Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Global Google Trends Internet Search Data 2022 to 2024

Global Google Trends Internet Search Data 2022 to 2024 [RSVs] for fasting, diet, nutrition, liver, GLP-1 RAs

opencc-by-4.0Mar 2022View details →

Data for 'Global trends in grassland carrying capacity and relative stocking density of livestock'

This collection contains all data needed to reproduce maps presented in the following publication (please cite that when using the data): Piipponen, J., Jalava, M., de Leeuw, J., Rizayeva, A., Godde, C., Cramer, G., Herrero, M., & Kummu, M. (2022). Global trends in grassland carrying capacity and relative stocking density of livestock. Global Change Biology, 28(12), 3902–3919. <a href="https://doi.org/10.1111/gcb.16174">https://doi.org/10.1111/gcb.16174</a> A detailed description of files, as well as references and licences related to input/output/supplementary data, can be found in the file List_of_provided_files.txt. All relevant source codes are available in GitHub: <a href="https://github.com/jpiippon/cc_rsd_repo">https://github.com/jpiippon/cc_rsd_repo</a> *Note that data for most of the output maps, such as AGB, CC and RSD can be found in simulation_results_n1000.csv inside Output.7z. See R script figures.Rmd in GitHub for converting this file to raster format and plotting.

opencc-zeroSep 2022View details →

Data from: Global trends of cropland phosphorus use and sustainability challenges

To meet the growing food demand while addressing the multiple challenges of exacerbating phosphorus (P) pollution and depleting P rock reserves, P use efficiency (PUE, the ratio of productive P output to P input in a defined system) in crop production needs to be improved. Although many efforts have been devoted to improving nutrient management practices on farms, few studies have examined the historical trajectories of PUE and their socioeconomic and agronomic drivers on a national scale. Here we present a database of the P budget (the input and output of the crop production system) and PUE by country and by crop type for 1961–2019 and examine the substantial contribution of several drivers for PUE, such as economic development stages and crop portfolios. To address the P management challenges, we found that global PUE in crop production must increase to 68–81%, and recent trends indicate some meaningful progress towards this goal. However, P management challenges and opportunities in croplands vary widely among countries.

opencc-by-4.0Oct 2022View details →

A synopsis of AIRS global-mean clear-sky radiance trends from 2003 to 2020

Here archived the time series of global-mean clear-sky BTs from AIRS observation, GEOS-5, and ERA5 simulations used for trend analysis.

opencc-zeroMay 2024View details →

Data coverage, biases, and trends in a global citizen-science resource for monitoring avian diversity

Aim: Understanding and addressing the global biodiversity crisis requires ecological information compiled continuously from across the globe. Data from citizen science initiatives are useful for quantifying species' ecological niches and geographical distributions but can be difficult to apply towards biodiversity monitoring. The presence of fixed geographical locations reduces the opportunistic nature of citizen science data, allowing for more reliable and nuanced trend estimation. The eBird citizen-science programs contains predefined locations whose bird assemblages are sampled across years ('hotspots'). For hotspots to function as a biodiversity monitoring resource, issues related to data coverage, biases, and trends need to be addressed. Location: Global. Methods: We estimated the survey completeness of species richness at 300,500 eBird hotspots during the years 2002 to 2022. We documented sampling biases at eBird hotspot and non-hotspot locations during 2022 based on protection status, temperature, precipitation, and landcover. Results: A total of 10,410 bird species (ca. 96.9% of total) were recorded at hotspots. The number hotspots and the quantity of data and unique participants and quality of species richness estimates has increased worldwide with the Nearctic containing the strongest and most consistent trends. Compared to non-hotspots, hotspots over sampled areas with higher protection status. Hotspots and non-hotspots over sampled warmer and wetter locations in the Antarctic, Nearctic, and Palearctic, and cooler locations in the Afrotropics, Australasia, and the Neotropics. Hotspots and especially non-hotspots over sampled urban areas. Hotspots and non-hotspots under sampled shrublands in Australasia. Hotspots and especially non-hotspots under sampled forests in the Afrotropics, Indomalaya, Neotropics, and Oceania. Main conclusions: Hotspots have captured a large component of the world's avian diversity but have done so inconsistently across space and time. Data quantity and quality are increasing in many regions, but the presence of sampling biases and spatial uncertainty needs to be addressed when applying the data.

opencc-by-4.0Apr 2018View details →

Current trends in scientific research on global warming: A bibliometric analysis (2005-2014)

