Mexico Average Hours Worked per Person Employed: Index: Professional, Scientific and Technical Activities, Administrative and Support Service Activities data was reported at 97.190 2015=100 in 2023. This records a decrease from the previous number of 97.660 2015=100 for 2022. Mexico Average Hours Worked per Person Employed: Index: Professional, Scientific and Technical Activities, Administrative and Support Service Activities data is updated yearly, averaging 99.620 2015=100 from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 101.440 2015=100 in 2005 and a record low of 97.190 2015=100 in 2023. Mexico Average Hours Worked per Person Employed: Index: Professional, Scientific and Technical Activities, Administrative and Support Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Mexico – Table MX.OECD.PDB: Hours Worked: by Industry: OECD Member: Annual.

Norway Earnings Index: Monthly Avg: Real Estate, Professional, Scientific and Technical Activities: Advertising and Market Research data was reported at 104.000 1Q2016=100 in Sep 2018. This records a decrease from the previous number of 106.300 1Q2016=100 for Jun 2018. Norway Earnings Index: Monthly Avg: Real Estate, Professional, Scientific and Technical Activities: Advertising and Market Research data is updated quarterly, averaging 103.000 1Q2016=100 from Mar 2016 (Median) to Sep 2018, with 11 observations. The data reached an all-time high of 106.300 1Q2016=100 in Jun 2018 and a record low of 99.300 1Q2016=100 in Dec 2016. Norway Earnings Index: Monthly Avg: Real Estate, Professional, Scientific and Technical Activities: Advertising and Market Research data remains active status in CEIC and is reported by Statistics Norway. The data is categorized under Global Database’s Norway – Table NO.G024: Earnings Index: Monthly Average: Q1 2016=100.

One the most obvious difficulties in comparing the influence of landscape on crop production across studies is the choice of landscape metric. There exist countless metrics of landscape composition—the categories of land cover found on a landscape—and landscape configuration—the spatial organization of these categories. Common landscape composition metrics include measures of diversity—such as the Shannon Diversity Index or the Simpson Diversity Index—and measures of land cover composition—such as the percent of the landscape classified as natural cover. Common landscape configuration metrics include measures of patch size (contiguous areas of the same land cover) and mixing as well as edge length (linear length of patch boundaries/perimeter) and fragmentation. Even just considering diversity metrics, numerous options to select from can be found in the literature. Each one of these metrics has its own particularities in terms of sensitivity to scale, rare categories, and boundaries that can significantly alter the conclusions of studies examining the relationship between landscape characteristics and crop production. To address this challenge, we assess the sensitivity of our model results to a number of indicators of landscape composition and configuration using the USDA NASS Cropland Data Layer (CDL) as our indicator of land cover. This dataset classifies land cover at a 30-meter resolution nationwide from 2008 to present using satellite imagery and extensive ground truth data. While the 30-meter spatial resolution of this land cover data cannot accurately represent very small or narrow patches of land cover including shelterbelts and wildflower strips, given its relatively high resolution, full coverage, and historical availability, it is the best data for understanding land cover across agricultural landscapes in the U.S. We extract landscape indices from the CDL data using the landscapemetrics package in R, which considers all land cover in each county’s bounding box with the exception of open water and null categories. We measure compositional complexity using a set of six common landscape metrics associated with the number or the predominance of land cover categories across a landscape. Five of these metrics—Shannon Diversity Index, Simpson Diversity Index, Richness, Shannon Evenness Index, and Simpson Evenness Index—can be considered measures of land cover diversity. The sixth metric–Percent Natural Cover–is a simple measure of the predominance of undeveloped and uncultivated land cover classes (such as wetlands, grasslands, and forests) on a landscape. All of the compositional complexity metrics are aspatial, in that their calculation is not contingent on how land cover categories are arranged within the landscape. Configurational complexity is measured using four landscape metrics associated with the size of land cover patches (continuous areas of a single land cover category), shape of land cover patches, or mixing of land cover categories across the landscape. The metrics Mean Patch Area and Largest Patch Index are most strongly associated with patch size, the Contagion metric is a measure of land cover category mixing and strongly related to patch size, and the Edge Density metric is related to patch size and shape. Unlike the landscape composition metrics, the four landscape configuration metrics are spatially explicit and depend on the arrangement of land cover categories across the landscape. All code used to build data can be found here: https://github.com/katesnelson/aglandscapes-what-or-how Resources in this dataset:

