Sampling
Samples were collected on board the RSV Aurora Australis between 22 January and 17 February 2016. The cruise surveyed the region south of the Kerguelen Plateau including the Princess Elizabeth Trough and BANZARE Bank in a series of eight transects covering 8165 km. Plankton communities were collected at 45 conductivity temperature depth (CTD) stations and seven additional underway stations, with biological replicates collected at two stations (52 independent sites). Surface water was sampled from 4 plus or minus 2 m depth using the uncontaminated seawater line. Deep Chlorophyll Maximum (DCM, 10-74 m) water samples were obtained using 10 L Niskin bottles mounted on a Seabird 911+ CTD. Plankton communities were size-fractionated by sequentially filtering 10 L seawater through 25 mm 20 micron (nylon) and 5 micron filters (PVDF), and 0.45 micron Sterivex filters (PVDF). Filters were stored frozen at -80 °C.

DNA extraction and high-throughput sequencing
DNA was extracted from half of each filter using the MoBio PowerSoil DNA Isolation kit at the Australian Genome Research Facility (AGRF, Adelaide, Australia; http://www.agrf.org.au). The V4 region of the 18S rDNA (approximately 380 bp excluding primers) was PCR-amplified using universal eukaryotic primers from all extracts and sequenced on an Illumina MiSeq v2 (2 x 250 bp paired-end) following the Ocean Sampling Day protocol (Piredda et al. 2017). Amplicon library preparation and high-throughput sequencing were carried out at the Ramaciotti Centre for Genomics (Sydney, Australia).

Sequence analysis, OTU picking and assignment followed the Biomes of Australian Soil Environments (BASE) workflow (Bissett et al. 2016). Taxonomy was assigned to OTUs based on the PR2 database using the ‘classify.seqs’ command in mothur version 1.31.2 with default settings and a bootstrap cut-off of 60%. OTUs representing any terrestrial contaminants (e.g. human) and samples with low sequencing coverage (less than 7000 reads) were removed from the dataset.

The date of sea ice melt for each station was estimated from daily SSM/I-derived sea-ice spatial concentration from the National Snow and Ice Data Centre (NSIDC) at 25 x 25 km resolution. Days since melt was considered to be the number of days between the date on which sea ice concentration first fell below 15% and the date of sampling.

Other environmental variables included are in situ chlorophyll a, as an indicator of biological production, and near-surface salinity (mean over the upper 10 m) as an indicator for recent sea ice melt. Both environmental measurements were taken from the associated CTD seawater samples. The surface chlorophyll a in seawater (1-2 L) collected in Niskin bottles was analysed by high performance liquid chromatography (HPLC, provided by Karen Westwood and Imojen Pearce, Australian Antarctic Division, doi:10.4225/15/5a94c701b98a8).

Sampling times are given in UTC.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Čḯxʷicən Charcoal" data publication.

