Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

Eddy Kinetic Energy and SST gradients global datasets and trends. Additionally, this dataset includes ocean basins and ocean processes masks.

This dataset includes the post-processed data used for the paper titled "Mesoscale kinetic energy response to changing oceans". The original data was obtained from AVISO+ SSH altimetry and NOAA optimal interpolated sea surface temperature (OISST): AVISO+ SSH: https://www.aviso.altimetry.fr/en/data/products/sea-surface-height-products/global/gridded-sea-level-heights-and-derived-variables.html NOAA-OISST: https://www.ncdc.noaa.gov/oisst From satellite observations of sea surface height (SSH) and sea surface temperature (SST) over the satellite record (1993 - 2019), EKE and SST gradients are derived.  Then the fields are then temporally smoothed using a running average of 12 months.  Trends and the significance of each field are finally computed with linear regression and a modified Mann–Kendall test (https://github.com/josuemtzmo/xarrayMannKendall). Geographical regions consist of the following ocean basins: the Southern Ocean, the Indian Ocean, the Pacific Ocean, and the Atlantic ocean. These ocean basins were expert-defined to capture ocean processes at all scales (ocean_basins_and_dynamical_masks.nc). Dynamical regions (Fig. 5d): the Antarctic Circumpolar Current (ACC), the boundary currents and their extensions, the tropics, the subtropical ocean gyres, and the remaining regions (ocean_basins_and_dynamical_masks.nc). Further information and scripts to reproduce the result of the manuscript can be found at: https://github.com/josuemtzmo/EKE_SST_trends

opencc-by-4.0Apr 2024View details →

A new merged dataset of global ocean chlorophyll-a concentration for better trend detection

Chlorophyll-a concentration (Chla) is recognized as an essential climate variable and is one of the primary parameters of ocean-color satellite products. Ocean-color missions have accumulated continuous Chla data for over two decades since the launch of SeaWiFS in 1997. However, the on-orbit life of a single mission is about five to ten years. To build a dataset with a time span long enough to serve as a climate data record (CDR), it is necessary to merge the Chla data from multiple sensors. The European Space Agency has developed two sets of merged Chla products, namely GlobColour and OC-CCI, which have been widely used. Nonetheless, issues remain in the long-term trend analysis of these two datasets because the intermission differences in Chla have not been completely corrected. To obtain more accurate Chla trends in the global and various oceans, we produced a new dataset by merging Chla records from the Sea-viewing Wide Field-of-view Sensor, Medium-spectral Resolution Imaging Spectrometer, Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite, and Ocean and Land Colour Instrument with intermission differences corrected in this work. The fitness of the dataset as a CDR was validated by using in situ Chla and comparing the trend estimates to the multi-annual variability of different satellite Chla records.

opencc-by-4.0Apr 2024View details →

Global Trends in Clinical Trials Involving Engineered Biomaterials

The study aimed to conduct a comprehensive analysis of all clinical trials involving engineered biomaterials by utilizing the ClinicalTrials.gov database. The search was executed in August 2023, and the analysis encompassed various attributes of the included studies, including the study title, URL, target disease, condition, intervention, biomaterial category, biomaterial type, specific biomaterial used, biomaterial properties, incorporation of cells, participant age and gender, clinical study phase, enrollment figures, study location, and the study's start and end dates. The corresponding data was systematically collected from the included studies and organized into dataset (Dataset S1 and Dataset S2).

opencc-by-4.0Aug 2024View details →

Global trends and collaborations in electrochemical methods: a dataset on etching and deposition research

This dataset supports the study "Electrochemical Etching vs. Electrochemical Deposition: A Comparative Bibliometric Analysis," which examines scientific publications on electrochemical etching and electrochemical deposition from 1970 to 2023. The dataset is derived from the Science Citation Index Expanded (SCIE) database and includes bibliometric information on publication trends, leading contributors, research areas, and keyword co-occurrences in both fields.

opencc-by-4.0Aug 2021View details →

First estimation of global trends in nocturnal power emissions reveals acceleration of light pollution

