Home Irish DatasetTacar Sonraí IrisHigh-Quality Irish General Conversation Dataset for AI & Speech Models Contact Us OverviewTitleIrish Language DatasetDataset TypeGeneral ConversationDescriptionUnscripted telephonic conversation between two people. Approx. Audio Duration (Range)…

Datafiles contain over 3 million simulated noisy FRET spectra which can be used for validating FRET analysis approaches. The same data is formatted for both matlab and python. The matlab file can be read in with each of the components as a separate variable. The python numpy array can be read in and then converted to a dictionary containing each component using numpy.load(‘YourFilepath’).item().Matlab Variable/Python Dictionary Entry: ’Simulated Pixels’: These are simulated noisy spectra covering a range of SNR and FRET efficiencies, organized in the

        following manner: Simulated_Pixels[N,Power,Efficiency,Excitation,Emission], where N are repeat simulations to calculate

statistics with (1000 simulations for every condition). Has an overall shape of (1000, 150, 11, 2, 32).

        ’sRET luxFRET Calibration Spectra’: These are noiseless calibration spectra organized in the following manner: sRET luxFRET Calibration Spectra[Power,Donor or Acceptor,Excitation,Emission], with an overall shape of (150, 2, 2, 32).

These spectra can also be used to calculate the normalized emission spectra and gamma parameter needed for sensorFRET, but we also included those values separately for convenience.

        ’Power Vector’: The vector relating the indices in the 2nd dimension of ‘Simulated Pixels’ to the simulated power used

ranging from 0.1-1000 (arbitrary units) in 150 logarithmic steps to change the SNR and provide normalized residuals in the approximate range of 0.001 to 0.1.

        ’Efficiency Vector’: The vector relating the indices in the 3rd dimension of ‘Simulated Pixels’ to the simulated FRET

efficiency ranging from 0 to 1 in 11 linear steps.

        ’Excitation Wavelength Vector’: The vector relating the indices of the 4th dimension of ‘Simulated Pixels’ to the simulated

Excitation wavelength, either 405 or 458.

        ’Emission Wavelength Vector’: The vector relating the indices of the 5th dimension of ‘Simulated Pixels’ to the simulated

Emission Wavelengths, ranging from 416 to 718 in 32 linear steps to match the spectral resolution of our experiments.

        ’Normalized Emission Spectra’:An array containing the normalized emission shapes for the Cerulean and Venus fluorophores (shape of (2, 32)).






        ’Gamma’: the sensorFRET gamma parameter for the Cerulean/Venus-405/458 pairing, 0.0605 (from experiment) ’Qd’: the quantum efficiency of Cerulean, 0.62.

’Qa’: the quantum efficiency of Venus, 0.57.

description: The U.S. Geological Survey developed this dataset as part of the Colorado Front Range Infrastructure Resources Project (FRIRP). One goal of the FRIRP was to provide information on the availability of those hydrogeologic resources that are either critical to maintaining infrastructure along the northern Front Range or that may become less available because of urban expansion in the northern Front Range. This dataset extends from the Boulder-Jefferson County line on the south, to the middle of Larimer and Weld Counties on the North. On the west, this dataset is bounded by the approximate mountain front of the Front Range of the Rocky Mountains; on the east, by an arbitrary north-south line extending through a point about 6.5 kilometers east of Greeley. This digital geospatial dataset consists of saturated-thickness polygons that were generated with a Geographic Information System (GIS).; abstract: The U.S. Geological Survey developed this dataset as part of the Colorado Front Range Infrastructure Resources Project (FRIRP). One goal of the FRIRP was to provide information on the availability of those hydrogeologic resources that are either critical to maintaining infrastructure along the northern Front Range or that may become less available because of urban expansion in the northern Front Range. This dataset extends from the Boulder-Jefferson County line on the south, to the middle of Larimer and Weld Counties on the North. On the west, this dataset is bounded by the approximate mountain front of the Front Range of the Rocky Mountains; on the east, by an arbitrary north-south line extending through a point about 6.5 kilometers east of Greeley. This digital geospatial dataset consists of saturated-thickness polygons that were generated with a Geographic Information System (GIS).