Resource Title: County-level Estimates of Landscape Complexity and Configuration in the Coterminous US File Name: landscape_panel.txt Resource Description: GEOID: State and county FIPS codes in format SSCCC YEAR: Year in which CDL data was collected VALUE: Index value INDEX_NAME: Indices with _AG were computed for the subset of agricultural lands in a county. Indices with _ALL were computed for the entire landscape (agricultural and nonagricultural lands) in a county. LSM_AREA_MN_AG/ALL: Mean patch area, a measure of patch structure. Approaches 0 if all patches are small. Increases, without limit, as the patch areas increase. Higher values generally indicate lower complexity. LSM_CONTAG_AG/ALL: Contagion, a measure of dispersion and interspersion of land cover classes where a high proportion of like adjacencies and an uneven distribution of pairwise adjacencies produces a high contagion value. Range of 0 to 100. Higher values generally indicate lower complexity. LSM_ED_AG/ALL: Edge density, a measure of the patchiness of the landscape. Equals 0 if only one land cover is present and increased without limit as more land cover patches are added. Higher values generally indicate higher complexity. LSM_LPI_AG/ALL: Largest patch index, a measure of patch dominance representing the percentage of the landscape covered by the single largest patch. Approaches 0 when the largest patch is becoming small and equals 100 when only one patch is present. Higher values generally indicate lower complexity. LSM_RICH_AG/ALL: Richness, a measure of the abundance of categories. Higher values generally indicate higher complexity. LSM_SHDI_AG/ALL: Shannon Diversity Index, a measure of the abundance and evenness of land cover categories. This index is sensitive to rare land cover categories. Typical values are between 1.5 and 3. Higher values indicate higher complexity. LSM_SHEI_ALL: Simpson Evenness Index, a measure of diversity or dominance calculated as the ratio between the Shannon Diversity Index and the theoretical maximum of the Shannon Diversity Index. Shannon Evenness Index = 0 when there is only one land cover on the landscape and equals 1 when all land cover classes are equally distributed. Higher values generally indicate higher complexity. LSM_SIDI_ALL: Simpson Diversity Index, a diversity measure that considers the abundance and evenness of land cover categories. This index is not sensitive to rare land cover categories. Values range from 0 to 1. Higher values generally indicate higher complexity MODE_AG : Most dominant agricultural land use type found in the data (mode of agricultural CDL categories) MODE_ALL : Most dominant land use type found in the data (mode of all land use categories) PNC : Percent natural cover

Resource Title: Technical Validation File Name: technical_validation.txt

The housing costs compared to the EU average differ significantly between Member States. The highest housing costs in 2021 compared to the EU average were found in Ireland (94 % above the EU average), Luxembourg (87 % above) and Denmark (78 % above). The lowest, on the other hand, were observed in Bulgaria (64 % below the EU average) and Poland (62 % below).

More at: https://ec.europa.eu/eurostat/cache/digpub/housing/bloc-2a.html?lang=en&lang=en

Mean values and standard deviations of quality statistical indices computed for 100 resampled validation datasets*.

This large longitudinal study is the result of professor Tatu Vanhanen's long-term research on democratization and power resources. International scientific community knows this data also by the name "Vanhanen's Index of Power Resources". The data have been collected from several written sources and have been published as appendices of five different books. The books are listed in the section Data sources below. The original sources of the numerical data published in these books have been collected to a separate document containing background information. Vanhanen divides the variables of his dataset into two main groups. The first group consists of Measures of Democracy and includes three variables. The second group is called Measures of Resource Distribution. The variables in the first group (Measures of Democracy) are Competition, Participation and Index of Democratization. The value of Competition is calculated by subtracting the percentage of votes/seats gained by the largest political party in parliamentary elections and/or in presidential (executive) elections from 100%. The Participation variable is an aggregate of the turnout in elections (percentage of the total population who voted in the same election) and the number of referendums. Each national referendum raises the value of Participation by five percentage points and each state referendum by one percentage point for the year of the referendum. The upper limit for both variables is 70%. Index of Democratization is derived by first multiplying the above mentioned variables Competition and Participation and then dividing this product by 100. Six variables are used to measure resource distribution: 1) Urban Population (%) (as a percentage of total population). 2) Non-Agricultural Population (%) (derived by subtracting the percentage of agricultural population from 100%). 3) Number of students: the variable denotes how many students there are in universities and other higher education institutions per 100.000 inhabitants of the country. Two ways are used to calculate the percentage of Students (%): before the year 1988 the value 1000 of the variable Number of students is equivalent to 100% and between the years 1988-1998 the value 5000 of the same variable is equivalent to 100%. 4) Literates (%) (as a percentage of adult population). 5) Family Farms Are (%) (as a percentage of total cultivated area or of total area of holdings). 6) Degree of Decentralization of Non-Agricultural Economic Resources. This variable has been calculated from the 1970s. Three new variables have been derived from the above mentioned six variables. 1) Index of Occupational Diversification is derived by calculating the arithmetic mean of Urban Population and Non-Agricultural Population. 2) Index of Knowledge Distribution is derived by calculating the arithmetic mean of Students and Literates. 3) Index of Distribution of Economic Power Resources is derived by first multiplying the value of Family Farm Area with the percentage of agricultural population. Then the value of Degree of Decentralization of Non-Agricultural Economic Resources is multiplied with the percentage of Non-Agricultural Population. After this these two products are simply added up. Finally two new variables have derived from the above mentioned variables. First derived variable is Index of Power Resources, calculated by multiplying the values of Index of Occupational Diversification, Index of Knowledge Distribution and Index of the Distribution of Economic Power Resources and then dividing the product by 10 000. The second derived variable Mean is the arithmetic mean of the five (from the 1970s six) explanatory variables. This differs from Index of Power Resources in that a low value of any single variable does not reduce the value of Mean to any great extent.

