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12,237 results for “2019”
Urban Heat and Desert Wildlife: Rodent Body Condition Across a Gradient of Surface Temperatures in the greater Phoenix, Arizona (USA) metropolitan area (2019-2020)
We live-trapped wild rodents from seven field sites spanning three strata of land-surface temperatures in the Phoenix, Arizona (USA) metropolitan area. We captured 116 adult pocket mice (Chaetodipus spp. and Perognathus spp.) and Merriam’s kangaroo rats (Dipodomys merriami) during 2019 and 2020 from mountainous urban parks and open spaces. Animal body condition was quantified as percent body fat (i.e., fat mass divided by body mass). We used a noninvasive quantitative magnetic resonance instrument to measure body condition.
Atmospheric Gaseous Elemental Mercury Fluxes at Harvard Forest EMS Tower 2019-2020
In terrestrial ecosystems, dry deposition of atmospheric gaseous elemental mercury (GEM) is considered the dominant source of mercury accounting for 54% to 94% of mercury loads observed in soils, yet direct quantification of GEM deposition across forests is largely missing. The goal of this project is to quantify atmosphere-surface exchange of GEM at Harvard Forest for one full year, providing the first such record in a non-polluted forest. GEM exchange is measured using micrometeorological techniques using a large measurement tower, the only available method for direct, non-intrusive and time-extended measurements of net GEM exchange at the ecosystem level encompassing all underlying sinks and sources. A second objective was to partition GEM fluxes into canopy and soil contributions via deployment of two corresponding flux systems: one system was deployed above the forest canopy to measure ecosystem-level GEM exchange; a second system was deployed below the canopy to quantify soil contributions. This dataset contains an 18-month record of gaseous elemental mercury concentrations and fluxes measured at the EMS tower at Harvard Forest from May 2019 to August 2020.
Effects of Long-Term Soil Warming on Ecosystem Function at Harvard Forest 2019
Across biomes, soil biodiversity promotes ecosystem functions. However, whether this relationship will be maintained within ecosystems under climate change is uncertain. Here, using two long-term soil warming experiments, we investigated how warming affects the relationship between ecosystem functions and bacterial diversity across seasons, soil horizons, and warming duration. Soils were sampled from these warming experiments located at the Harvard Forest Long-Term Ecological Research (LTER) site, where soils had been heated +5°C above ambient for 13 or 28 years at the time of sampling. We assessed seven measurements representative of different ecosystem functions and nutrient pools. We also surveyed bacterial community diversity. We found that ecosystem function was significantly affected by season, with autumn samples having a higher intercept than summer samples in our model, suggesting a higher overall baseline of ecosystem function in the fall. The effect of warming on bacterial diversity was similarly affected by season, where warming in the summer was associated with decreased bacterial evenness in the organic horizon. Despite the decreased bacterial evenness in the warmed plots, we found that the relationship between ecosystem function and bacterial diversity was unaffected by warming or warming duration. Our findings highlight that season is a consistent driver of ecosystem function as well as a modulator of climate change effects on bacterial community evenness.
Chloride Concentrations, Conductivity, and Water Temperature Data from Lake Mendota and Lake Monona Madison, WI: December 2019 – April 2021
Conductivity and chloride were measured for 2 years in Lake Mendota and Lake Monona in Madison, WI. Conductivity was continuously measured (every 30 minutes) on under-ice buoys in the eplimnia (1-2m below the surface) and hypolimnia (1m off the bottom of the lake) of the lakes. Depth-discrete chloride grab samples were collected from the lakes quarterly. Profile sampling in Mendota, which is approximately 25 m deep, occurred every 5m from 0-20m and at 23.5m. Profile sampling in Monona, which is approximately 21m deep, occurred every 4m from 0-20m. This data was needed for a master’s research thesis with the goal of identifying the lakes' mixing dynamics and how salinization may impact them.
