Measuring the usage of informatics resources such as software tools and databases is essential to quantifying their impact, value and return on investment. We have developed a publicly available dataset of informatics resource publications and their citation network, along with an associated metric (u-Index) to measure informatics resources’ impact over time. Our dataset differentiates the context in which citations occur to distinguish between ‘awareness’ and ‘usage’, and uses a citing universe of open access publications to derive citation counts for quantifying impact. Resources with a high ratio of usage citations to awareness citations are likely to be widely used by others and have a high u-Index score. We have pre-calculated the u-Index for nearly 100,000 informatics resources. We demonstrate how the u-Index can be used to track informatics resource impact over time. The method of calculating the u-Index metric, the pre-computed u-Index values, and the dataset we compiled to calculate the u-Index are publicly available.

The Case Mix Index (CMI) is the average relative DRG weight of a hospital’s inpatient discharges, calculated by summing the Medicare Severity-Diagnosis Related Group (MS-DRG) weight for each discharge and dividing the total by the number of discharges. The CMI reflects the diversity, clinical complexity, and resource needs of all the patients in the hospital. A higher CMI indicates a more complex and resource-intensive case load. Although the MS-DRG weights, provided by the Centers for Medicare & Medicaid Services (CMS), were designed for the Medicare population, they are applied here to all discharges regardless of payer. Note: It is not meaningful to add the CMI values together.

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants.

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants.

More information and details about the data provided can be found at http://www.bls.gov/cpi

Price index of consumer goods and services is calculated on the basis of the results of:
- surveys on prices of consumer goods and services on the retail market,
- surveys on household budgets, providing data on average expenditures on consumer goods and services; these data are then used for compilation of a weight system.

Calculating price index of consumer goods and services is done on the basis of the Classification of Individual Consumption by Purpose (COICOP) adapted for the use of Harmonized Indices of Consumer Prices (HICP).

The price index of a representative in the region included in the price survey results from relating its average monthly price to an average annual price from the previous yea The all-Polish price index of a representative included in the survey is calculated as geometric mean of price indices from all regions. Calculating price indices of groups of consumer goods and services at the lowest level of weight system aggregation is done on the basis of price indices of the representatives included in price survey in a given group by using geometric mean. They are then used by applying weight system to calculate indices of higher level of aggregation up to the price index of total consumer goods and services. price index is calculated in line with the Laspeyress’s formula by applying weights from the year preceding the reference year.

The index relates to costs ruling on the first day of each month. NATIONAL HOUSE CONSTRUCTION COST INDEX; Up until October 2006 it was known as the National House Building Index Oct 2000 data; The index since October, 2000, includes the first phase of an agreement following a review of rates of pay and grading structures for the Construction Industry and the first phase increase under the PPF. April, May and June 2001; Figures revised in July 2001due to 2% PPF Revised Terms. March 2002; The drop in the March 2002 figure is due to a decrease in the rate of PRSI from 12% to 10¾% with effect from 1 March 2002. The index from April 2002 excludes the one-off lump sum payment equal to 1% of basic pay on 1 April 2002 under the PPF. April, May, June 2003; Figures revised in August'03 due to the backdated increase of 3% from 1April 2003 under the National Partnership Agreement 'Sustaining Progress'. The increases in April and October 2006 index are due to Social Partnership Agreement "Towards 2016". March 2011; The drop in the March 2011 figure is due to a 7.5% decrease in labour costs. Methodology in producing the Index Prior to October 2006: The index relates solely to labour and material costs which should normally not exceed 65% of the total price of a house. It does not include items such as overheads, profit, interest charges, land development etc. The House Building Cost Index monitors labour costs in the construction industry and the cost of building materials. It does not include items such as overheads, profit, interest charges or land development. The labour costs include insurance cover and the building material costs include V.A.T. Coverage: The type of construction covered is a typical 3 bed-roomed, 2 level local authority house and the index is applied on a national basis. Data Collection: The labour costs are based on agreed labour rates, allowances etc. The building material prices are collected at the beginning of each month from the same suppliers for the same representative basket. Calculation: Labour and material costs for the construction of a typical 3 bed-roomed house are weighted together to produce the index. Post October 2006: The name change from the House Building Cost Index to the House Construction Cost Index was introduced in October 2006 when the method of assessing the materials sub-index was changed from pricing a basket of materials (representative of a typical 2 storey 3 bedroomed local authority house) to the CSO Table 3 Wholesale Price Index. The new Index does maintains continuity with the old HBCI. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Oct 2008 data; Decrease due to a fall in the Oct Wholesale Price Index.