This zip file contains 21 raster layers representing data from a variety of landscape metrics used to analyze the landscape context of Bighorn Canyon National Recreation Area (BICA). Their names, descriptions and categorization are as follows: Housing This raster dataset contains sixteen layers named in the format "bhc1950us," with the name of each layer containing a year representing the decades from 1950 through 2100 (ex. bh1960us, bhc1970us, bhc1980us, etc.). The layers depict housing density classes for the area around the 30 km buffer around and including BICA’s managed lands for each decade. These housing density estimates come from a Spatially Explicit Regional Growth Model (SERGoM, Theobald 2005) based on U.S. Census data from 2010 and depict the location and density of private land housing unit classes around BICA. SERGoM methods combined housing data with information on land ownership and density of major roads (interstates, state highways, and county roads) to provide a more accurate allocation of the location of housing units over the landscape. Details on how SERGoM was used for NPS data can be found in the NPScape Standard Operating Procedure (SOP): Housing Measure at https://irma.nps.gov/DataStore/Reference/Profile/2221576 The SERGoM used historical and current housing density patterns as data inputs to develop a simulation model to forecast future housing density patterns based on county-level population projections. Further details about the methodology of SERGoM can be found at https://www.jstor.org/stable/26267722?seq=2 SERGoM_bhc_metrics: Value CLASSNAME 0 Private undeveloped 1 2,470 units / square km 12 Commercial/industrial Land Cover This raster dataset depicts land cover and contains four layers from the National Land Cover Database (NLCD). The names and descriptions of each layer are as follows: NLCD2001. The National Land Cover Database 2001 land cover layer for mapping was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. This is a single layer for 2001 landcover data at all levels. Level 2 data for 2001 can be derived from this layer by collapsing level 1 features into level 2 categories. This level 1 layer contains seventeen classes: Value Land Cover 0 Unknown 11 Open Water 12 Perennial Snow/Ice 21 Developed, Open Space 22 Developed, Low Intensity 23 Developed, Medium Intensity 24 Developed, High Intensity 31 Barren Land 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest 52 Shrub/Scrub 71 Herbaceous 81 Hay/Pasture 82 Cultivated Crops 90 Woody Wetlands 95 Emergent Herbaceous Wetlands NLCD2001 land cover class descriptions: Open Water - All areas of open water, generally with less than 25% cover or vegetation or soil. Perennial Ice/Snow - All areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover. Developed, Open Space - Includes areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20 percent of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes. Developed, Low Intensity - Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20-49 percent of total cover. These areas most commonly include single-family housing units. Developed, Medium Intensity - Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50-79 percent of the total cover. These areas most commonly include single-family housing units. Developed, High Intensity - Includes highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80 to100 percent of the total cover. Barren Land - Rock/Sand/Clay; Barren areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover. Deciduous Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species shed foliage simultaneously in response to seasonal change. Evergreen Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species maintain their leaves all year. Canopy is never without green foliage. Mixed Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75 percent of total tree cover. Shrub/Scrub - Areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions. Herbaceous - Areas dominated by graminoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling but can be utilized for grazing. Hay/Pasture - Areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20 percent of total vegetation. Cultivated Crops - Areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20 percent of total vegetation. This class also includes all land being actively tilled. Woody Wetlands - Areas where forest or shrub land vegetation accounts for greater than 20 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for greater than 80 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. Landcover_NaturalConverted_NLCD2011. This layer depicts natural vs. converted land cover circa 2011 and was extracted from the map package NLCD2011_LNC.mpk. This layer contains two classes: Value Class Name 1 Converted 2 Natural Natural vs. Converted class descriptions: Converted - Developed areas, cultivated crops, and hay/pasture lands. Natural - All other major cover types. Landcover_Level1_NLCD2011. This layer was extracted from the map package NLCD2011_Level1.mpk and contains nine classes: Value Class Name 1 Open Water 2 Developed 3 Barren/Quarries/Transitional 4 Forest 5 Scrubs/Shrub 6 Perennial Ice/Snow 7 Grassland/Herbaceous 8 Agriculture 9 Wetlands Landcover_Level2_NLCD2011. This layer was extracted from the map package NLCD2011_Level2.mpk and contains fifteen classes: Value Class Name 11 Open Water 12 Perennial Ice/Snow 21 Developed, Open Space 22 Developed, Low Intensity 23 Developed, Medium Intensity 24 Developed, High Intensity 31 Barren Land 41 Deciduous Forest 42 Evergreen Forest 43 Mixed Forest 52 Shrub/Scrub 71 Herbaceous 81 Hay/Pasture 82 Cultivated Crops 90 Woody Wetlands 95 Emergent Herbaceous Wetlands Road Density - Road_Density. This raster layer depicts road density (km/km2) calculated for all roads in and around the study area (30 km buffer around BICA) as of 2005. This layer was extracted from the map package AllRoads_rdd.mpk. The map packages mentioned above can be found in the DataStore reference: Bighorn Canyon National Recreation Area Landscape Context, Map Packages. National Park Service. https://irma.nps.gov/DataStore/Reference/Profile/2306146>https://irma.nps.gov/DataStore/Reference/Profile/2306146

The Swedish Contextual Database provides a large number of longitudinal and regional macro-level indicators primarily assembled to facilitate research on the effects of contextual factors on family and fertility behavior. It can be linked to the individual-level data of the Swedish GGS as well as to data of other surveys. It can also be used for other types of research and for teaching. The comparative data will also be integrated into the international Contextual Database of the GGP. The contextual data are available open-access through the GGP webpage: www.ggp-i.org and through the webpage of Stockholm University Demography Unit www.suda.su.se

Purpose:

The Swedish contextual database (CDB) was established to accompany the Swedish Generations and Gender Survey (GGS) and to complement the contextual database of the international Generations and Gender Programme (GGP).