Cascade Project at North Temperate Lakes LTER – High-resolution Spatial Data for Whole Lake Experiments 2018 - 2019
Spatial measurements of water quality from Peter and Paul lakes in 2018 and 2019. In 2019, inorganic nitrogen and phosphorus were added to Peter Lake daily to cause an algal bloom while Paul Lake was an unmanipulated reference lake. In 2018, both lakes were sampled 1 time per week, while in 2019 lakes were sampled three times per week. Measurements were taken using the FLAMe sampling platform (Crawford et al. 2015, Environmental Science and Technology 49:442-450), which was driven in a grid pattern and recorded GPS coordinates and water measurements at 1Hz to create high resolution spatial maps.
Temperature data for Green Lake 4 inlet and outlet, 2019 - ongoing
This dataset contains temperature data from two Onset HOBO temperature pendant loggers installed in Green Lake 4’s inlet and outlet from summer 2019. High-resolution water quality data are fundamental to observing rapid ecological responses to meteorology, climate, and other disturbance events. The inlet and outlet temperature data collected here, together with Niwot Ridge’s buoy deployed in Green Lake 4, allow us to understand lake hydrology, water budget, and stratification and mixing dynamics that drive seasonal in-lake processes to understand effects of warming.
PIE LTER water-column nutrient and particulate transects along the Parker River Estuary, Massachusetts, 1994 - 2019.
Water chemistry including nutrient concentrations for various forms of N, P, C, as well as suspended sediments and light extinction coefficients, was determined from bi-annual nutrient transects along the Plum Island Sound estuary from the Parker River Dam to the mouth of the sound. Grab samples were taken at 11 sites along a 24 km transect from the mouth of the estuary to as near the dam at the head of the estuary as poosible. These samples have generally been collected in Spring and Fall and are done in conjunction with metabolism transects measuring dissolved oxygen levels. The Spring and Fall transects correspond to the high-flow, pre-growth season and the low-flow, post-growth season, respectively.
Groundwater, soil moisture, light and weather data in Brownsville forest, Nassawadox, VA, 2019-2023
This dataset includes groundwater, soil moisture, weather and light data collected in eight study sites in the Brownsville forested area (VA). In particular, sites H5 and H7 characterize the high forest where healthy Pinus taeda dominate, sites L1 and L6 characterize the low forest, where barren or dead Pinus taeda are present, sites M1 and M2 characterize the medium forest, representing transition between high and low forest, transition site where dead trees and saltmarsh vegetation live together and marsh site dominated by saltmarsh vegetation. Data collection, started in January 2019, is done for VCR-LTER and CCZN (Coastal Critical Zone Network) long term projects. CTD-Diver are used to measure groundwater pressure, specific conductance and temperature. They hang from a cable in six wells, one for each study site. Water pressure is compensated using barometric pressure data collected by the weather station nearby. Water levels are georeferenced to NAVD 88. Two soil moisture sensors for each site are placed 7-10 cm and 30 cm below the ground surface to detect water content, specific conductance and temperature of the first soil layer. Two weather stations are always installed in different sites. Their position is periodically changed to cover all sites. This material is based upon work supported by the National Science Foundation under Grant No. 2012322, Collaborative Research: Network Cluster: The Coastal Critical Zone: Processes that transform landscapes and fluxes between land and sea.
Invertebrates in a retreating coastal forest near Nassawadox, VA , 2019-2020
Sampling was conducted on the Eastern Shore of Virginia at Brownsville Preserve, part of the Virginia Coast Reserve LTER and adjacent to Upper Phillips Creek (37.463, -75.835). Plots were established in five zones along the forest-to-salt marsh gradient based on vegetation communities and seedling recruitment. The high forest (HF) is characterized by both coniferous and deciduous tree species, with maximum seedling recruitment. The mid forest (MF) contains primarily coniferous trees and shrubs and shows reduced seedling recruitment. The low forest (LF) canopy shows partial mortality due to salt stress and no tree recruitment; the understory contains primarily invasive (Phragmites australis) but also salt marsh (Spartina patens, Distichlis spicata) plant species. The transition zone (TZ) contains mostly dead mature trees, with its understory dominated by S. patens, D. spicata, and P. australis. The high marsh (HM) does not contain trees and is dominated by S. patens and D. spicata. 2019 data was collected September 16-18, 2019. 2020 data was collected September 8-9, 2020.
