Find research datasets worth reusing

GIS30 GIS Coverages Defining Sample Locations for Abiotic Datasets on Konza Prairie (1972-present)

These data show sample locations for various abiotic data collected on Konza Prairie (rain gauges, soil moisture, and stream data). Included in these data are the locations for 12 rain gauges (GIS300) on Konza Prairie. The Konza headquarters weather station formerly consisted of two gauges which were operated year-round. The Konza headquarters weather station currently consists of one Otto-Pluvio2 gauge which is operated year-round. The remaining Konza-operated gauges run from April 1 to November 1. These data are to be used in conjunction with the APT01 (precipitation) dataset. GIS305 defines the locations where measurements of soil moisture (%volume) are taken on Konza Prairie. These data are to be used in conjunction with the ASM01 (soil moisture) dataset. GIS309 defines the locations within watershed N4D of soil sampler nests. In Jan 2020, we separated the original GIS310 file 'Wells in N4D' into GIS310 'Wells in N4D' and GIS309 'Soil Sampler Nests'. Prior to then, soil sampler nests and wells were combined in GIS310. GIS310 defines the locations within watershed N4D where samples are taken for analyzing the belowground water chemistry of the watershed. These data are to be used in conjunction with the AGW01 dataset. GIS311 defines the locations of 14 wells at two sites along Kings Creek. Depth and nutrient content of groundwater is measured at these sites. These data are to be used in conjunction with the AGW02 dataset. GIS315 defines the locations of stream sampling stations within multiple Konza watersheds. These data are to be used in conjunction with the NWC, ASS, ASD, and ASW datasets. GIS320 defines the locations of the rainfall collectors used to collect the samples analyzed as a part of the National Atmospheric Deposition Program. These data are to be used in conjunction with the ANA01 dataset. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).

GIS35 GIS Coverages Defining Sample Locations for Belowground Datasets on Konza Prairie (1982-present)

These data show the locations of research conducted at the below ground plots near Konza Headquarters. Record type 1 (GIS350) describes the 64 belowground plots receiving a variety of nutrient, burn, and mowing treatments. Data for BMS01, BMS02, and BNS01 are collected on these plots. Record type 6 (GIS355) describes the locations of the Micro-Rhizotrons. Two spatial datasets lie on the belowground plots, but are classified separately. These are the Lysimeters on belowground plots (GIS455) and Aboveground biomass on belowground plots (GIS505) datasets. GIS505 may be used alongside the BGPVC dataset, because it shares sample locations with PBB01. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS40 GIS Coverages Defining the Sample Locations of Konza Consumer Data (1982-present)

These data show the sampling locations for the consumer datasets at Konza Prairie. GIS400 defines the starting points for sweep samples of grasshoppers across Konza Prairie. These data may be used in conjunction with the sweep sample datasets (CGR02). GIS401 defines the starting points for sweep samples of grasshoppers across Konza Prairie, focusing on grazing impact. These data may be used in conjunction with the sweep sample datasets (CGR02Z). GIS405 defines the trap locations for small mammal sampling across Konza Prairie. These data may be used in conjunction with CSM0X. GIS 406 defines the locations of small mammal host parasite sampling at Konza Prairie. These data may be used in conjunction with CSM08. GIS410 defines the stream stretches for fish sampling across Konza Prairie. These data may be used in conjunction with CFC01. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS45 GIS Coverages Defining the Konza Nutrient Data Sample Locations (1982-present)

These data show the sample locations for soil bulk density and chemical characteristics along LTER vegetation plots. This dataset contains the transect lines (GIS450) and sample locations(GIS451) at which the soil cores are sampled. These data may be used in conjunction with the Soil Chemistry and Bulk Density (NSC01) datasets. GIS455 contains the locations of the lysimeters used to measure soil water chemistry on the belowground plots. These data may be used in conjunction with the NBS01 dataset. GIS460 contains the locations of the bulk precipitation collectors on Konza Prairie. These data may be used in conjunction with the NBP01 dataset. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS50 Coverages Defining the Konza Producer Data Sample Locations (1982-present)