The power emitted by different countries at night is based on DMSP and VIIRS data. Inclued also, some extra data from Spain, Portugal, Italy, UK and Greece.

opencc-by-4.0Jan 2023View details →

Model agreement and trend analysis data associated to the publication: "Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050"

This dataset is associated with the following publication: Haslebacher, C., Demory, M.-E., Demory, B.-O., Sarazin, M., and Vidale, P. L., “Impact of climate change on site characteristics of eight major astronomical observatories using high-resolution global climate projections until 2050. Projected increase in temperature and humidity leads to poorer astronomical observing conditions”, Astronomy and Astrophysics, vol. 665, 2022. doi:10.1051/0004-6361/202142493. In the folder 'model_agreement', there are pickle files from which a python dictionary can be extracted with: <pre><code>with open('mypklfile.pkl', 'rb') as myfile: dload = pickle.load(myfile)</code></pre> Pickle files ending with '_d_obs_ERA5.pkl' contain in situ data and ERA5 data. Pickle files ending with 'd_model.pkl' contain PRIMAVERA model data. A few explanations: - 'ds_sel': contains monthly timeseries of selected intersecting data - 'ds_taylor': contains data used for the Taylor diagram (Figs. 4-10) - 'ds_mean_month': contains seasonal cycle for plotting (Figs. 4-10) - 'ds_mean_year': contains yearly timeseries for plotting (Figs. 4-10)  The subfolder 'median_nc_u_v_t' contains NETCDF files with the median and interquartile range of the wind speed in u and v direction, the temperature and geopotential height. This was used for Figs. G1-G8 and to calculate the refractive index structure constant Cn2. The subfolder 'skill_score_classification' contains csv files with the sorted skill score classifications. The column headers are: model_name, skill score, correlation coefficient, standard deviation, centred root mean square error. The folder 'trend_analysis' contains for each variable csv files of ERA5 and PRIMAVERA monthly time series used for trend analysis, pdf files of analysis summaries, csv files of Bayesian analysis results and png files of longitude-latitude maps of trends (analysed with linear regression). Additionally, there is a csv file of averaged in situ pressures. Code that generated and used this data is available on github: <a href="https://github.com/CarolineHaslebacher/Astroclimate-future-project">https://github.com/CarolineHaslebacher/Astroclimate-future-project</a>

opencc-by-4.0Jun 2021View details →

Availability and trends in sports foods available for sale at New Zealand supermarketsy of sports foods globally and in New Zealand supermarkets

Sports foods are specially formulated to help people achieve specific nutritional or sporting performance goals. Anecdotal evidence suggests increasing availability and marketing of such products to consumers, however, very few studies have looked at in-store product availability.  Data for 2013 to 2018 were collected from the Nutritrack database, an online searchable database of all unique packaged foods and beverages sold at four main supermarket chains in New Zealand. Availability of sports foods and on-pack marketing techniques were assessed in 2018 using descriptive analysis, and changes in proportions over time were assessed using Chi-Square analyses. In 2018, the proportion of packaged foods available in major New Zealand supermarkets which were classified as sports foods was 2.1% (n=325), which had increased from 1.8% (n=247) in 2013. Sports foods also appeared in more food groups and subcategories in 2018 compared with 2013 (11 vs. 6 food groups, and 25 vs. 19 subcategories, respectively). The use of on-pack marketing techniques also increased over time, with Nutrient Claims present on 87% of sports foods in 2013 and 98% in 2018. The implications of the increase in product availability and on-pack marketing of sports foods in New Zealand supermarkets warrants consideration from public health, sporting, and consumer sectors. Sports foods are specially formulated to help people achieve specific nutritional or sporting performance goals. Anecdotal evidence suggests increasing availability and marketing of such products to consumers, however, very few studies have looked at in-store product availability.  Data for 2013 to 2018 were collected from the Nutritrack database, an online searchable database of all unique packaged foods and beverages sold at four main supermarket chains in New Zealand. Availability of sports foods and on-pack marketing techniques were assessed in 2018 using descriptive analysis, and changes in proportions over time were assessed using Chi-Square analyses. In 2018, the proportion of packaged foods available in major New Zealand supermarkets which were classified as sports foods was 2.1% (n=325), which had increased from 1.8% (n=247) in 2013. Sports foods also appeared in more food groups and subcategories in 2018 compared with 2013 (11 vs. 6 food groups, and 25 vs. 19 subcategories, respectively). Use of on-pack marketing techniques also increased over time, with Nutrient Claims present on 87% of sports foods in 2013 and 98% in 2018. The implications of the increase in product availability and on-pack marketing of sports foods in New Zealand supermarkets warrants consideration from public health, sporting, and consumer sectors.