Taiwan AREI: Professional, Scientific & Technical Services data was reported at 107.180 2001=100 in Dec 2008. This records an increase from the previous number of 106.850 2001=100 for Nov 2008. Taiwan AREI: Professional, Scientific & Technical Services data is updated monthly, averaging 70.495 2001=100 from Jan 1975 (Median) to Dec 2008, with 408 observations. The data reached an all-time high of 107.820 2001=100 in Oct 2008 and a record low of 12.780 2001=100 in Apr 1975. Taiwan AREI: Professional, Scientific & Technical Services data remains active status in CEIC and is reported by Directorate-General of Budget, Accounting and Statistics, Executive Yuan. The data is categorized under Global Database’s Taiwan – Table TW.G039: Average Regular Earnings Index: 2001=100.

In scientific research, assessing the impact and influence of authors is crucial for evaluating their scholarly contributions. Whereas in literature, multitudinous parameters have been developed to quantify the productivity and significance of researchers, including the publication count, citation count, well-known h index and its extensions and variations. However, with a plethora of available assessment metrics, it is vital to identify and prioritize the most effective metrics. To address the complexity of this task, we employ a powerful deep learning technique known as the Multi-Layer Perceptron (MLP) classifier for the classification and the ranking purposes. By leveraging the MLP’s capacity to discern patterns within datasets, we assign importance scores to each parameter using the proposed modified recursive elimination technique. Based on the importance scores, we ranked these parameters. Furthermore, in this study, we put forth a comprehensive statistical analysis of the top-ranked author assessment parameters, encompassing a vast array of 64 distinct metrics. This analysis gives us treasured insights in between these parameters, shedding light on the potential correlations and dependencies that may affect assessment outcomes. In the statistical analysis, we combined these parameters by using seven well-known statistical methods, such as arithmetic means, harmonic means, geometric means etc. After combining the parameters, we sorted the list of each pair of parameters and analyzed the top 10, 50, and 100 records. During this analysis, we counted the occurrence of the award winners. For experimental proposes, data collection was done from the field of Mathematics. This dataset consists of 525 individuals who are yet to receive their awards along with 525 individuals who have been recognized as potential award winners by certain well known and prestigious scientific societies belonging to the fields’ of mathematics in the last three decades. The results of this study revealed that, in ranking of the author assessment parameters, the normalized h index achieved the highest importance score as compared to the remaining sixty-three parameters. Furthermore, the statistical analysis results revealed that the Trigonometric Mean (TM) outperformed the other six statistical models. Moreover, based on the analysis of the parameters, specifically the M Quotient and FG index, it is evident that combining these parameters with any other parameter using various statistical models consistently produces excellent results in terms of the percentage score for returning awardees.

In scientific research, assessing the impact and influence of authors is crucial for evaluating their scholarly contributions. Whereas in literature, multitudinous parameters have been developed to quantify the productivity and significance of researchers, including the publication count, citation count, well-known h index and its extensions and variations. However, with a plethora of available assessment metrics, it is vital to identify and prioritize the most effective metrics. To address the complexity of this task, we employ a powerful deep learning technique known as the Multi-Layer Perceptron (MLP) classifier for the classification and the ranking purposes. By leveraging the MLP’s capacity to discern patterns within datasets, we assign importance scores to each parameter using the proposed modified recursive elimination technique. Based on the importance scores, we ranked these parameters. Furthermore, in this study, we put forth a comprehensive statistical analysis of the top-ranked author assessment parameters, encompassing a vast array of 64 distinct metrics. This analysis gives us treasured insights in between these parameters, shedding light on the potential correlations and dependencies that may affect assessment outcomes. In the statistical analysis, we combined these parameters by using seven well-known statistical methods, such as arithmetic means, harmonic means, geometric means etc. After combining the parameters, we sorted the list of each pair of parameters and analyzed the top 10, 50, and 100 records. During this analysis, we counted the occurrence of the award winners. For experimental proposes, data collection was done from the field of Mathematics. This dataset consists of 525 individuals who are yet to receive their awards along with 525 individuals who have been recognized as potential award winners by certain well known and prestigious scientific societies belonging to the fields’ of mathematics in the last three decades. The results of this study revealed that, in ranking of the author assessment parameters, the normalized h index achieved the highest importance score as compared to the remaining sixty-three parameters. Furthermore, the statistical analysis results revealed that the Trigonometric Mean (TM) outperformed the other six statistical models. Moreover, based on the analysis of the parameters, specifically the M Quotient and FG index, it is evident that combining these parameters with any other parameter using various statistical models consistently produces excellent results in terms of the percentage score for returning awardees.