These data show the sample locations for datasets pertaining to primary production at Konza Prairie. These data reference various treatments across Konza including varying burn frequencies, belowground plots, patch burn, exclosures, etc.Record type one (GIS500) contains sample locations for estimated standing crop biomass in various burning-grazing treatments (PABXX). Record type six (GIS505) contains sample locations for peak foliage biomass measured at the belowground plot experiments (PBBXX).Record type 11 and 12 contain the transect (GIS510) and plot (GIS511) locations for plots in the patch-burn experiments (PBGXX). Record type 16 (GIS515) contains the locations of exclosures used to sample primary productivity in bison grazed watersheds (PEB01). Record type 21 (GIS520) contains the locations of exclosures used to sample primary productivity in cattle grazed watersheds (PEB01X). Record type 26 (GIS525) contains the locations of sample sites for litterfall (PGLXX) collectors in the gallery forest. Record type 31 (GIS530) contains species composition transects, and (GIS531) provides locations for species transect plots in the patch-burn experiments for Konza Prairie. These data may be used in conjunction species composition (PVC01 and PVC02), primary production in grazing exclosures (PEB01, PEB01_X), soil chemistry and bulk density (NSC01) and primary production (PAB01). These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).

GIS55 GIS Coverages Defining the Konza HQ Irrigation System (1982-present)

These data show the components of the irrigation system near Konza Prairie HQ. Record types 1, 2, 3 and 4 demarcate the locations of the study plots heads (GIS550), transect lines (GIS551), irrigation lines (GIS552), and irrigation line joints (GIS553). Record types 4 and 5 describe the location of the storage piles (GIS554) and the irrigation reservoir (GIS555). This data may be used in conjunction with the Irrigation Transect Studies (WATXX) data. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz), and associated EML metadata (.xml).

GIS60 GIS Coverages Defining Other Konza Sample and Research Areas (1982-present)

These data show locations of samples and research areas at Konza that do not fit under our standard classifications. GIS 600 contains the locations of the Hulbert plots on Konza Prairie. GIS605 contains locations for rainfall shelters, ramps, experimental streams, restoration plots, the weather station, grasshopper cages, the climate extremes project. Currently no associated LTER datasets exist for these locations. GIS 610 provides a record of the historic Konza gridded location system. Older datasets may reference these locations with a column letter and row number. GIS615 contains the location for the Clean Air Status and Trends Network (CASTNET) site on Konza Prairie. For more information, visit the following link: http://www.epa.gov/castnet/javaweb/site_pages/KNZ184.html. GIS620 contains the location for the USGS gauging station. These data may be used in conjunction with the Stream Discharge for Kings Creek Measured at USGS Gauging Station (ASD01) dataset. For more information, visit the following link: http://waterdata.usgs.gov/nwis/nwisman/?site_no=06879650. GIS630) and GIS635 contain the location and treatment information for two bison grant grazing experiments. Currently, no associated LTER datasets exist for these data. These data are available to download as zipped shapefiles (.zip), compressed Google Earth KML layers (.kmz).

openCC (other)Dec 2023View details →

North Temperate Lakes LTER: High Frequency Meteorological and Metabolism Data - Crystal Bog Buoy 2005 - present

The instrumented buoy on Crystal Bog is equipped with a dissolved oxygen sensor, a thermistor chain, light sensor, and in the past, meteorological sensors that provide fundamental information on lake thermal structure and lake metabolism. A surface buoy was used from 2005 - 2014, which included a met station beginning 2006. Since then, only a subsurface buoy as been used (no met station), and includes frequent over-winter deployments under the ice. The thermistor chain data is included in a separate dataset (knb-lter-ntl.119). Hourly and daily averages are provided for the met data, while hourly averages are provided for the dissolved oxygen and light data. To accommodate the under-ice deployments, sensor depths vary somewhat year-to-year. Light data is collected with HOBO pendant light and temperature sensors. The make of the dissolved oxygen sensor has changed over the years: YSI (2005), D-Opto (2006-2014), and PME miniDOT (2015-present).

openCC (other)Dec 2023View details →

North Temperate Lakes LTER: High Frequency Meteorological and Metabolism Data - Trout Bog Buoy 2003 - present

The instrumented buoy on Trout Bog is equipped with a dissolved oxygen sensor, a thermistor chain, light sensor, and in the past, meteorological sensors that provide fundamental information on lake thermal structure and lake metabolism. A surface buoy was used from 2003 - 2014, which included a met station (air temp and winds). Since then, only a subsurface buoy as been used (no met station), and includes frequent over-winter deployments under the ice. The thermistor chain data is included in a separate dataset (knb-lter-ntl.70). Hourly and daily averages are provided for the met data, while hourly averages are provided for the dissolved oxygen and light data. To accommodate the under-ice deployments, sensor depths vary somewhat year-to-year. Light data is collected with HOBO pendant light and temperature sensors. The make of the dissolved oxygen sensor has changed over the years: Greenspan DO (2003-2005), D-Opto (2006-2014), and PME miniDOT (2015-present).