The effectiveness of nitrogen fertilizer in maximizing agricultural production and increasing economic returns for farmers has led to its widespread use. However, when this element is applied to a farming system, it can enter the surrounding environment via atmospheric, surface and leaching pathways. Consultants, extension agents, farmers, and other stakeholders need tools than can be used to quickly calculate the risk of nitrogen movement into the environment. The Nitrogen Index can assist users in making these assessments by integrating data on a series of management practices, weather conditions, soil characteristics and off-site factors. This tool has been tested using data from different agroecosystems across the United States, China, Mexico, Argentina, a Mediterranean region in Spain, and the Caribbean. It has performed well in comparing the effects of different management practices on nitrogen losses by distinguishing practices with high and very high risk levels from practices with medium, low and very low risk levels. Resources in this dataset:Resource Title: Nitrogen Index Tool - N Index 4.5. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=275&modecode=30-12-30-15 download page

Excel files using output from the LandSHIFT model to calculate changes in BII in India for the four scenarios and the base year 2010.

SCHOOL PROFICIENCY INDEXSummaryThe school proficiency index uses school-level data on the performance of 4th grade students on state exams to describe which neighborhoods have high-performing elementary schools nearby and which are near lower performing elementary schools. The school proficiency index is a function of the percent of 4th grade students proficient in reading (r) and math (m) on state test scores for up to three schools (i=1,2,3) within 1.5 miles of the block-group centroid. S denotes 4th grade school enrollment:Elementary schools are linked with block-groups based on a geographic mapping of attendance area zones from School Attendance Boundary Information System (SABINS), where available, or within-district proximity matches of up to the three-closest schools within 1.5 miles. In cases with multiple school matches, an enrollment-weighted score is calculated following the equation above. Please note that in this version of the data (AFFHT0004), there is no school proficiency data for jurisdictions in Kansas, West Virginia, and Puerto Rico because no data was reported for jurisdictions in these states in the Great Schools 2013-14 dataset. InterpretationValues are percentile ranked and range from 0 to 100. The higher the score, the higher the school system quality is in a neighborhood. Data Source: Great Schools (proficiency data, 2015-16); Common Core of Data (4th grade school addresses and enrollment, 2015-16); Maponics (attendance boundaries, 2016).Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 7.

To learn more about the School Proficiency Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive).

Spatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.

All indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').

Version 3.0.4 was added due to an error in how the Rx1day and Rx5day data were being handled for one of the West African data sources. More details can be found in the HadEX3 blog under 'Details/Docs' tab.

Additionally, an extension to HadEX3, comprising additional indices recommended by the WMO Expert Team on Sector-specific Climate Indices (ET-SCI), has been produced. These data are available in a separate dataset connected to this record, marked as supplemental to this dataset.

This table shows the evolution of the house price index since 2016. The index makes it possible to calculate the evolution of the prices of dwellings, subdivided into apartments, houses and villas. Historically, the General Administration of Heritage Documentation (AGDP) performed its calculations based on the first quarter of 2003 as the reference period. Since then, AGDP has had complete electronic data for these calculations. Price indices could not be calculated for 2015. Indeed, the mathematical model of the AGDP was based on the output of the Cadnet/Loco application. Cadnet/Loco was replaced by STIPAD in 2015. Therefore, the sales of which data were available were insufficient to calculate an index for the whole year. In view of the improvements allowed by the new STIPAD application, we took over the publication of the index from 2016 as the reference year (=100 %). Since 2022, we have also calculated the evolution of this index by Region.