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Malta Average Hours Worked per Person Employed: Index: Professional, Scientific and Technical Activities, Administrative and Support Service Activities data was reported at 101.310 2015=100 in 2019. This records an increase from the previous number of 99.560 2015=100 for 2018. Malta Average Hours Worked per Person Employed: Index: Professional, Scientific and Technical Activities, Administrative and Support Service Activities data is updated yearly, averaging 115.655 2015=100 from Dec 2000 (Median) to 2019, with 20 observations. The data reached an all-time high of 120.900 2015=100 in 2005 and a record low of 99.560 2015=100 in 2018. Malta Average Hours Worked per Person Employed: Index: Professional, Scientific and Technical Activities, Administrative and Support Service Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Malta – Table MT.OECD.PDB: Hours Worked: by Industry: Non OECD Member: Annual.

This tabular data set represents the mean base-flow index expressed as a percent, compiled for every catchment in NHDPlus for the conterminous United States. Base flow is the component of streamflow that can be attributed to ground-water discharge into streams. The source data set is Base-Flow Index for the Conterminous United States (Wolock, 2003).

The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when availa ...

IntroductionEstimating a reliable gestational age (GA) is essential in providing appropriate care during pregnancy. With advancements in data science, there are several publications on the use of artificial intelligence (AI) models to estimate GA using ultrasound (US) images. The aim of this meta-analysis is to assess the accuracy of AI models in assessing GA against US as the gold standard.MethodsA literature search was performed in PubMed, CINAHL, Wiley Cochrane Library, Scopus, and Web of Science databases. Studies that reported use of AI models for GA estimation with US as the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Mean error in GA was estimated using STATA version-17 and subgroup analysis on trimester of GA assessment, AI models, study design, and external validation was performed.ResultsOut of the 1,039 studies screened, 17 were included in the review, and of these 10 studies were included in the meta-analysis. Five (29%) studies were from high-income countries (HICs), four (24%) from upper-middle-income countries (UMICs), one (6%) from low-and middle-income countries (LMIC), and the remaining seven studies (41%) used data across different income regions. The pooled mean error in GA estimation based on 2D images (n = 6) and blind sweep videos (n = 4) was 4.32 days (95% CI: 2.82, 5.83; l2: 97.95%) and 2.55 days (95% CI: −0.13, 5.23; l2: 100%), respectively. On subgroup analysis based on 2D images, the mean error in GA estimation in the first trimester was 7.00 days (95% CI: 6.08, 7.92), 2.35 days (95% CI: 1.03, 3.67) in the second, and 4.30 days (95% CI: 4.10, 4.50) in the third trimester. In studies using deep learning for 2D images, those employing CNN reported a mean error of 5.11 days (95% CI: 1.85, 8.37) in gestational age estimation, while one using DNN indicated a mean error of 5.39 days (95% CI: 5.10, 5.68). Most studies exhibited an unclear or low risk of bias in various domains, including patient selection, index test, reference standard, flow and timings and applicability domain.ConclusionPreliminary experience with AI models shows good accuracy in estimating GA. This holds tremendous potential for pregnancy dating, especially in resource-poor settings where trained interpreters may be limited.Systematic Review RegistrationPROSPERO, identifier (CRD42022319966).

In scientific research, assessing the impact and influence of authors is crucial for evaluating their scholarly contributions. Whereas in literature, multitudinous parameters have been developed to quantify the productivity and significance of researchers, including the publication count, citation count, well-known h index and its extensions and variations. However, with a plethora of available assessment metrics, it is vital to identify and prioritize the most effective metrics. To address the complexity of this task, we employ a powerful deep learning technique known as the Multi-Layer Perceptron (MLP) classifier for the classification and the ranking purposes. By leveraging the MLP’s capacity to discern patterns within datasets, we assign importance scores to each parameter using the proposed modified recursive elimination technique. Based on the importance scores, we ranked these parameters. Furthermore, in this study, we put forth a comprehensive statistical analysis of the top-ranked author assessment parameters, encompassing a vast array of 64 distinct metrics. This analysis gives us treasured insights in between these parameters, shedding light on the potential correlations and dependencies that may affect assessment outcomes. In the statistical analysis, we combined these parameters by using seven well-known statistical methods, such as arithmetic means, harmonic means, geometric means etc. After combining the parameters, we sorted the list of each pair of parameters and analyzed the top 10, 50, and 100 records. During this analysis, we counted the occurrence of the award winners. For experimental proposes, data collection was done from the field of Mathematics. This dataset consists of 525 individuals who are yet to receive their awards along with 525 individuals who have been recognized as potential award winners by certain well known and prestigious scientific societies belonging to the fields’ of mathematics in the last three decades. The results of this study revealed that, in ranking of the author assessment parameters, the normalized h index achieved the highest importance score as compared to the remaining sixty-three parameters. Furthermore, the statistical analysis results revealed that the Trigonometric Mean (TM) outperformed the other six statistical models. Moreover, based on the analysis of the parameters, specifically the M Quotient and FG index, it is evident that combining these parameters with any other parameter using various statistical models consistently produces excellent results in terms of the percentage score for returning awardees.