openCC (other)Mar 2024View details →

Small Mammal Mark-Recapture Population Dynamics at Core Research Sites at the Sevilleta National Wildlife Refuge, New Mexico (1989-present)

This file contains mark/recapture trapping data collected from 1989-present on permanently established web trapping arrays at sites on the Sevilleta National Wildlife Refuge in central New Mexico.. The trapping sites are representative of Chihuahuan Desert Grassland, Chihuahuan Desert Shrubland, Pinyon-Juniper Woodland, Juniper Savanna, Plains-Mesa Sand Scrub and Blue Grama Grassland. Not all sites have been trapped for the entire period: goatdraw (1992-2008), blue grama (2002-2004) rsgrass (1989-1998), rslarrea (1989-2009), two2 (1989-1998), savanna (1999-2002). Only 2 sites have been continuously been sampled since 1989 (5pgrass and 5plarrea). At each site 3 trapping webs are sampled for 3 consecutive nights in spring and fall. Each trapping web consists of 145 rebar stakes numbered from 1-145. There are 148 traps deployed on each web: 12 along each of 12 spokes radiating out from a central point (stake #145) plus 4 traps placed at the center of each web. The wide format facilitates community composition and species diversity analyses. Wide format has been reshaped so that the count data for each species are presented in a unique column. Data are summarized for each trapping web X trapping bout to present the mean number of animals per trap per night of the trapping bout. Wide format fills zeros for species that were not captured on a web during a given trapping bout. Long format facilitates filtering the dataset to a particular small mammal species of interest, but this format requires the addition of zeros to be functional for accurate data analysis requiring counts of animals.

opencc-by-4.0Jul 2020View details →

Towards a generic processingand presentation ofTEI encoded digital editions

This dataset is the basis for the talk given at the TEI Member's Meeting 2014, Evanston, IL The abstract of the paper submitted: The set of XSL stylesheets provided and maintained by the TEI is relatively cautious about processing and presentation of transcriptions and content of digital editions. Only very basic functions are implemented, such as to surround abbreviations with brackets or to process from the element choice in plain mode only those children that represent the "critical" reading. Processing in plain mode means that the elements will be treated as in-line elements.[1] On the other hand the encoding must have been done with a special purpose. A general rule of text encoding is that the editor may encode only those structures and semantic features that he wants to process in the end. The processing might include elaborated examination and analysis of the encoded text or more complex queries as well as a reproduction of visual properties of the original document or provide a (simplified) reading text. Thus the encoding will tell something about the functionalities of the text in processing and presentation. According to Patrick Sahle's "Textrad"[2], "the" text does not exist in a transcription but the encoded text usually represents multiple properties and serves multiple purposes. Whatever the editor might state in some introductory notes and the documentation of the edition which should contain some statements about the encoding used, in the end the encoded text will speak on its own, can be interpreted and will be processed as is. In succession of the modelling of the TEI, realised in the modules, the grouping of elements and of attributes, the semantics of certain elements might let the processor estimate about the foreseen presentation, processing, and use: - The elements <pb>, <lb>, <l>, <lg>, etc. as well as attributes @rend, @rendition or @style represent visual aspects of the text, therefore these might have to be reproduced; users may be given a choice to either see a document-centred view which visualises these aspects or switch to an editorial view on the text which eliminates these properties. - The same applies to the element <choice>: If this is used the editor must have had in mind the opportunity to change the views on the document respectively encoded text. - Entities encoded as <rs>, <name>, <persName>, <placeName>, etc might be referenced, especially if they are accompanied by the related list elements such as <listPerson>, <listPlace>, etc. Additionally, one might assume that there will be norm data available which allows for links into the open. - Bibliographic records (<bibl>, <msDesc>) will serve a similar purpose and will have to be referenced. - Quotations like <cit>, <foreign>, <q>, <quote> etc. will have to be distinguished from the surrounding text. Concerning the overall structure of an (critical) edition one might expect up to three apparatuses: The critical apparatus, the commentary and maybe a bibliographical apparatus. How many of these are present in a given edition is up to the editor but the presence of certain elements and especially of the amount of certain elements will give anybody an idea of how many apparatuses are "appropriate": If the encoding contains editorial elements like <choice>, <abbr>/<expan>, <add>/<del>, etc. the representation of this information in an apparatus will be inevitable. If a certain amount of bibliographic references point to biblical or classical texts the tradition of the publication of editions has provided a separate apparatus as well. Last, editorial notes will have to be distinguished from the former two categories. This paper will examine existing editions with statistical methods and by clustering the elements used it might be possible to assign the encoded text to one or more text types of Sahle's typology. Additionally, the paper shall foster the discussion about the presentation of an edited text according to the intended purpose of the encoding. On the basis of the typology and purpose of the edition it will be more likely that a generic presentation of any edited text is possible. Finally, with some statistical data about the editions some remarks about the interoperability of the TEI-encoded texts shall be possible. [1] e.g. https://github.com/TEIC/Stylesheets/blob/master/html/html_core.xsl [2] Patrick Sahle: Digitale Editionsformen, 3 vols. 2013, esp. vol. 3, p. 9ff.