Forest Transition Experiment - Vegetation Monitoring on a Coastal Virginia Forest, 2019-2023
This dataset contains data on vegetation (shrubs, trees, non-woody vegetation, seedlings and Phragmites occurrence in permanent plots at the Brownsville Forest near Nassawadox, VA.
Abundance and Size of Seagrass-Associated Fishes in the Virginia Coastal Lagoons, 2019-2024
These data comprise annual summer estimates of the abundance (counts) and size (length) of fishes across restored seagrass meadows of the Virginia coastal lagoons. Fish were collected using a 25-ft (7.62-m) wide beach seine hauled by hand over a 25 m linear swath of the seafloor. Seine hauls were collected in June at 31 sites (1 haul per site). All fish caught in the seine were identified to lowest practical taxonomic level, counted, measured (total length), and released. Data collection began in June 2019 and continues annually (sampling was not carried out in 2020 due to logistical interruptions associated with the COVID-19 pandemic). Data on water temperature, salinity, and conductivity were collected while sampling occurred using a YSI 30 probe. Dissolved oxygen measurements were collected using a YSI ProODO probe. In 2019, these data were collected on at the top and bottom of the water column, but in 2021 and subsequent sampling only one observation (mid-water column) was made. To reconcile this difference for the combined data set, top and bottom environmental measurements from 2019 were averaged. Each fish collection site is co-located with a nearby synoptic site where long-term measurements of seagrass, sediments, and fauna are made. The relationship between site names and coordinates are given in Synoptic_fish_sites.csv. The sites where fish sampling occurred are different and are given by the "fish_sites" column, with coordinates for these sites under the "fish_longitude" and "fish_latitude" columns. Importantly, the coordinates of where sampling occurred will differ slightly between years without a change to the name of the site. Site geographic coordinates for individual years are in the PhysicalSamples.csv file. Sites are separated by at least 300 meters. In 2023, three new sites were added to represent unvegetated areas outside of but near the seagrass meadows. These sites are HI29, SPDR-bare, and SS-bare, and are designed to serve as references for se
Abundance, biomass, and length of seagrass-associated invertebrates in the Virginia coastal lagoons, 2019-2023
These data comprise annual summer estimates of the abundance (counts), biomass (dry mass), and individual lengths of infaunal and epifaunal invertebrates across restored seagrass meadows (eelgrass Zostera marina) of the coastal lagoons of Virginia, USA. Infauna were collected during low tide by hand using cylindrical benthic cores. Epifauna were collected during low tide using cubic weighted throw traps that were sampled with dip nets. Incidentally captured fishes are included in these data. In 2019-2022, 50 sites were sampled, using 3 replicates per sampling method per site. Beginning with 2023 sampling, two additional sites were added that are consistently bare of seagrass (unvegetated seafloor). At sites with patchy areas of seagrass and bare substrate, cores were collected within seagrass only and thus represent seagrass-associated fauna at those sites, rather than a spatially haphazard sample. At the few sites that lack seagrass, cores were collected in bare substrate. All cores were separated by 25 m. Regardless of substrate and seagrass conditions, throw traps were deployed haphazardly and separated by at least 10 m. In the laboratory, invertebrates were first sorted to broad taxonomic groups and later identified to lowest practical taxonomic level and enumerated. Most taxonomic groups were either dried and weighed by taxon or measured as individual length by specimen. Existing data include one table with counts and weights for broad taxonomic groups (2019-2023) and three tables related to lowest practical taxonomic identification (2019-2020), including one for counts and biomass, one for individual lengths, and one for taxonomic information. Data collection began in July 2019 and continues annually in June-July.