The Swedish Contextual Data Collection is available in xls format. In addition to that, the internationally comparative data will be integrated into the Contextual Database (CDB) of the GGP in 2018. These data can be exported in other formats, as well (e.g. CSV, XML). The indicators can also be accessed in a single file in STATA or SPSS format. The data can be matched with the Swedish GGS. International regional coding schemes are also supported, such as NUTS, OECD.

This dataset contains contextual information for the Wildland Fire Science Initiative (WFSI) research burn campaign: Fort Stewart 2025, conducted April 14, 2025 to April 18, 2025 in longleaf pine-wiregrass ecosystems on the Army base, Fort Stewart. WFSI research burn campaigns are collaborative opportunities for researchers in the WFSI community to jointly conduct prescribed fire research and combine expertise. The campaigns are supported by the WFSI Integrated Research Management Team (IRMT) in conjunction with installation personnel. The Fort Stewart 2025 campaign was led by NASA's FireSense project, an effort aimed at "delivering NASA's unique earth science and technological capabilities to operational agencies, striving to address challenges in US wildland fire management". The contextual information includes: descriptions of Fort Stewart climatology, plant communities, fuels, and operational information from Fort Stewart prescribed fire organization. Burn unit perimeters and maps as well as Remote Automated Weather Station (RAWS) data from Wright Army Airfield covering all burn days is also included. The contextual information is to be used as an aid to researchers when publishing on WFSI related research campaigns. To find datasets collected as part of the research campaign, search keyword: "Fort Stewart 2025".

The IPUMS Contextual Determinants of Health (CDOH) data series includes measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The CDOH measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2000 to 2020. The Race and Ethnicity measures in this release are indicators of residential segregation, which measures the physical separation of population groups into different areas (i.e., neighborhoods) in a geographic unit (i.e., a county or city). The index of dissimilarity is a measure of evenness and measures the proportion of a group's population that must move so that each sub-county geographic unit in a county has the same proportion of that group as the county. Census tracts are used as the sub-county geographic unit because census tracts nest within counties.

The construction of the Swedish CDB and the data collection followed a template developed within the GGP. The template provided detailed guidelines for the collection, preparation, and documentation of the indicators. The database covers 16 main areas: Demography, Economy and Social Aspects, Labour and Employment, Parental Leave, Pension, Childcare, Military, Unemployment, Tax Benefits, Housing, Legal Aspects, Education, Health, Elderly Care, Politics, Culture. Each of these main domains contains more detailed indicators at the national or subregional (Riskområde NUTS2) level. In total, there are 243 indicators. Many of these indicators were calculated using Swedish Register Data. These indicators were not available in publicly accessible statistics and the Swedish CDB is thus currently the only database to provide them. The Swedish CDB offers a rich and unique set of time-series indicators at the national and subregional level.

Investigating geographic variations in mammography screening participation and breast cancer incidence help improve prevention strategies to reduce the burden of breast cancer. This study examined the suitability of health insurance claims data for assessing and explaining geographic variations in mammography screening participation and breast cancer incidence at the district level. Based on screening unit data (1,181,212 mammography screening events), cancer registry data (13,241 incident breast cancer cases) and claims data (147,325 mammography screening events; 1,778 incident breast cancer cases), screening unit and claims-based standardized participation ratios (SPR) of mammography screening as well as cancer registry and claims-based standardized incidence ratios (SIR) of breast cancer between 2011 and 2014 were estimated for the 46 districts of the German federal state of Lower Saxony. Bland-Altman analyses were performed to benchmark claims-based SPR and SIR against screening unit and cancer registry data. Determinants of district-level variations were investigated at the individual and contextual level using claims-based multilevel logistic regression analysis. In claims and benchmark data, SPR showed considerable variations and SIR hardly any. Claims-based estimates were between 0.13 below and 0.14 above (SPR), and between 0.36 below and 0.36 above (SIR) the benchmark. Given the limited suitability of health insurance claims data for assessing geographic variations in breast cancer incidence, only mammography screening participation was investigated in the multilevel analysis. At the individual level, 10 of 31 Elixhauser comorbidities were negatively and 11 positively associated with mammography screening participation. Age and comorbidities did not contribute to the explanation of geographic variations. At the contextual level, unemployment rate was negatively and the proportion of employees with an academic degree positively associated with mammography screening participation. Unemployment, income, education, foreign population and type of district explained 58.5% of geographic variations. Future studies should combine health insurance claims data with individual data on socioeconomic characteristics, lifestyle factors, psychological factors, quality of life and health literacy as well as contextual data on socioeconomic characteristics and accessibility of mammography screening. This would allow a comprehensive investigation of geographic variations in mammography screening participation and help to further improve prevention strategies for reducing the burden of breast cancer.

