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Testing parts of the curation workflow.

TEST The Sustainable Corn CAP (Cropping Systems Coordinated Agricultural Project: Climate Change, Mitigation, and Adaptation in Corn-based Cropping Systems) was a multi-state transdisciplinary project supported by the USDA National Institute of Food and Agriculture (Award No. 2011-68002-30190). Research experiments were located through the U.S. Corn Belt and examined farm-level adaptation practices for corn-based cropping systems to current and predicted impacts of climate change. Research data were collected from 2011 to 2015 at research sites in 8 states: Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, and Wisconsin. The research coverage area spanned 95.3°W to 81.9°W and 38.5°N to 44.7°N. Research sites encompassed a varying set of management practices including crop rotation, cover crop, tillage, drainage, and nitrogen management, with several sites having landscape position incorporated as an additional treatment. These treatments were typically arranged in a randomized complete block design as a complete factorial or main-split plot with 3 to 4 replications per site. It should be noted that none of the sites were identical in terms of treatment structure or data collected as sites were a combination of previously and newly established experiments that aligned with project research goals. The dataset contains agronomic, soil, water, greenhouse gas, crop disease, and pest data collected from 30 sites. Standardized protocols were developed and followed by the project team for estimating C, N, and water footprints of corn production in the region and performing baseline monitoring. Variables measured during the five-year period include: grain and biomass yield, C and N content in crop grain and vegetation, soil water moisture and temperature, C and N concentration in soil, greenhouse gas fluxes, drainage water quality and quantity, groundwater table and others. Hourly or sub-hourly weather data are also provided for each location. In addition, the dataset includes site description (e.g. site location, plot area, soil type), field management information (e.g. planting, harvesting, tillage and fertilizer application dates, seeding rate, fertilizer and pesticide type and application rate), and experimental design (e.g. plot identifiers, experimental treatments, variables measured).

Data scraped from an online repository 1,925 articles collected by the biosurveillance program, PestLens (2019) from 2008-2018, and then edited. The 1,612 edited records shown had usable information for report date, pest species taxonomy and type, and country of report. Also included are the genera and country matching lists. PestLens (2019) Preclearance and Offshore Programs, Plant Protection and Quarantine, U.S. Department of Agriculture, and Center for Integrated Pest Management, North Carolina State University. https://pestlens.info/. Accessed April 11, 2019

Trapping survey detections data for five insect species and one mollusk (traps and other finds), with latitudes and longitudes and other spatial identifications removed per agreement with agencies. Species and survey data as follows: European grapevine moth (EGVM), Lobesia botrana, California, 2011-2013, Source: CDFA (California Department of Food and Agriculture) Giant African landsnail (GALS), Achatina fulica, Florida, 2011-2020, Source: FDACS (Florida Department of Agriculture and Consumer Services) Japanese beetle (JB), Popillia japonica, California, 2010-2019, Source: CDFA Medfly, Ceratitis capitata, California, 2015-2019, Source: CDFA Medfly, Florida, 1956-2011, Source: FDACS Mexfly, Anastrepha ludens, Texas, 2016-2019, Source: PPQ (Plant Protection and Quarantine, USDA) Oriental fruit fly (OFF), Bactrocera dorsalis, California, 2015-2019, Source: CDFA OFF, Florida, 1995-2018, Source: FDACS "Raw detections data v1.xlsx" is the collection of all detections. "Collected distances data v1.xlsx" is the data after clustering for proximity in time and space has been done. This data includes clustering information and distances calculated for each detection from the cluster centroid.

Citation metrics are widely used and misused. We have created a publicly available database of over 100,000 top-scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator. Separate data are shown for career-long and single year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 176 sub-fields. Field- and subfield-specific percentiles are also provided for all scientists who have published at least 5 papers. Career-long data are updated to end-of-2020. The selection is based on the top 100,000 by c-score (with and without self-citations) or a percentile rank of 2% or above. The dataset and code provides an update to previously released version 1 data under https://doi.org/10.17632/btchxktzyw.1; The version 2 dataset is based on the May 06, 2020 snapshot from Scopus and is updated to citation year 2019 available at https://doi.org/10.17632/btchxktzyw.2 This version (3) is based on the Aug 01, 2021 snapshot from Scopus and is updated to citation year 2020.

The project goal is to study the Russian digital economy as a new social field, which is a place of interaction of social forces, carried by individual agents, groups, organizations with their aggregate capitals, habitus, and practices. The author has developed an approach to the definition of habitus, combining the actor's character and capital. To classify habitus, a series of studies was carried out and general characteristics of actions were determined depending on disposition and capitals combination. The research was carried out in 4 Russian regions - Kaliningrad, Yaroslavl, Tambov and Kursk region. 48 groups of common habitus characteristics were identified. Each group was given a name, which is projected in the minds of the Russian population into a certain holistic image. More detailed characteristics have been identified for the digital economy. The following data is presented - Description of the population habitus, Description of the digital habitus, Grouping of the Russian population habitus, Grouping of habitus by research area.

Colleccion de prueba para demostracion en taller

The United Nations Sustainable Development Goals (SDGs) challenge the global community to build a world where no one is left behind. Since 2018, Elsevier have generated SDG search queries to help researchers and institutions track and demonstrate progress towards the targets of the United Nations Sustainable Development Goals (SDGs). At the end of 2018, Elsevier worked on 2 versions of the SDG queries. One version was created by the Elsevier Analytical Services group and another by the Science-Metrix group, who had recently become part of Elsevier. At that time Science-Metrix was creating queries for 5 of the 16 SDGs, as part of pro-bono work for UNESCO. In 2020 inspired by the earlier queries, Elsevier, through its Science-Metrix group, used a new approach to mapping publications to the SDGs. Taking customer feedback into account, they significantly increased the number of search terms used to define each SDG. Those queries were then complemented by a machine learning model, which helped increase the recall by approximately 10%. As a result, this year’s “Elsevier 2021 SDG mapping” captures on average twice as many articles as the 2020 version, while keeping precision above 80%. The mapping also has a better overlap with SDG queries from other independent projects. Times Higher Education (THE) are using the “Elsevier 2021 SDG mapping” as part of their 2021 Impact Rankings. The documentation below describes the methods used and shares the queries. For each SDG, you can download the query as a text file, along with an html file that describes the methodology used to create the search query, plus additional information such as the most influential keyphrases and journals. It also breaks down the query into digestable chunks. A separate folder contains the methodology for the machine learning component, along with a sample of the top 100 keyphrases per SDG and a stratified sample of 8,000 EIDs that the model identified arcoss the SDGs.

Compilation of Raw diffraction datasets for proteases