This survey continues the original dataset "FSD1216 Democratization and Power Resources 1850-2000" collected by professor Tatu Vanhanen, which was a result of long-term research on democratization and power resources. The updated data have been collected from several written sources and published also in Vanhanen's book "The Limits of Democratization". The original sources of the numerical data published in the book have been collected to a separate document, the link to which can be found below in the section Other material: Original sources. Eight variables are used to measure country-specific resource distribution: 1) Tertiary Enrollment Ratio (%); 2) Adult Literacy Rate (%); 3) Index of Intellectual Power Resources, IR; 4) Family Farms, FF (%); 5) Agricultural Population, AP (%); 6) Estimated Degree of Decentralization of Economic Power Resources, DD; 7) Index of Economic Power Resources, ER; and 8) Index of Power Resources, IPR. The variables have been updated from the previous dataset, and the calculation methods have been specified in some cases, or even reconstructed in a totally new way in some cases. Tertiary Enrollment Ratio (%) is based on the percentage of students enrolled in universities and institutes of higher learning within the relevant age group. Adult Literacy Rate (%) is calculated as a percentage of adult population. Index of Intellectual Power Resources, IR is the mean of these two variables. Family Farms, FF (%) means the percentage of total cultivated area or of total area of holdings. The proportion of agricultural population between 2000 and 2005 is coded in variable Agricultural Population, AP (%). Estimated Degree of Decentralization of Economic Power Resources, DD is calculated by adding the percentage of the population living under the poverty line with the richest 10 percent of the population, and then calculating the proportion of their income or expenditure compared to the whole population minus 10 percentage units, and then subtracting the sum from 100. In some cases, the calculated percentage has been increased or decreased for reasons given in detail in Vanhanen Vanhanen's book "The Limits of Democratization". Index of Economic Power Resources, ER is calculated by the formula ER = (FF * AP) + (DD * NAP), where NAP = 100-AP. The last variable, Index of Power Resources, IPR is calculated by dividing the product of Index of Intellectual Power Resources, IR and Index of Economic Power Resources, ER by 100.

Explore LSEG S&P Global Purchasing Managers Index (PMI) for monthly surveys that provide up-to-date, accurate, and unique indicators of economic trends.

Norway Earnings Index: Monthly Avg: Real Estate, Professional, Scientific and Technical Activities: Architecture, Engineering Activities data was reported at 102.300 1Q2016=100 in Sep 2018. This records a decrease from the previous number of 104.700 1Q2016=100 for Jun 2018. Norway Earnings Index: Monthly Avg: Real Estate, Professional, Scientific and Technical Activities: Architecture, Engineering Activities data is updated quarterly, averaging 101.800 1Q2016=100 from Mar 2016 (Median) to Sep 2018, with 11 observations. The data reached an all-time high of 104.900 1Q2016=100 in Mar 2018 and a record low of 97.800 1Q2016=100 in Sep 2016. Norway Earnings Index: Monthly Avg: Real Estate, Professional, Scientific and Technical Activities: Architecture, Engineering Activities data remains active status in CEIC and is reported by Statistics Norway. The data is categorized under Global Database’s Norway – Table NO.G024: Earnings Index: Monthly Average: Q1 2016=100.