opencc-by-4.0Aug 2024View details →

ChinaHighPM₁₀ (2022–Present)

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM10 dataset for China from 2022 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.90, a root-mean-square error (RMSE) of 21.12 µg m-3, and a mean absolute error (MAE) of 11.22 µg m-3 on a daily basis. If you use the ChinaHighPM10 dataset in your scientific research, please cite the following reference (Wei et al., EI, 2021): <ul> <li> Wei, J., Li, Z., Xue, W., Sun, L., Fan, T., Liu, L., Su, T., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-EI-2021.pdf">The ChinaHighPM10 dataset: generation, validation, and spatiotemporal variations from 2015 to 2019 across China</a>. Environment International, 2021, 146, 106290. https://doi.org/10.1016/j.envint.2020.106290 </li> </ul> The data for the period 2000–2021 are accessible at: <a href="https://doi.org/10.5281/zenodo.3752465">https://doi.org/10.5281/zenodo.3752465</a> 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>

opencc-by-4.0Jul 2023View details →

GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present

Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional investigation of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940-2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent datasets such as the land-ocean mass budget, large-scale water balance in 341 large river basins, and streamflow measurements at 10,168 gauges. We find that the proposed approach performs overall as well as or is more reliable than previous TWS datasets. Moreover, our reconstructions successfully reproduce the impact of climate variability, such as strong El Niño events. GTWS-MLrec dataset consists of three reconstructions based on JPL, CSR and GSFC mascons, three detrended and de-seasonalized reconstructions, and six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a broad range of applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management.Please cite the reference: Yin J, Slater L, Khouakhi A, et al. GTWS-MLrec: Global terrestrial water storage reconstruction by machine learning from 1940 to present. Earth System Science Data. 2023.For any inquiry about the dataset, welcome to contact Dr. Jiabo Yin (jboyn@whu.edu.cn).

opencc-by-4.0Apr 2023View details →

USHighPM₂.₅: Daily Seamless 1 km Ground-Level PM₂.₅ Dataset for the United States (2000–Present)

USHighPM2.5 is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for the United States (i.e., USHighAirPollutants, USHAP). 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. Here is the first big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM2.5 dataset for the United States from 2000 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.92 and a normalized root-mean-square error (NRMSE) of 0.4 on a daily basis. If you use the USHighPM2.5 dataset in your scientific research, please cite the following reference (Wei et al., LPH, 2023): <ul> <li>Wei, J., Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. <a href="https://weijing-rs.github.io/publications/Wei_et_al-LPH-2023.pdf">Long-term mortality burden trends attributed to black carbon and PM2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study</a>. The Lancet Planetary Health, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8</li> </ul> More USHAP 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>

opencc-by-4.0Jun 2023View details →

KDE Representations of the Gravitational Wave Background Free Spectra Present in the NANOGrav 15-Year Dataset