This dataset was created in the context of the project: " Current trends in scientific research on global warming: A bibliometric analysis (2005-2014)". --- Global warming is a topic of increasing public importance, but there have not been published scientometric studies on this topic. The objective of this paper is to contribute to a better understanding of the scientific knowledge in global warming and his effect, as well as to investigate its evolution through the published papers included in Web of Science database. Items under study were collected from Web of Science database from Thomson Reuters. A bibliometric and social network analyses was performed to obtain indicators of scientific productivity, impact and collaboration between researchers, institutions and countries. A subject analysis was also carried out taking into account the key words assigned to papers and subject areas of journals. 1,672 articles were analysed since 2005 until 2014. The most productive journals were Journal of Climate (n=95) and Geophysical Resarch Letters (n=78). The most frequent keywords have been Climate Change  (n=722), Model (n=216) and Temperature (n=196). The network of collaboration between countries shows the central position of the United States, together with other leading countries such as United Kingdom, Germany, France and Peoples Republic of China. The research on global warming had grown steadily during the last decade. A vast amount of journals from several subject areas publishes the papers on the topic, including journals of general purpose with high impact factor. Almost all the countries have USA as the main country with which one collaborates. The analysis of key words shows that topics related with climate change, impact, temperature, models and variability are the most important concerns on global warming. --- The dataset consist of the following: 1) The list of papers included in the analyses: Papers.xlsx This file contains 1672 titles, each line representing a paper (including title of the paper, journal ISSN and year of publication). 2) The list of authors: Authors.xlsx This file contains all 4488 authors, each line representing an author (including full name, total number of papers and year of publication). 3) The list of scientific journals: Journals.xlsx This file containts all 687 journals, each line representing a journal (including name of the journal, ISSN, total number of papers and year of publication). 4) The list of countries: Country.xlsx This file contains all 84 countries, each line representing a country (including country name, total number of papers, total number of citations, and number of citations per paper). 5) The list of keywords: Keywords.xlsx This file contains all 6422 keywords, each line representing a keyword (including keywords, number of papers and year of publication)

opencc-by-4.0Sep 2019View details →

Data for "Trend and variability in global upper-ocean stratification since the 1960s"

Abstract of associated paper: Many studies on future climate projection point out that, with progressing of global warming, upper-ocean stratification will strengthen over this century and consequently global-averaged ocean primary production will decrease. Observed long-term changes in the stratification to date, however, still show large uncertainties of the change itself and its driver. Focusing on the vertical difference in the emergence of the global warming signals, we used only observational profiles to describe the spatiotemporal characteristic of long-term trend and variability in the upper-ocean stratification. Rapid strengthening of the stratification (defined as the density difference between the surface and 200 m depth) since the 1960s was detected over most of the global ocean. Although the global average increase over 58 years (1960–2017) corresponds to 3.3–6.1% of the mean stratification, these strengthening trends considerably change depending on the regions. In addition to the well-documented explanation of strengthening stratification, namely that the surface intensification of global warming signal, we found that changes in subsurface temperature and salinity stratification associated with changes in atmospheric/ocean circulations and the global water cycle significantly contribute to the long-term change in the stratification and setting its regional difference. In mid- and high-latitude ocean of the northern hemisphere, the long-term trend in density stratification has noteworthy seasonality, which shows faster increase in boreal summer than that in winter. From the detrended time series, interannual variabilities correlated with a particular climate mode are detected in several ocean regions, suggesting that these variabilities are mainly driven by associated sea surface temperature variation.

opencc-by-4.0Jul 2021View details →

Figure 1 in Is the global decline reflects local declines? A case of the population trend of Far Eastern Curlew Numenius madagascariensis in Banyuasin Peninsula, South Sumatra, Indonesia

Figure 1. Map of Banyuasin Peninsula, South Sumatra, Indonesia.

opencc-by-4.0Oct 2024View details →

Global Trends in research of work-related musculoskeletal disorders among surgeons: Bibliometric analysis and visualization from 1991 to 2024

This file contains all the information relating to the 184 articles included in the bibliometric analysis.