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>
Base de datos para validación CECAPEU 2019 EDU2017-83284-R.sav
<p>Base de datos de 1237 sujetos de tres universidades de la ciudad de Valencia, dos públicas (Universidad de Valencia y Universidad Politécnica de Valencia), y una privada (Universidad Católica de Valencia).</p><p>El trabajo realizado con esta muestra se centró en el diseño y validación de un cuestionario cuantitativo estandarizado para evaluar la adquisición de la competencia "aprender a aprender" en estudiantes universitarios, dadas las limitaciones de los actualmente disponibles. Para ello se hizo uso de un diseño de validación de pruebas. </p><p>Se usó un muestreo no probabilístico intencional, seleccionándose de cada universidad alumnado de una de sus grandes áreas de conocimiento: Ciencias de la Salud (UVEG), Ingenierías y Arquitecturas (UPV), y Educación (UCV). Se pretendía lograr una muestra lo suficientemente variada y representativa de grandes ámbitos/áreas de conocimiento diferentes, cada uno de una de estas tres universidades.</p><p>El producto final es un cuestionario con cinco dimensiones/escalas (cognitiva, metacognitiva, afectivo-motivacional, social-relacional y ética), veintiuna subdimensiones/ subescalas y 85 ítems, más sólido y completo que los anteriormente disponibles. Es un instrumento que permite el avance del conocimiento en este ámbito y que será útil para los investigadores, sirviendo para el diagnóstico y evaluación de la competencia y para contrastar resultados en muestras amplias de población.</p><p> </p><p>La base de datos, en SPSS 25.0, está integrada por 206 variables, siendo las seis primeras nominales y las restantes escalares, con valores que van de 1 (máximo desacuerdo) a 5 (máximo acuerdo). De la variable 7 a la 204 recogen resultados de los 198 ítems iniciales, diseñados antes de su validación estadística definitiva, del cuestionario CECAPEU, que evalúa el aprendizaje de la competencia "aprender a aprender" del alumnado universitario. Las variables 205 y 206 recogen los datos de dos ítems recodificados.</p><p>En el fichero que se ha subido, en txt, junto a la base de datos, se explican las variables y otras cuestiones relativas a la base de datos.</p><p> </p><p> </p>
Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2019 Dataset
<p>High-throughput computational screening of metal-organic frameworks rely on the availability of<strong><em> </em></strong>atomic coordinate files which can be used as input to simulation software packages. CoRE MOF Datasets are derived from Cambridge Structural Database (CSD) and also from the World Wide Web.</p> <p><strong>Nomenclatures:</strong></p> <p>LCD (Largest Cavity Diameter), PLD (pore limiting diameter), LFPD (Largest Sphere along the Free Path), ASA (Accessible Surface Area), NASA (Non-accessible surface area), AV_VF (Void Fraction, 0 - 1), NAV (Non Accessible Volume)</p> <p><strong>Dataset Directory Organization</strong></p> <p>CoREMOF2019_public_v2.zip: dataset with CR and NCR classifications</p> <p>1. CR dataset: computaion-ready (<em>N</em> = 10,367)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 6,603)</li> <li> FSR: free solvent removed (<em>N</em> = 3,764)</li> </ul> <p>2. NCR: not computaion-ready (<em>N</em> = 8,714)</p> <ul> <li> ASR: all solvent removed (<em>N</em> = 5,417) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 2,597)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 958)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,859)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> <li> FSR: free solvent removed (<em>N</em> = 3,297) <ul> <li>Both: NCR determined by Chen_Manz and mofchecker (<em>N</em> = 1,646)</li> <li>Chen_Manz: NCR determined by Chen_Manz (<em>N</em> = 463)</li> <li>mofchecker: NCR determined by mofchecker (<em>N</em> = 1,185)</li> <li>PACMAN_fail: NCR determined by Chen_Manz and mofchecker, and fail to predict PACMAN charges (<em>N</em> = 3)</li> </ul> </li> </ul> <p>2. NCR_detail.xlsx: details of all structures by mofchecker and Chen_Manz for each NCR cases</p> <p><strong>November, 24 2024</strong></p> <ul> <li>Re-ordering of folders such that top level directory is based on computation-ready and not-computation ready classification.