This dataset contains gridded monthly Standardized Precipitation Index (SPI) at 10 timescales: 1-, 3-, 6-, 9-, 12-, 18-, 24-, 36-, 48-, and 60-month intervals from 1920 to 2012 at 250 m resolution for seven of the eight main Hawaiian Islands (18.849°N, 154.668°W to 22.269°N, 159.816°W; the island of Ni‘ihau is excluded due to lack of data). The gridded data use a World Geographic Coordinate System 1984 (WGS84) and are stored as individual GeoTIFF files for each month-year, organized by SPI interval, as indicated by the GeoTIFF file name. Thus, for example, the file “spi3_1999_11.tif” would contain the gridded 3-month SPI values calculated for the month of November in the year 1999. Currently, the data are available from 1920 to 2012, but the datasets will be updated as new gridded monthly rainfall data become available.SPI is a normalized drought index that converts monthly rainfall totals into the number of standard deviations (z-score) by which the observed, cumulative rainfall diverges from the long-term mean. The conversion of raw rainfall to a z-score is done by fitting a designated probability distribution function to the observed precipitation data for a site. In doing so, anomalous rainfall quantities take the form of positive and negative SPI z-scores. Additionally, because distribution fitting is based on long-term (>30 years) precipitation data at that location, SPI score is relative, making comparisons across different climates possible.The creation of a statewide Hawai‘i SPI dataset relied on a 93-year (1920-2012) high resolution (250 m) spatially interpolated monthly gridded rainfall dataset [1]. This dataset is recognized as the highest quality precipitation data available [2] for the main Hawaiian Islands. After performing extensive quality control on the monthly rainfall station data (including homogeneity testing of over 1,100 stations [1,3]) and a geostatistical method comparison, ordinary kriging was using to generate a time series of gridded monthly rainfall from January 1920 to December 2012 at 250 m resolution [3]. This dataset was then used to calculate monthly SPI for 10 timescales (1-, 3-, 6-, 9-, 12-, 18-, 24-, 36-, 48-, and 60-month) at each grid cell. A 3-month SPI in May 2001, for example, represents the March-April-May (MAM) total rainfall in 2001 compared to the MAM rainfall in the entire time series. The resolution of the gridded rainfall dataset provides a more precise representation of drought (and pluvial) events compared to the other available drought products.Frazier, A.G.; Giambelluca, T.W.; Diaz, H.F.; Needham, H.L. Comparison of geostatistical approaches to spatially interpolate month-year rainfall for the Hawaiian Islands. Int. J. Climatol. 2016, 36, 1459–1470, doi:10.1002/joc.4437.Giambelluca, T.W.; Chen, Q.; Frazier, A.G.; Price, J.P.; Chen, Y.-L.; Chu, P.-S.; Eischeid, J.K.; Delparte, D.M. Online Rainfall Atlas of Hawai‘i. B. Am. Meteorol. Soc. 2013, 94, 313–316, doi:10.1175/BAMS-D-11-00228.1.Frazier, A.G.; Giambelluca, T.W. Spatial trend analysis of Hawaiian rainfall from 1920 to 2012. Int. J. Climatol. 2017, 37, 2522–2531, doi:10.1002/joc.4862.

First launched by the U.S. Department of Housing and Urban Development (HUD) and Department of Transportation (DOT) in November 2013, the Location Affordability Index (LAI) provides ubiquitous, standardized household housing and transportation cost estimates for all 50 states and the District of Columbia. Because what is affordable is different for everyone, users can choose among eight household profiles—which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.

Version 3 updates the constituent data sets with 2012-2016 American Community Survey data and makes several methodological tweaks, most notably moving to modeling at the Census tract level rather at the block group. As with Version 2, the inputs to the simultaneous equation model (SEM) include six endogenous variables—housing costs, car ownership, and transit usage for both owners and renters—and 18 exogenous variables, with vehicle miles traveled still modeled separately due to data limitations.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2012-2016 Data Dictionary: DD_Location Affordability Indev v.3.0LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation

Data on the climate-related financial policy index (CRFPI) - comprising the global climate-related financial policies adopted globally and the bindingness of the policy - are provided for 74 countries from 2000 to 2020. The data include the index values from four statistical models used to calculate the composite index as described in D’Orazio and Thole 2022. The four alternative statistical approaches were designed to experiment with alternative weighting assumptions and illustrate how sensitive the proposed index is to changes in the steps followed to construct it. The index data shed light on countries’ engagement in climate-related financial planning and highlight policy gaps in relevant policy sectors.