ISCCP H Gridded By Hour (HGH) cloud_irtype_label Dimensioned By cloud_irtype. _CoordSysBuilder=ucar.nc2.dataset.conv.CF1Convention acknowledgement=This project received funding support from NASA REASON PROGRAM, NASA MEASURES PROGRAM and NOAA CLIMATE DATA RECORD (CDR) PROGRAM cdm_data_type=Grid comment=---------- TO RE-MAP EQUAL-AREA MAP TO EQUAL-ANGLE (SQUARE LON,LAT) MAP ---------- For display purposes, the ISCCP equal-area map may be converted to an equal-angle map using replication. The variables 'eqlat_index', 'sqlon_beg' and 'sqlon_end'are provided for this purpose. Each equal-area cell is replicated into a specific range of longitude cells in the equal-angle map. For example, to remap an equal-area array eqvar[41252] to an equal-angle array sqmap[360,180], each eqvar[i] should be replicated into the range of cells indicated by sqlon_beg[i] and sqlon_end[i], and the lat index eqlat_index[i]. Using Fortran notation the assignment is: sqmap[sqlon_beg[i]:sqlon_end[i], eqlat_index[i]] = eqvar[i]. ---------- TO CONVERT COUNT UNITS TO PHYSICAL UNITS ---------- When attribute conversion_table is present for any variable, the reported values of count units may be converted to physical quantities by using the specified conversion table variable as a look-up table whose index is count value 0-255. For example, temperature = tmptab(count), temperature_variance = tmpvar(count), pressure = pretab(count), reflectance = rfltab(count), optical_depth = tautab(count), ozone = ozntab(count), humidity = humtab(count), water_path = wpatab(count). ---------- DEFINITION OF CLOUD TYPES ---------- VIS/IR cloud types are defined by a histogram of cloud top pressure and cloud optical depth, for both liquid and ice clouds. IR cloud types are defined by a histogram of cloud top pressure. Identification labels for the 18 VIS/IR cloud types and the 3 IR cloud types are given in the 'cloud_type_label' and 'cloud_irtype_label' variables, which correspond to the order of the cloud type variable arrays. contributor_name=William B. Rossow, Alison Walker, Violeta Golea, NOAA, EUMETSAT, ESA, JP/JMA, CHINA/CMA, BR/INPE, NASA contributor_role=principalInvestigator, processor, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider Conventions=CF-1.4, ACDD-1.3 date_metadata_modified=2019-07-19T06:48:07Z geospatial_bounds=POLYGON((-90.0 0.0, -90.0 360.0, 90.0 360.0, 90.0 0.0, -90.0 0.0)) geospatial_bounds_crs=EPSG:4326 history=Fri Jul 19 06:48:07 2019: ncatted -a conversion_table,,d,, -a title,global,a,c, Basic -a description,snoice,m,c,Mean snow/ice cover for the cell -a source,global,o,c,The source for the ISCCP Basic data files are the original ISCCP files. ISCCP Basic represents a subset of variables from ISCCP that have been remapped to equal-angle, do not use table to store data, etc. in order to make the files CF compliant -a product_version,global,m,c,v01r00 Basic -a date_issued,global,m,c,2019-07-19T06:48:07Z -a date_created,global,m,c,2019-07-19T06:48:07Z -a date_modified,global,m,c,2019-07-19T06:48:07Z -a date_metadata_modified,global,m,c,2019-07-19T06:48:07Z -a long_name,cldbin_bounds,c,c,Boundaries of the cloud fractional amounts -a description,cldbin_bounds,c,c,The frequency of occurrence of this amount of cloud cover is provided in cldamt_dist -a units,cldbin_bounds,c,c,percent -a cell_methods,eqheight,c,c,area: mean -a cell_methods,snoice,c,c,area: mean -a cell_methods,cldamt,c,c,area: mean time: mean within days -a cell_methods,^pc,c,c,area: mean time: mean within days -a cell_methods,^tc,c,c,area: mean time: mean within days -a cell_methods,^tau,c,c,area: mean time: mean within days -a cell_methods,^wp,c,c,area: mean time: mean within days -a cell_methods,_time$,c,c,area: mean time: standard_deviation -a cell_methods,_space$,c,c,area: standard_deviation time: mean -a cell_methods,cldamt_ir,c,c,area: mean time: mean within days -a long_name,cldamt_irmarg,m,c,Cloud amount uncertainty (using IR data) -a cell_methods,cldamt_irmarg,c,c,area: mean time: mean within days -a note,cldamt_irmarg,c,c,This is the ISCCP variable: cldamt_irmarg. It represents the fraction of pixels that are colder than clear sky by a smaller amount than what is flagged in cldamt_ir and represents cloud amount uncertainty. -a cell_methods,cldamt_irtypes,c,c,area: mean time: mean within days -a cell_methods,cldamt_types,c,c,area: mean time: mean within days -a cell_methods,snoice,c,c,area: mean time: mean within days /glfs2/isccp-p/basic/intermediate//temp_file2.nc -O /glfs2/isccp-p/basic/intermediate//temp_file3.nc Fri Jul 19 06:48:06 2019: ncks --no-abc -4 -L 5 /glfs2/isccp-p/basic/intermediate//temp_file.nc -O /glfs2/isccp-p/basic/intermediate//temp_file2.nc 2019-05-14T19:54:07.000Z bhankins d2proda /glfs2/isccp-p/prd/wrkdirs/2017_06 2017 06 ; FMRC Best Dataset id=ISCCP.HGH.0.GLOBAL.2017.06.99.1800.GPC.10KM.CS00.EQ1.00.nc infoUrl=https://www.ncei.noaa.gov/thredds/catalog/cdr/isccp_hgh_agg/catalog.html?dataset=cdr/isccp_hgh_agg/ISCCP-H_Aggregation_Basic_Gridded_By_Hour_(HGH)_best.ncd institution=International Cloud Climatology Project (ISCPP) instrument=Himawari-8 AHI, SEVIRI, GOES-15 Imager, GOES-13 Imager, SEVIRI,, AVHRR-3 instrument_vocabulary=NASA Global Change Master Directory (GCMD) Instruments Keywords Version 8.1 isccp_gmt=18 isccp_input_files=ISCCP.HGG.0.GLOBAL.2017.06.01.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.02.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.03.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.04.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.05.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.06.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.07.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.08.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.09.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.10.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.11.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.12.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.13.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.14.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.15.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.16.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.17.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.18.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.19.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.20.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.21.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.22.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.23.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.24.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.25.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.26.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.27.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.28.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.29.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.30.1800.GPC.10KM.CS00.EQ1.00.nc isccp_month=6 isccp_number_of_satellites_contributing=7 isccp_percent_empty_cells=0 isccp_percent_full_cells=100 isccp_year=17 keywords_vocabulary=NASA Global Change Master Directory (GCMD) Science Keyword Version 8.1 location=Proto fmrc:ISCCP-H_Aggregation_Basic_Gridded_By_Hour_(HGH) metadata_link=gov.noaa.ncdc.C00956 naming_authority=gov.noaa.ncdc NCO=netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) platform=HIM-8, METEOSAT-10, GOES-15, GOES-13, METEOSAT-8, NOAA-19, METOP-A platform_vocabulary=NASA Global Change Master Directory (GCMD) Platforms Keyword Version 8.1 processing_level=3 program=NOAA Climate Data Record Program for satellites, FY 2016 project=International Satellite Cloud Climatology Project (ISCCP) references='Please include a citation for this paper in addition to the dataset citation when using the dataset: Rossow, W.B. and R.A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bulletin of the American Meteorological Society, 80, 2261-2287. doi: https://dx.doi.org/10.1175/1520-0477(1999)0802261:AIUCFI2.0.CO;2','ISCCP CDR Climate Algorithm Theoretical Basis Document (C-ATBD)' source=The source for the ISCCP Basic data files are the original ISCCP files. ISCCP Basic represents a subset of variables from ISCCP that have been remapped to equal-angle, do not use table to store data, etc. in order to make the files CF compliant sourceUrl=https://www.ncei.noaa.gov/thredds/dodsC/cdr/isccp_hgh_agg/ISCCP-H_Aggregation_Basic_Gridded_By_Hour_(HGH)_best.ncd time_coverage_duration=P1M time_coverage_resolution=PT3H