This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.

This dataset contains contextual information for the Wildland Fire Science Initiative (WFSI) research burn campaign: Eglin Air Force Base 20223, conducted on March 11-19, 2023, in longleaf pine-sandhill ecosystems on Eglin Air Force Base. WFSI research burn campaigns are collaborative opportunities for researchers in the WFSI community to jointly conduct prescribed fire research and combine expertise. The campaigns are supported by the WFSI Integrated Research Management Team (IRMT) in conjunction with installation personnel. The contextual information includes; descriptions of Fort Stewart climatology, plant communities, fuels, and operational information from Fort Stewart prescribed fire organization. Burn unit perimeters and maps as well as Remote Automated Weather Station (RAWS) data from Wright Army Airfield covering all burn days is also included. The contextual information is to be used as an aid to researchers when publishing on WFSI related research campaigns. To find datasets collected as part of the research campaign, search keyword: "Eglin Air Force Base 2023".

ABSTRACT OBJECTIVE To investigate the relationship between covid-19 hospital mortality and risk factors, innovating by considering contextual and individual factors and spatial dependency and using data from the city of São Paulo, Brazil. METHODS The study was performed with a spatial hierarchical retrospective cohort design using secondary data (individuals and contextual data) from hospitalized patients and their geographic unit residences. The study period corresponded to the first year of the pandemic, from February 25, 2020 to February 24, 2021. Mortality was modeled with the Bayesian context, Bernoulli probability distribution, and the integrated nested Laplace approximations. The demographic, distal, medial, and proximal covariates were considered. RESULTS We found that per capita income, a contextual covariate, was a protective factor (odds ratio: 0.76 [95% credible interval: 0.74–0.78]). After adjusting for income, the other adjustments revealed no differences in spatial dependence. Without income inequality in São Paulo, the spatial risk of death would be close to one in the city. Other factors associated with high covid-19 hospital mortality were male sex, advanced age, comorbidities, ventilation, treatment in public healthcare settings, and experiencing the first covid-19 symptoms between January 24 and February 24, 2021. CONCLUSIONS Other than sex and age differences, geographic income inequality was the main factor responsible for the spatial differences in the risk of covid-19 hospital mortality. Investing in public policies to reduce socioeconomic inequities, infection prevention, and other intersectoral measures should focus on lower per capita income, to control covid-19 hospital mortality.

Examples of the data analysis process from meaning unit to category.

This data were collected during the Safety Pilot Model Deployment (SPMD). The data sets that these entities will provide include basic safety messages (BSM), vehicle trajectories, and various driver-vehicle interaction data, as well as contextual data that describes the circumstances under which the Model Deployment data was collected. Large portion of the data contained in this environment is obtained from on board vehicle devices and roadside units. This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

Paired-sample t-tests of differences between measured exposures to outdoor ALAN while controlling contextual settings of buffer zone radius.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "The Eastern Korinthia Archaeological Survey" data publication.

An Open Context "subjects" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Context" record is part of the "San Diego Archaeological Center" data publication.

The cross-calibration between different remote sensing NTL data.

Context

This data-set contains information from the "Protocol Gift Util" in the US department of state, which documents all of the official gifts accepted by the president and white house staff. Quoting from the U.S. Department of State website:

The Protocol Gift Unit within the Office of the Chief of Protocol serves as the central processing point for all tangible gifts received from foreign sources by employees of the Executive Branch of the Federal government. The Unit is responsible for the creation and maintenance of the official record of all gifts presented by the Department of State to officials of foreign governments. Working closely with the Chief of Protocol and the staffs of the President, the Vice President, and the Secretary of State, the Gift Unit selects the gifts presented to foreign dignitaries. Gifts received by the President, Vice President, and the Secretary of State and their spouses from foreign governments are also handled by the Gift Unit in the Office of Protocol.

Content

The file contains data scraped from the the Protocol Gift Unit website (the R script and more information about exclusions and possible issues can be found here.