opencc-by-4.0Oct 2022View details →

Spatiotemporal variations of air pollution during the COVID-19 pandemic across Tehran, Iran: Commonalities with and differ-ences from global trends

Figure S1: Green space and green area per capita across Tehran; Figure S2: Temporal distribution of CO content at each station, gray rectangular shows strict social distancing time. Figure S3: Temporal distribution of NO2 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S4: Temporal distribution of PM10 content in all investigated stations gray rectangular shows strict social distancing time; Figure S5: Temporal distribution of O3 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S6: Temporal distribution of SO2 content in all investigated stations, gray rectangular shows strict social distancing time; Figure S7: Temporal distribution of AQI indices in all investigated stations, gray rectangular shows strict social distancing time.

opencc-by-4.0Jul 2021View details →

Figure 2 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 2. Population trend of number of the Far Eastern Curlew in Banyuasin Peninsula from 1984 to 2020.

opencc-by-4.0Jul 2021View details →

Figure 3 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 3. Mix flocks of Far Eastern Curlew and Eurasian Curlew Numenius arquata in flight on 8 November 2020 in Banyuasin Peninsula, South Sumatra province, Indonesia (Photo: Cipto Dwi Handono).

opencc-by-4.0Jul 2021View details →

Figure 4 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 4. Far Eastern Curlew standing at the mudflat on 8 November 2020 in the coastal zone of Banyuasin Peninsula, South Sumatra province, Indonesia (Photo: Cipto Dwi Handono).

opencc-by-4.0Sep 2020View details →

Fig. 3. Global temperature trends 1880–2017 in Book Review The Wildlife Techniques Manual, Eighth Edition

Fig. 3. Global temperature trends 1880–2017. Global mean estimates based on land and ocean data. https://data.giss.nasa.gov/ gistemp/graphs/. Graphic in the Public Domain.

opencc-by-4.0Sep 2021View details →

Global Flash Drought Data for the article "Global Distribution, Trends, and Drivers of Flash Drought Occurrence"

Data is provided (in netcdf format) to reproduce Figures 1-4 in the article entitled "Global Distribution, Trends, and Drivers of Flash Drought Occurrence."