</li> </ul> <p><strong>November, 13 2024</strong></p> <ul> <li>Classification of Computation-Ready (CR) and Not Computation-Ready (NCR) Structures based on <a href="https://pubs.rsc.org/en/content/articlelanding/2020/ra/d0ra02498h">Chen & Manz</a> (RSC Adv., 2020,10, <a>26944-26951</a>) and <a href="https://github.com/kjappelbaum/mofchecker">MOFChecker </a>program by <a href="https://github.com/kjappelbaum">Kevin M. Jablonka</a>)</li> <li>ML-predicted DDEC6 partial atomic charges based on <a href="https://github.com/mtap-research/PACMAN-charge">PACMAN</a></li> </ul> <p><strong>Acknowledgements</strong></p> <ul> <li>This reserach is supported by the National Research Foundation of Korea (No. 2016R1D1A1B3934484, NRF-2020R1C1C1010373, RS-2024-00449431)</li> <li>This research is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-17ER16362 (Predictive Hierarchical Modeling of Chemical Separations and Transformations in Functional Nanoporous Materials: Synergy of Electronic Structure Theory, Molecular Simulations, Machine Learning, and Experiment)</li> </ul>
The mass of the lowermost stratosphere (LMS): LMS mass calculation and trends in five reanalyses for the time period 1979–2019
<p><strong>Description</strong></p> <p>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.</p> <p>The mass calculation is performed with calc_LMS_mass.py.</p> <p>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).</p> <p>The neccesary 3D (time, lat, lon) pressure fields to define the LMS boundaries are provided for the time period 1979–2019<sup>1</sup> from five modern reanalyses: ERA5<sup>2</sup> (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):</p> <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 1979-2019<sup>1</sup>, 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 „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 1979–2019<sup>1 </sup>, 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 „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 1979–2019<sup>1</sup>, 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 1979–2019<sup>1</sup>, 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 1979–2019<sup>1</sup>, 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 1979–2019<sup>1</sup>, 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> <p> </p> <p>For further details see Weyland et al. (2024).</p> <p><sup>1</sup>Note that the ERA-Interim time series ends in 2018 and that the MERRA-2 time series starts in 1980.</p> <p><sup>2</sup>For 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).</p> <p> </p> <p><strong>How to use – example: </strong></p> <p>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:</p> <p> </p> <ul> <li> <p>Calculate the respective LMS mass timeseries:</p> <p><strong>$ 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</strong></p> <p>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. „LMS_mass_ERA5_lrtp_p380K_NH.nc“.</p> </li> </ul> <p> </p> <ul> <li> <p>Perform a DLM trend analysis for your LMS mass time series, here LMS_mass_ERA5_lrtp_p380K_NH.nc (-mf) :</p> <p>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 „dlmmc“ folder, containing the DLM modules in your working directory.</p> </li> </ul> <p><strong>$ python dlm_lms_mass.py -mf=LMS_mass_ERA5_lrtp_p380K_NH.nc -s=2000</strong></p> <p>In this example, the DLM will provide 2000 samples (-s) after an additional 1000 warm-up samples.</p> <p>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 „dlm_“ + mf, i.e. „dlm_ LMS_mass_ERA5_lrtp_p380K_NH.nc“ in this example.</p> <p>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).</p> <p>See the DLM documentation (Laine et al. 2014, Alsing 2019) for further options.</p> <p> </p> <p><strong>Funding</strong>: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742: “The tropopause region in a changing atmosphere”.</p>
Monthly aggregated GLASS FAPAR V6 (250 m): 95th percentile monthly time-series (2019)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