• Number of recorded gifts: 1913 (after some exclusions)
• Years: 2002 to 2015
• Encoding: UTF8 (with many special characters)

Inspiration

Looking forward to see how people can use creative text mining techniques to extract more information about the different columns (for example classify givers / receivers, tag geographies, extract the gift object from the description text, etc.). You can find my future humble attempts here.

Reporting by health unit, this layer includes the following information:percentage of rural area populationpercentage of seniors (65 and older) for 2018percentage of immigrant populationpercentage of aboriginal populationpercentage of children living in low income familiespercentage of households experiencing food insecurity*prevalence of diabetes*prevalence of chronic obstructive pulmonary (COPD) disease*prevalence of high blood pressureprevalence of mood disorders*Data provided by the Canadian Institute for Health Information. Original source data can be found here. Complete metadata for this layer can be found here. Related dataset Contextual Health Measures by Province.* indicates that some of the numbers are estimated and should be used with caution.

1. INTRODUCTION

MalaMix is a compiled metabarcoding dataset composed of 451 marine samples collected from a range of depths - from the surface (3m) to deep waters (as far down as 4800m). This dataset covers three ocean layers: the epi- (0-200m – including DCM), meso- (200-1000m) and bathypelagic (1000-4000m). MalaMix combines samples obtained during two oceanographic expeditions with similar sampling strategies: i) the Malaspina-2010 global expedition that produced 263 samples collected between December 2010 and July 2011 from 120 stations distributed along the tropical and subtropical portions (latitudes between 35° N and 40° S) of the Pacific, Atlantic and Indian oceans; and ii) the HotMix trans-Mediterranean cruise that produced 188 samples collected between April and May 2014 in 29 stations distributed along the whole Mediterranean Sea (from -5° W to 33° E) and the adjacent Northeast Atlantic Ocean.

MalaMix comprises:

a 16S-V4V5 rRNA gene ASV table (MalaMix_16S.csv);

an 18S-V4 rRNA gene ASV table (MalaMix_18S.csv);

two tables of contextual metadata (MalaMix_EnvData_16S and MalaMix_EnvData_18S) including 6 standardized environmental parameters (temperature [°C], salinity, fluorescence, PO43− [µmol L-1], NO3− [µmol L-1], and SiO2 [µmol L-1]) as well as species taxonomic and phylogenetic diversity metrics

a table (MalaMix_FCdata.csv) with flow cytometry microbial counts [cell mL-1] and bacterial activity measurements [pmol Leu L-1 h-1];

a README file (README_Metadata.csv) describing the meaning and units of each variable column in the metadata tables.

The raw DNA sequences are publicly available at the European Nucleotide Archive (https://www.ebi.ac.uk/ena) under accession numbers PRJEB23913 [18S rRNA genes] & PRJEB25224 [16S rRNA genes] for the Malaspina surface dataset; PRJEB23771 [18S rRNA genes] & PRJEB45015 [16S rRNA genes] for the Malaspina vertical profiles; PRJEB45011 [16S rRNA genes] & PRJEB45014 [18S rRNA genes] for the Malaspina deep sea dataset; and PRJEB44683 [18S rRNA genes] & PRJEB44474 [16S rRNA genes] for the HotMix expedition.

Further methodological details are available here: https://www.biorxiv.org/content/10.1101/2023.01.13.523743v1

1. FUTURE FORMAT CHANGES

No major changes are expected for the main general format of the database.

1. ACKNOWLEDGMENTS

The current dataset was generated with funds from the projects INTERACTOMICS (CTM2015-69936-P, MINECO, Spain), MicroEcoSystems (240904, RCN, Norway), MINIME (PID2019-105775RB-I00, AEI, Spain), and PID2021-125469NB-C31 (AEI, Spain), as well as DOREMI (CTM2012-34294) and HOTMIX (CTM2011-30010-C02-01 and CTM2011-30010-C02-02) of the Spanish Ministry of Economy and Innovation, co-financed with FEDER funds.

1. COPYRIGHT NOTICE

This database is provided “as is” and without any warranty of any kind, of openly available for non-commerical purposes (CC BY-NC). CC BY-NC means that users can make use of the work (including copying, distributing, adapting and building upon the work), but only for noncommercial purposes and as long as attribution is given to the creator: https://oabooks-toolkit.org/lifecycle/article/4012101-choosing-a-license