Fig. 5 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 5. Data sources according to epidemiological level and scale. Representation of the data sources (left column) used for each epidemiological level (central column), and the scale of the corresponding data sources (right). Colors of the left column correspond to general data-sources categories; for example, green corresponds to biological/biodiversity data sources (e.g., Biodiversity repositories and biological general source). Health-related sources are represented in purple (Health gov: governmental, init-program: initiative or programs). Using this broad categorization, most of the sources contribute with data related to the three epidemiological levels, although with an unpaired flow. For example, scientific literature has a lower contribution for hosts/ reservoirs, and biodiversity-biological sources have a minor contribution for pathogens. Most data sources have a global scale meanwhile governmental sources have a relevant contribution to pathogen data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 3. Diseases explored in the studies according to the use of GBIF and the taxa class of the causal pathogen. In the right panel: positive studies (i.e., those studies that used GBIF-mediated data for at least one of the variables explored), negative studies in the left. Bars represent the number of studies exploring each disease, and filling colors represent the corresponding taxa class of the disease agent or causal pathogen (Purple scale, with lighter coloration for fungal diseases, followed by parasites, bacteria, and viruses with the darker purple). Abbreviations: the abbreviation Oth (Fungal Oth, Parasite Oth, Bacteria Oth, Virus Oth) represents a category with multiple species, merged to simplify the figure due to the low number of studies of each disease. Ricket-related: diseases related to Rickettsia species; Paras: parasites; Schistos: Schistosomiases; Leishm: Leishmaniases (both cutaneous and visceral); Dis: disease; Bact: bacteria; Fev: fever; V: virus; CoronaV: Coronavirus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 2 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 2. Research areas identified in the studies. Research areas subcategories are represented in the left axis, and general research area groups in the right axis. Orange circles represent the number of positive studies, the blue circles the negatives, and the black lines between them represent the differences in the number of studies, in which larger lines represent larger differences between positive and negatives. Orange icons correspond to research areas with larger number of positive studies, i.e., positive studies were more related to Biology (Bio), Ecology (Ecol) and Other (Hum Soc: Human society; Phy Env Geo: Physical environmental geology; Earth Atm: Earth and atmospheric sciences). Negative studies were more frequent in research areas with blue icons, including Medical (Med) and Veterinary sciences (Vet: Veterinarian and agriculture). In the green icon (Eng Inf Mat: Engineering, informatics, and mathematics) there was no major differences between groups. Subcategories: Bio Zoo: Biology and zoology; Bio Evo Gen: Biology, evolution, and genetics; Bio Bioch: Biology and biochemistry; Env Mang: Environmental management and sciences; Eco App: Ecological applications; Math Stat: Mathematical statistics; Inf Comp: informatics and computing; Eng Geom: Engineer and geometrics; Microb: Microbiology; Pub Heal: Public health; Med Micro: Medical microbiology; Med Clin Heal: Medical clinical and health. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 1 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 1. General framework of analyses at study- and variable-levels. In the upper section (study-level, in grey), studies are divided in those that used GBIF-mediated data (positives, in orange) and those that did not (negatives, in blue). Positive studies were group according if GBIF was used as the only data source for all variables (2 studies), or if the variables were based on GBIF together with other data sources (105 studies). In the variable-level section (bottom, white background) the total 358 variables extracted from the positive and negative studies were categorized according to the specific use of GBIF, resulting in five types of variables, four of them extracted from the positive studies. Note that in those studies based on GBIF, the different variables could be based on GBIF alone (33 variables), GBIF together with other sources (85), or specific variables may not be based on GBIF-mediated data at all (81 variables). Finally, each variable was related to different epidemiological roles, resulting in a larger number hosts/reservoirs variables, mostly based on GBIF-mediated data, and a higher presence of pathogen species-variables not using GBIF. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities

Fig. 4. Variables according to taxon class (Y- axis) epidemiological level (bar colour) and the use of GBIF- mediated data. Bars represent the number of variables by each taxon class (Y-axis), separated in two panels according to the use of GBIF-mediated data. In the right panel, variables in which GBIF-mediated data was used (Used_GBIF), in the left panel variables in which data was not obtained from GBIF (NonGBIF). Next to the bars, the number of variables by each epidemi- ological level, and percentage in rela- tion to the total number of variables of each group (Used_GBIF: 120 and Non- GBIF: 238). Taxon classes are grouped by taxonomic associations (e.g., birds, primates, ticks, mosquitoes); however, some were merged to simplify the figure. For example, mamm/oth/var includes multiple mammal species which were sparsely mentioned; simi- larly, hosts/res var, vector other and path other grouped several species participating as hosts/reservoirs, vec- tors, and pathogens, respectively. Bar colors represent epidemiological levels (pathogens, vectors, hosts/reservoirs), and Other (in sienna) includes species participating as hosts' regulator, predators, among others. GBIF-mediated data was only used in three pathogen variables (purple), representing only 2.5% of the variables in which GBIF-mediated data was used, resulting in a remarkable difference with other sources (NonGBIF), in which pathogens represented a 59.7%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)