OVERVIEW----------------This is a downloadable file of probability densities from KDEs (Kernel Density Estimator) of free spectrum analyses of the NANOGrav 15yr Dataset (DOI <a href="https://doi.org/10.5281/zenodo.7967584">10.5281/zenodo.7967584</a>) that can be used with the <a href="https://github.com/astrolamb/ceffyl">Ceffyl</a> and <a href="https://github.com/andrea-mitridate/PTArcade">PTArcade</a> packages. Please see the GitHub page for Ceffyl/PTArcade installation details and usage information. Details on how this data product was produced can be found in <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.103019">Lamb, Taylor & van Haasteren 2023 (DOI 10.1103/PhysRevD.108.103019).</a>DIRECTORY AND FILE STRUCTURE----------------------------------------------------Each directory contains a file with log-pdfs representing their corresponding free spectra (`density.npy`), the frequencies at which they were analysed (`freqs.npy`), the grid-points at which each log-pdf was computed (`log10rhogrid.npy`), frequency labels (`log10rholabels.txt`), analysis label (`pulsar_list.txt`), an array of bandwidths computed using the Sheather-Jones method (see Lamb et al. 2023; 'bandwidths.npy'), and a file with some metadata about the data (`log.txt`)../30f_fs{cp}_ceffyl <ul><li>A representation of a 30 frequency CURN free spectrum.</li></ul>./30f_fs{hd}_ceffyl<ul><li>A representation of a 30 frequency HD-correlated free spectrum</li></ul>./30f_fs{hd+mp+dp}_ceffyl_hd-only<ul><li>A representation of an analysis that simultaneously modeled a HD-correlated free spectrum, a MP-correlated free spectrum, and a DP-correlated free spectrum. Only the HD component is represented here.</li></ul>./30f_fs{hd+mp+dp+cp}_ceffyl_hd-only<ul><li>A representation of an analysis that simultaneously modeled a HD-correlated free spectrum, a MP-correlated free spectrum, a DP-correlated free spectrum, and a CURN free spectrum. Only the HD component is represented here.</li></ul>./README<ul><li>this is a readme</li></ul>SOFTWARE------------------This data should ideally be used with the latest versions of:<ul><li>ceffyl (https://github.com/astrolamb/ceffyl)</li><li>PTArcade (https://github.com/andrea-mitridate/PTArcade)</li></ul>PLANNED REVISIONS---------------------------------NoneCHANGE LOG----------------------10/12/2023 - updated KDE representationsA bug was found that produced a poor reflection at the lower prior boundary. Hence, data was being represented well at the lower prior boundary. This has now been corrected.

opencc-by-4.0Apr 2024View details →

Presentation of e-DSS: the e-SAFE Decision Support System

e-DSS (e-SAFE Decision Support System) is a simple and intuitive software tool conceived to support professionals during the preliminary co-design process of an e-SAFE building renovation. The users can assess the energy performance of the building in its current state. Then, they are guided in the creation of a renovation project with the e-SAFE solutions (e-PANEL, e-EXOS, e-CLT, e-THERM): the tool facilitates the choice of the most suitable solution in terms of energy and seismic improvement, while also calculating energy savings, environmental benefit, and payback time. The tool is intended for all types of non-historical buildings and is available at <a href="https://esafe.eng.it/">https://esafe.eng.it/</a>. e-DSS has been created in the framework of Work Package 4 in the H2020 "e-SAFE" project. A final updated and refined version will be released at the end of the project. This video wants to show the tool with its different functions, including input data and results.  For further information please contact: marilena.lazzaro@eng.it.

opencc-by-4.0Aug 2024View details →

ChinaHighTEMmax: Daily Seamless 1 km Maximum Air Temperature Dataset for China (2003–Present)

ChinaHighTEM 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. Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) maximum air temperature (TEMmax) dataset for China from 2003 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.98 and a root-mean-square error (RMSE) of 1.49 ℃ on a daily basis. If you use the ChinaHighTEMmax dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024): <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. Scientific Data, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> 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>

opencc-by-4.0Jun 2024View details →

New Andrews-Curtis trivializations for Miller-Schupp groups presentations. Supplementary materials.

This folder  contains  supplementary materials for the papers "New Andrews-Curtis trivializations  for Miller-Schupp groups presentations" and "Automated Theorem Proving Reveals a Lengthy Andrews-Curtis Trivialization for a Miller-Schupp Trivial Group Presentation" (tentative, as of january 2025)

opencc-by-4.0Aug 2024View details →

ChinaHighPM₂.₅ (2022–Present)

Here is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM2.5 dataset for China from 2022 to the present. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R2) of 0.92, a root-mean-square error (RMSE) of 10.76 µg m-3, and a mean absolute error (MAE) of 6.32 µg m-3 on a daily basis. If you use the ChinaHighPM2.5 dataset in your scientific research, please cite the following references (Wei et al., RSE, 2021; Wei et al., ACP, 2020): <ul> <li> Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-RSE-2021.pdf">Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications</a>. Remote Sensing of Environment, 2021, 252, 112136. https://doi.org/10.1016/j.rse.2020.112136 </li> <li> Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y. <a href="https://weijing-rs.github.io/publications/Wei_et_al-ACP-2020.pdf">Improved 1 km resolution PM2.5 estimates across China using enhanced space-time extremely randomized trees</a>. Atmospheric Chemistry and Physics, 2020, 20(6), 3273–3289. https://doi.org/10.5194/acp-20-3273-2020 </li> </ul> The data for the period 2000–2021 are accessible at: <a href="https://doi.org/10.5281/zenodo.3539349">https://doi.org/10.5281/zenodo.3539349</a> 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>