[THIS DATASET HAS BEEN WITHDRAWN]. 5km gridded Standardised Precipitation Index (SPI) data for Great Britain, which is a drought index based on the probability of precipitation for a given accumulation period as defined by McKee et al. [1]. SPI is calculated for different accumulation periods: 1, 3, 6, 12, 18, 24 months. Each of these is in turn calculated for each of the twelve calendar months. Note that values in monthly (and for longer accumulation periods also annual) time series of the data therefore are likely to be autocorrelated. The standard period which was used to fit the gamma distribution is 1961-2010. The dataset covers the period from 1862 to 2015. NOTE: the difference between this dataset with the previously published dataset 'Gridded Standardized Precipitation Index (SPI) using gamma distribution with standard period 1961-2010 for Great Britain [SPIgamma61-10]" (Tanguy et al., 2015 [2]), apart from the temporal and spatial extent, is the underlying rainfall data from which SPI was calculated. In the previously published dataset, CEH-GEAR (Keller et al., 2015 [3], Tanguy et al., 2014 [4]) was used, whereas in this version, Met Office 5km rainfall grids were used (see supporting information for more details). The methodology to calculate SPI is the same in the two datasets. [1] McKee, T. B., Doesken, N. J., Kleist, J. (1993). The Relationship of Drought Frequency and Duration to Time Scales. Eighth Conference on Applied Climatology, 17-22 January 1993, Anaheim, California. [2] Tanguy, M.; Hannaford, J.; Barker, L.; Svensson, C.; Kral, F.; Fry, M. (2015). Gridded Standardized Precipitation Index (SPI) using gamma distribution with standard period 1961-2010 for Great Britain [SPIgamma61-10]. NERC Environmental Information Data Centre. https://doi.org/10.5285/94c9eaa3-a178-4de4-8905-dbfab03b69a0 [3] Keller, V. D. J., Tanguy, M., Prosdocimi, I., Terry, J. A., Hitt, O., Cole, S. J., Fry, M., Morris, D. G., and Dixon, H. (2015). CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological use, Earth Syst. Sci. Data Discuss., 8, 83-112, doi:10.5194/essdd-8-83-2015. [4] Tanguy, M.; Dixon, H.; Prosdocimi, I.; Morris, D. G.; Keller, V. D. J. (2014). Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2012) [CEH-GEAR]. NERC Environmental Information Data Centre. https://doi.org/10.5285/5dc179dc-f692-49ba-9326-a6893a503f6e Full details about this dataset can be found at https://doi.org/10.5285/ed7444fc-8c2a-473e-98cd-e68d3cffa2b0

ERA5–Drought is a global reconstruction of drought indices from 1940 to present. The dataset comprises two standardised drought indices: - the Standardised Precipitation Index (SPI) - the Standardised Precipitation-Evapotranspiration Index (SPEI). The SPI measures the precipitation deficit that accumulated over the preceding months and evaluates the deficit with respect to a reference period. The SPEI is an extension of the SPI and incorporates potential evapotranspiration to capture the impact of temperature on drought. SPI and SPEI values are in units of standard deviation from the standardised mean, i.e., negative values indicate drier-than-usual periods while positive values correspond to wetter-than-usual periods. Both indices can be used to identify the onset and the end of drought events as well as their severity. In ERA5–Drought, SPI and SPEI are calculated using precipitation and potential evapotranspiration from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5). ERA5 combines model data with observations from across the world to provide a globally complete and consistent description of the atmosphere. Drought indices are calculated for a range of accumulation windows (1/3/6/12/24/36/48 months) using the reference period from 1991–2020. All data is regridded to a regular grid of 0.25 degrees, making it suitable for many common applications. SPI and SPEI are calculated using both the ERA5 reanalysis (single realisation from the monthly means of daily means(moda) stream) and the ensemble of the reanalysis (10 realisations from the monthly means of daily means for ensemble members (edmo) stream), enabling uncertainty assessment of drought occurrence and intensity. The quality of the derived indices is evaluated using significance testing. The dataset currently covers 1940 to near-real time and is updated monthly. The consolidated data set is updated 2-3 months behind real time, while the intermediate data set is updated with 1 month of delay. New versions of the dataset are published as settings, such as the reference period, are updated or bug fixes are applied. Bug Fixes will be released using a minor revision (i.e. v1.1), while changes to the reference period will be released as major revisions (i.e. v2.0). Bug Fixes will be published to the Known Issues area on the Documentation tab. A more detailed description of the ERA5–Drought dataset and comparisons to other drought indices can be found in the associated dataset paper (see Documentation Tab). Information on access and usage examples, e.g. to calculate the area in drought, are provided in these guidelines. The dataset is produced by ECMWF.