Here, our objective was to use a dragonfly citizen science database to identify both i) the spatial scale at which landscapes influence Odonates diversity in water bodies and ii) the effects of intensive agricultural landscapes on this diversity. We also identified the potential biases inherent to this particular database in order to assess to what extent it can be used reliably to infer the effects of intensive agriculture on Odonates’ species richness. The quantification of the distance up to which agricultural lands alter the diversity of Odonates is of paramount importance for (i) deciding which wetland areas are better candidates for applying efficient conservation strategies focusing on habitats (i.e., areas within this distance) or (ii) thinking about nature conservation policies at larger territorial scales. We compiled 7,731 observations made over 10 years by naturalists in a portion of the region Centre-Val-de-Loire (France) on 729 water bodies to analyse separately the effect of agricultural landscapes on the species richness of damselflies (Zygoptera) and dragonflies (Anisoptera). We focused on lentic systems because ponds and marshes are expected to be strongly influenced by local landscape characteristics, while river and stream ecosystems can also be influenced by quite more distant processes. However, we included the presence of rivers and streams near the water bodies in our analysis to control for potential dispersion of generalist species from lotic to lentic systems which could positively affect observed species richness independently of surrounding agricultural landscape. Citizen science databases are implicitly “imperfect” data because most observations are not necessarily done according to standard protocols and detection methods (Johansson et al. 2020). Study areaOur study area is the whole Indre-et-Loire administrative department in west-central France (Fig. 1a). This department covers a surface of 6,127km2 (centroid: 47°15’29.00’’N, 0°41’29.50’’E – EPSG 4326) and altitudes ranging from 26 to 187m (mean=100m). The climate is temperate with relatively warm summers (mean monthly temperature from 11.1°C to 25.5°C) and mild winters (mean monthly temperature from 1.9°C to 12.3°C). Rainfall is relatively low, around 650mm per year (fr.climate-data.org). The landscape of the whole region consists mainly of cultivated fields (56%) and forests (24%), leaving few portions for grape or urban areas (CORINE Land Cover, 2012). This territory is particularly heterogeneous regarding aquatic habitats because it includes several large rivers and streams with quite varied water flows, and a large variety of ponds, as well as marshes and peatlands. The area includes various types of ponds including natural and artificial water bodies, ponds with (and at various levels of vegetation diversity) or without aquatic vegetation, and in open as well as closed (forestry) environments (but see below for a description of water body types).Dragonfly distributionsMore than one hundred participants, mainly members of naturalists’ associations, were involved in this biodiversity inventory project. Dragonfly presence data were georeferenced and generated directly in the field through apps for mobile devices (ObsMapp and iObs). Subsequently, the data were extracted from a local sub-site of Observation.org, a worldwide observation-reporting system that provides high quality data for all taxa (Hochachka & Fink, 2012). Each data was validated by experts as a part of a local dragonfly atlas program (https://anepe-caudalis.observation.org/). These dragonflies and damselflies data are also available through the Global Biodiversity Information Facility (https://www.gbif.org/) and are part of the French National Inventory of the Natural Heritage coordinated by French Office of Biodiversity (OFB), National Centre for Scientific Research (CNRS) and National Museum of Natural History (MNHN).We considered information relevant to lentic hydrosystems and to contemporary landscapes by selecting observations collected inside water bodies and within a 50m radius around them between 2007 and 2017 (n=7,731 observations; an observation consists of a single species sight at specific geographic coordinates for a given day and contains number of individuals as well as reproductive or behavioural information). Hence, for each water body and each species, an index of potential autochthony was determined from indices showing that reproduction occurred (exuviae, emergence, egg laying or mating behaviours) and/or from the number of individuals in the case of damselfly species (reproduction was considered as “effective” for a species when ≥5 individuals were present). Citizen data are necessarily ‘imperfect’ data with several biases including no observation of reproduction cues for species that are actually autochthonous (e.g., survey was done within a daily window when species are not mating or laying eggs) and observation of reproduction cues for species that actually cannot develop within the water body where they lay eggs (e.g., presence of pollutants in the water). Therefore, this index of autochthony should be interpreted as a deliberately simple proxy for specifying the ecological level at which the effects of intensive agriculture may have the strongest impact. In addition, strict lotic species were automatically excluded (Table S1). Then, the data collected during the 10 years were pooled to compute the overall species richness (OSR) and the autochthonous species richness proxy (ASR) for each water body. Those two indexes were also calculated separately for dragonflies (OdragSR and AdragSR) and damselflies (OdamSR and AdamSR). Water bodies (n=729) were distributed over the entire study area (Fig. 1b) and categorized according to the habitat classification given in the French dragonflies monitoring program INVOD (Dommanget 2002) and by including some additional information on basic habitat diversity (categories are provided in Table S2). The area of water bodies ranged from 0.01 to 55.3 ha (mean 1.76 ha and median 0.29 ha; see Fig. S5-B for the complete distribution of water body areas).Landscape characteristicsWe retrieved the landscape characteristics around the water bodies using the CORINE Land Cover classification which is a pan-European land cover database. This database reports land use and cover based on images taken approximately 1–2 years before the release date (here, 2012) (Soukup et al., 2016). Around each of the 729 water bodies, we generated a set of landscape variables calculated for areas within a given distance from the water body. We generated four different landscape ‘buffers’ corresponding to areas within a distance of 200, 400, 800 and 1,600m from the water body using Q-GIS 2.14.11 (Fig. 1c). We chose the 200m landscape scale as the smallest reliable buffer given the CORINE Land Cover mapping scale (1:100,000). In accordance with previous published data on Odonata dispersal in agricultural landscapes, we chose not to go into a buffer larger than 1,600m (Conrad et al., 1999). These buffer sizes include the estimated distances travelled by non-territorial damselflies (e.g., coenagrionids) (Rouquette & Thompson, 2007) and, even if dragonflies (Anisoptera) can of course travel over larger distances, most of them are strongly territorial when sexually mature and remain relatively close to their water body. Although it is acknowledged that overlapping landscape buffers may not necessarily violate statistical independence (Zuckerberg et al. 2020), we also chose not to investigate larger radii to minimise spatial overlap between sites. For each water body and buffer, we calculated the water body area, the proportion (%) of intensive agriculture (CORINE Land Cover, 2012; Büttner et al., 2004) which represented the main land-use pressure in our study area, as well as the total length of streams and rivers (m/ha) (IGN, 2012). To ovoid collinearity issues, we did not include the proportion of forest cover in our models. Indeed, this proportion is strongly and negatively correlated with intensive agriculture in our system. In the CORINE Land Cover database (Büttner et al., 2004), we used the category of “arable lands” (code 2.1) as a proxy for intensive agriculture. In our study area, this arable land corresponds to this type of conventional agriculture that rely on frequent use of pesticides and chemical fertilizers. This intensive agriculture is locally dominated by wheat, rapeseed and sunflower (Fig. S1). In addition, the proportion of the land around water bodies covered by intensive agriculture (in a radius of 1,600m) remained similar between 2006, 2012 and 2018 (Fig. S2). Therefore, the snapshot of 2012 is representative of the intensive agriculture coverage for the whole period 2007-2017 when Odonata were observed by citizens.

Taiwan AREI: Services: Professional, Scientific & Technical Services data was reported at 111.520 2006=100 in Dec 2013. This records a decrease from the previous number of 111.580 2006=100 for Nov 2013. Taiwan AREI: Services: Professional, Scientific & Technical Services data is updated monthly, averaging 78.745 2006=100 from Jan 1975 (Median) to Dec 2013, with 468 observations. The data reached an all-time high of 111.940 2006=100 in Mar 2013 and a record low of 13.570 2006=100 in Apr 1975. Taiwan AREI: Services: Professional, Scientific & Technical Services data remains active status in CEIC and is reported by Directorate-General of Budget, Accounting and Statistics, Executive Yuan. The data is categorized under Global Database’s Taiwan – Table TW.G039: Average Regular Earnings Index: 2006=100.