Monthly aggregated GLASS FAPAR V6 (250 m): 5th percentile monthly time-series (2019)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
Polarized and nonpolarized Twitter networks from the 2019 Finnish Parliamentary Elections
<p><strong>Polarized and nonpolarized Twitter networks from the 2019 Finnish Parliamentary Elections</strong></p> <p>This dataset includes 183 Twitter retweet networks collected during the 2019 Finnish Parliamentary Elections.</p> <p>The first 150 networks are built around single hashtags, such as #police, #nature, and #immigration. The remaining 33 networks are constructed using a combination of hashtags focused on specific topics like climate change and economic policy.</p> <p>Each filename consists of two parts: the first part indicates whether the network is based on a single hashtag (in lowercase) or a set of hashtags (in uppercase). The second part represents the tweet period.</p> <ul> <li> <p>"p1" corresponds to the pre-election period (March 1 to April 14).</p> </li> <li> <p>"p2" corresponds to the inter-election period (April 15 to May 26).</p> </li> <li> <p>"p3" corresponds to the post-election period (May 27 to July 31).</p> </li> </ul> <p>The nodes in the networks represent anonymized Twitter accounts, and directed ties indicate retweet endorsements on specific topics. Each file contains three columns: retweeter, retweeted, and weight.</p> <p>Please see the references for more details.</p> <p>Network labels, whether they are labeled as controversial, and whether they are based on single or multiple hashtags, can be found in the "networks_info.csv" file.</p> <p>Importantly, the dataset does not contain any identifying information or original raw data from the Twitter platform. Anonymization was achieved by shuffling the order of unique nodes across all networks and assigning each node a new identifier (ID). These new IDs were then applied to the edgelists to obtain the anonymized version.</p> <p>Kindly ensure to reference the original article(s) when utilizing this dataset.</p> <p>Chen, T. H. Y., Salloum, A., Gronow, A., Ylä-Anttila, T., & Kivelä, M. (2021). Polarization of climate politics results from partisan sorting: Evidence from Finnish Twittersphere. <em>Global Environmental Change</em>, <em>71</em>, 102348. <a href="https://doi.org/10.1016/j.gloenvcha.2021.102348">https://doi.org/10.1016/j.gloenvcha.2021.102348</a></p> <p>Salloum, A., Chen, T. H. Y., & Kivelä, M. (2022). Separating polarization from noise: comparison and normalization of structural polarization measures. <em>Proceedings of the ACM on human-computer interaction</em>, <em>6</em>(CSCW1), 1-33. <a href="https://doi.org/10.1145/3512962">https://doi.org/10.1145/3512962</a></p>
Concentration of dissolved organic carbon in water samples taken from the Upper Clark Fork River (Montana, USA) during water year 2019 (1 Oct 2018 - 30 Sep 2019)
These data were collected to support monitoring of the Upper Clark Fork River restoration, and data collection was funded by the US NSF Long Term Research in Environmental Biology (LTREB) program and the US NSF EPSCoR funded Montana Consortium for Research on Environmental Water Systems. The LTREB monitoring project consists of monthly or bi-weekly water quality monitoring across a 200-km restoration gradient contaminated by historic mining practices to monitor inorganic phosphorus and nitrogen concentrations, biotic standing stocks, and heavy metal contamination. The original analytical intent for these data was to assess the response of river dissolved organic carbon to the floodplain restoration. Data are Aurora Total Organic Carbon combustion analyses of the concentration of organic carbon dissolved in filtered samples of well-mixed river thalweg water. Data are from the 2019 water year (1 Oct 2018 to 30 Sep 2019). Data were collected on the Upper Clark Fork River (USGS HUC 17010201) at project sites distributed along the river from the vicinity of Anaconda to Missoula, Montana, USA.
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