This dataset presents the numerical fragility indicators calculated from the formulas developed by the Mednum. All the variables taken into account to calculate the scores are present in this dataset. Calculations are done via python’s Jupyter Notebook.

The endpoints selected for evaluation of the HIINT formula were percent relative liver weight of mice (PcLiv) and the logarithm of ALT [Log(ALT)], where the log transformation was used to help stabilize the increases in variance with dose found in the ALT dataset.

Time - It is the time stamp of the price

Unemployment data - The Employment Situation report is typically released on the third Friday after the conclusion of the reference week, i.e., the week which includes the 12th of the month. (Every Month)

CPI(consumer price index) - Currently, the consumer price index (CPI) is calculated by considering 299 items. The formula for calculating the CPI index is: (Cost of a fixed basket of goods and services in the current year/cost of a fixed basket of goods and services in the base year) * 100. (Released every month)

P/E(Price to Earning Ratio) - The ratio is used for valuing companies and for finding out whether they are overvalued or undervalued.

Open - The opening price of the price for the particular time frame

High - It is the highest price of the index in the particular time frame

Low - It is the lowest price of the index in the particular time frame

Close - It is the Closing price(The price at which the day or particular timeframe ended).

Industrial production index (IPI) - IPI measures levels of production and capacity in the manufacturing, mining, electric, and gas industries, relative to a base year.

The Digital Divide Index or DDI ranges in value from 0 to 100, where 100 indicates the highest digital divide. It is composed of two scores, also ranging from 0 to 100: the infrastructure/adoption (INFA) score and the socioeconomic (SE) score.The INFA score groups five variables related to broadband infrastructure and adoption: (1) percentage of total 2020 population without access to fixed broadband of at least 100 Mbps download and 20 Mbps upload as of 2020 based on Ookla Speedtest® open dataset; (2) percent of homes without a computing device (desktops, laptops, smartphones, tablets, etc.); (3) percent of homes with no internet access (have no internet subscription, including cellular data plans or dial-up); (4) median maximum advertised download speeds; and (5) median maximum advertised upload speeds.The SE score groups five variables known to impact technology adoption: (1) percent population ages 65 and over; (2) percent population 25 and over with less than high school; (3) individual poverty rate; (4) percent of noninstitutionalized civilian population with a disability: and (5) a brand new digital inequality or internet income ratio measure (IIR). In other words, these variables indirectly measure adoption since they are potential predictors of lagging technology adoption or reinforcing existing inequalities that also affect adoption.These two scores are combined to calculate the overall DDI score. If a particular county or census tract has a higher INFA score versus a SE score, efforts should be made to improve broadband infrastructure. If on the other hand, a particular geography has a higher SE score versus an INFA score, efforts should be made to increase digital literacy and exposure to the technology’s benefits.The DDI measures primarily physical access/adoption and socioeconomic characteristics that may limit motivation, skills, and usage. Due to data limitations it was designed as a descriptive and pragmatic tool and is not intended to be comprehensive. Rather it should help initiate important discussions among community leaders and residents.Data for the digital divide index (DDI) was compiled by Purdue Center for Regional Development and obtained from the 5-year American Community Survey (ACS) and Ookla Speedtest® open dataset.