant 18 MPTs (.tre) recovered by the analysis, the skull length measurement data sourced from the literature for capitosaurs (.csv), a list of references used to source this measurement data (.pdf), and a README file with more metadata and details (.txt).

opencc-by-4.0Aug 2024View details →

Incriminations in the inquisition register of Bologna (1291-1310): network data and code

Cross-sectional (synchronic) projection of network data on incriminations (nominations of people in the criminal context of heresy trials) in the medieval inquisition register of Bologna, 1291–1310 in TSV format (tabulator-separated values), and R code for the article: Zbíral, David, Katia Riccardo, Tomá&scaron; Hampejs, and Zoltán Brys. ‘Gender, Kinship, and Other Social Predictors of Incrimination in the Inquisition Register of Bologna (1291–1310): Results from an Exponential Random Graph Model’. PLOS One 20, no. 2 (11 February 2025): e0315467. https://doi.org/10.1371/journal.pone.0315467. The data and analysis are described in the related article.

opencc-by-4.0Feb 2023View details →

LDEO pCO2-Residual Method

The ocean reduces human impacts on global climate by absorbing and sequestering CO2 from the atmosphere. To quantify global, time-resolved air-sea CO2 fluxes, surface ocean pCO2 is needed. A common approach for estimating full-coverage pCO2 is to train a machine learning algorithm on sparse in situ pCO2 data and associated physical and biogeochemical observations. Though these associated variables have understood relationships to pCO2, it is often unclear how they drive pCO2 outputs. Here, we make two advances that enhance connections between physical understanding and reconstructed pCO2. First, we apply pre-processing to the pCO2 data to remove the direct effect of temperature. This enhances the biogeochemical/physical component of pCO2 in the target variable and reduces the complexity that the machine learning must disentangle. Second, we demonstrate that the resulting algorithm has physically understandable connections between input data and the output biogeochemical/physical component of pCO2. The final pCO2 reconstruction agrees modestly better with independent data than most other approaches. Uncertainties in the reconstructed pCO2 and impacts on the estimated CO2 fluxes are quantified. Uncertainty in piston velocity drives substantial flux uncertainties in some regions, but does not increase globally integrated estimates of uncertainty in CO2 fluxes from observation-based products. Our reconstructed CO2 fluxes show larger interannual variability than smoother neural network approaches, but a lesser trend since 2005.

Astrophysical S-factors for H-burning stars

This dataset contains the latest recommendations of astrophysical S-factors for nuclear fusion reactions occurring in hydrogen-burning stars, included in the Solar Fusion III decadal review article (submitted for publication, e-print available at <a href="https://arxiv.org/abs/2405.06470" target="_blank" rel="noopener">arXiv:2405.06470</a>).   The data includes S-factors and their derivatives at zero energy (where available). That is, S(0), S´(0), S´´(0), in units of MeV·b, b, and b/MeV, respectively. Fractional uncertainties are also provided (marked as fr_err). Unavailable data are marked as NA.   This data was used to compute the <a href="https://zenodo.org/records/10822316">Standard Solar Models B23 / SF-III</a>.    For further information and references consult the Solar Fusion III  article linked above.