opencc-by-4.0Nov 2023View details →

Trends in global fuel economy of new vehicles: 2005 - 2022

This data contains information on the fuel economy of new light-duty vehicle sales in major automotive markets and underpins the report - Trends in the global vehicle fleet 2023: managing the SUV shift and the EV transition. This report is the 2023 installment of the Global Fuel Economy Initiative (https://www.globalfueleconomy.org/) which, among other things, tracks progress in the efficiency of the global vehicle fleet.  Yearly vehicle sales are defined by segment (small car, medium car, large car, small SUV, large SUV, Light Commercial Vehicle) and powertrain ( internal combustion engine, mild hybrid, hybrid, plug-in hybrid, battery electric, fuel cell hydrogen). For each combination of segment and powertrain the data contains information on sales, average weight (kg), average footprint (m2), and average specific energy consumption (lge/100 km). The data was obtained from a set of sources and processed to gain the best possible estimate of global trends in energy consumption for light duty vehicles. More information on the methodologies underpinning the data analysis can be found in the report. Files included: <ul> <li>supplementary_information_GFEI2023_TDC.xlsx <ul> <li>data: sheet containing the the data -  blank rows indicate missing data.</li> <li>unique values: contains the unique values taken by each column in sheet data</li> </ul> </li> <li> supplementary_information_GFEI2023_vizualisation.xlsx <ul> <li>raw_data: same data as that available in the file above</li> <li>country_map: tha mapping of individual countries to regions used in the report</li> <li>filter_data: combinations of powertrain-year-country that account for less than 0.5% of yearly sales that are not displayed in the Graphs tab</li> <li>pivots_country, pivots_powertrain, pivots_segment: intermediate step </li> <li>Powertrain: registrations, specific fuel consumption, weight and footprint  by powertrain</li> <li>Segment: registrations, specific fuel consumption, weight and footprint  by segment</li> <li>Graphs: Includes plots of the above two tabs, contains a country selector in I2</li> </ul> </li> <li>sdmx.zip <ul> <li>Data structures and data in SDMX-ML format, containing exactly the same information as supplementary_information_GFEI2023_TDC.xlsx, converted using the code at <a href="https://github.com/transport-data/gfei-2023" target="_blank" rel="noopener">https://github.com/transport-data/gfei-2023</a></li> </ul> </li> </ul> Nota Bene: Older versions of Excel might encounter issues with titles in graphs, that is likely caused by the CONCAT function not being availble - substituting the CONCAT function with CONCATENATE should solve the problem.

Figure 5 in Global panorama of studies about freshwater oligochaetes: main trends and gaps

Figure 5. Percentage of works on freshwater oligochaetes published between 1985 and 2015 by continent (A) and by countries (15 leading ones in number of publications) (B).

Figure 3 in Global panorama of studies about freshwater oligochaetes: main trends and gaps

Figure 3. Percentage of works on freshwater oligochaetes published between 1985 and 2015 by area of knowledge (A) and 10-year totals (B). The "Others" category contains works related to oligochaetes as food for fish and used in reduction of activated sludge.

Figure 2 in Global panorama of studies about freshwater oligochaetes: main trends and gaps

Figure 2. Number of works on freshwater oligochaetes published between 1985 and 2015 (A) and 10-year totals (B).

Figure 1 in Global panorama of studies about freshwater oligochaetes: main trends and gaps

Figure 1. Total number of exclusive and shared publications on oligochaetes in freshwater habitats found in each database entre 1985 e 2015.

opencc-zeroJan 2023View details →

The undetectability of global biodiversity trends using local species richness

Although species are being lost at alarming rates, previous research has provided conflicting results on the extent and even direction of global biodiversity change at the local scale. Here, we assessed the ability to detect global biodiversity trends using local species richness and how it is affected by the number of monitoring sites, sampling interval (i.e., time between original survey and re-survey of the site), measurement error (error of the measurement of the local species richness), spatial grain of monitoring (a proxy for the taxa mobility), and spatial sampling biases (i.e., site-selection biases). We use PREDICTS model-based estimates as a proxy for the real-world distribution of biodiversity and randomly selected monitoring sites to calculate local species richness trends. We found that while a monitoring network with hundreds of sites could detect global change in species richness within a 30-year period, the number of sites for detecting trends doubled for a decade, increased 10-fold within three years, and yearly trends were undetectable. Measurement errors had a non-linear effect on statistical power, with a 1% error reducing statistical power by a slight margin and a 5% error drastically reducing the power to reliably detect any trend. The ability to detect global change in local species richness was also related to spatial grain, making it harder to detect trends for sites sampled at smaller plot sizes. Spatial sampling biases not only reduced the ability to detect negative global biodiversity trends but sometimes yielded positive trends. We conclude that detecting accurate global biodiversity trends using local richness may simply be unfeasible with current approaches. We suggest that monitoring a representative network of sites implemented at the national level, combined with models accounting for errors and biases, can help improve our understanding of global biodiversity change.