This dataset provides processed and normalized/standardized indices for the management tool 'Business Process Reengineering' (BPR). Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding BPR dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "business process reengineering" + "process reengineering" + "reengineering management". Processing: None. The dataset utilizes the original Google Trends index, which is base-100 normalized against the peak search interest for the specified terms and period. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Reengineering + Business Process Reengineering + Process Reengineering. Processing: The annual relative frequency series was normalized by setting the year with the maximum value to 100 and scaling all other values (years) proportionally. Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching BPR-related keywords [("business process reengineering" OR ...) AND ("management" OR ...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly publication counts in Crossref. Data deduplicated via DOIs. Processing: For each month, the relative share of BPR-related publications (BPR Count / Total Crossref Count for that month) was calculated. This monthly relative share series was then normalized by setting the month with the maximum relative share to 100 and scaling all other months proportionally. Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Reengineering (1993, 1996, 2000, 2002); Business Process Reengineering (2004, 2006, 2008, 2010, 2012, 2014, 2017, 2022). Processing: Semantic Grouping: Data points for "Reengineering" and "Business Process Reengineering" were treated as a single conceptual series for BPR. Normalization: The combined series of original usability percentages was normalized relative to its own highest observed historical value across all included years (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Reengineering (1993, 1996, 2000, 2002); Business Process Reengineering (2004, 2006, 2008, 2010, 2012, 2014, 2017, 2022). Processing: Semantic Grouping: Data points for "Reengineering" and "Business Process Reengineering" were treated as a single conceptual series for BPR. Standardization (Z-scores): Original scores (X) were standardized using Z = (X - ?) / ?, with a theoretically defined neutral mean ?=3.0 and an estimated pooled population standard deviation ??0.891609 (calculated across all tools/years relative to ?=3.0). Index Scale Transformation: Z-scores were transformed to an intuitive index via: Index = 50 + (Z * 22). This scale centers theoretical neutrality (original score: 3.0) at 50 and maps the approximate range [1, 5] to [?1, ?100]. Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding BPR dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

Aim: Climate change is altering habitat suitability for many organisms and modifying species ranges at a global scale. Here we explored the impact of climate change on 112 pine species (Pinus), fundamental elements of Northern terrestrial ecosystems. Location: Global. Methods: We applied a novel methodology for species distribution modelling that considers uncertainty in climatic projections and taxon sampling, and incorporates elements of species’ recent evolutionary history. We based our niche calculations on climate and soil data and computed projections across multiple algorithms and IPCC scenarios, which were ensembled into one single suitability map. We then used phylogenetic methods to account for recent evolution in climatic requirements by estimating the evolution of climatic niche. Edaphoclimatic and evolutionary analyses were then combined to calibrate the projections in areas showing high uncertainty. We validated our models using naturalized occurrences of invasive pine species. Results: Our models predicted that by 2070 most pine species (58%) might face important reductions of habitat suitability, potentially leading to range losses and a decrease in species richness, particularly in some regions such as the Mediterranean Basin and South North America, albeit migration might mitigate these shifts in some cases. In contrast, our projections showed increased habitat suitability for approx. 20% of species, which may undergo range expansions under climate change. Moreover, the consideration of recent evolutionary trends modified projected scenarios, decreasing range loss and increasing range expansion for some species. The independent validation endorsed our models for many species and the influence of recent evolution in some cases. Conclusions: We predict that climate change will impose drastic changes in pine distribution and diversity across biogeographical regions, but the magnitude and direction of change will vary significantly across regions and taxa. Species-level responses are likely to be influenced by regional conditions and the recent evolutionary history of each taxon.

HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) provides hydrographic information in a consistent and comprehensive format for regional and global-scale applications. HydroSHEDS offers a suite of geo-referenced data sets (vector and raster), including stream networks, watershed boundaries, drainage directions, and ancillary data layers such as flow accumulations, distances, and river topology information. HydroSHEDS is derived from elevation data of the Shuttle Radar Topography Mission (SRTM) at 3 arc-second resolution. Available HydroSHEDS resolutions range from 3 arc-second (approx. 90 meters at the equator) to 5 minute (approx. 10 km at the equator) with seamless near-global extent.

Citation:Title: HydroSHEDS (RIV) - River network (stream lines) at 15s resolution - AfricaCredits: World Wildlife Fund (WWF)Publication Date: 2006Publisher: U.S. Geological SurveyOnline Linkages: http://hydrosheds.cr.usgs.govhttp://www.worldwildlife.org/hydroshedsOther Citation Info: Please cite HydroSHEDS as: Lehner, B., Verdin, K., Jarvis, A. (2006): HydroSHEDS Technical Documentation. World Wildlife Fund US, Washington, DC. Available at http://hydrosheds.cr.usgs.gov.

This layer package was loaded using Data Basin.Click here to go to the detail page for this layer package in Data Basin, where you can find out more information, such as full metadata, or use it to create a live web map.

Overview: ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.

Processing steps: The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically: 1. spatially aggregate CHELSA to the resolution of ERA5-Land 2. calculate difference of ERA5-Land - aggregated CHELSA 3. interpolate differences with a Gaussian filter to 30 arc seconds. 4. add the interpolated differences to CHELSA

Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.

Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):

maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))

actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))

relative humidity = actual water pressure / maximum water pressure

The resulting relative humidity has been aggregated to decadal averages. Each month is divided into three decades: the first decade of a month covers days 1-10, the second decade covers days 11-20, and the third decade covers days 21-last day of the month.

Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].

File naming scheme (YYYY = year; MM = month; dD = number of decade): ERA5_land_rh2m_avg_decadal_YYYY_MM_dD.tif

Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent: north: 82:00:30N south: 18N west: 32:00:30W east: 70E

Spatial resolution: 30 arc seconds (approx. 1000 m)

Temporal resolution: Decadal

Pixel values: Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)

Software used: GDAL 3.2.2 and GRASS GIS 8.0.0

Original ERA5-Land dataset license: https://apps.ecmwf.int/datasets/licences/copernicus/

CHELSA climatologies (V1.2): Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4 Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Processed by: mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC

Data is also available in EU LAEA (EPSG: 3035) projection: https://zenodo.org/record/7427010

This dataset provides processed and normalized/standardized indices for the management activity 'Mergers and Acquisitions' (M&A). Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding M&A dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "mergers and acquisitions" + "mergers and acquisitions corporate". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Mergers and Acquisitions + Mergers & Acquisitions. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching M&A-related keywords [("mergers and acquisitions" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (M&A Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Mergers and Acquisitions (2006, 2008, 2010, 2012, 2014, 2017). Note: Not reported before 2006 or after 2017. Processing: Normalization: Original usability percentages normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Mergers and Acquisitions (2006-2017). Note: Not reported before 2006 or after 2017. Processing: Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding M&A dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

The High Plains aquifer extends from about 32 degrees to almost 44 degrees north latitude and from about 96 degrees 30 minutes to 106 degrees west longitude. The aquifer underlies about 175,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. This digital dataset consists of three sets of water-level measurements. The first set are the supplemental water-level measurements for 547 wells screened in the High Plains aquifer, not located in New Mexico, measured in predevelopment and at least once for 2015 through 2018, but not for 2019. These supplemental measurements were used to calculate historical water-level change values for predevelopment to 2015 to 2018 and approximate water-level change values from predevelopment to 2019 to substantiate the map of water-level changes, predevelopment (about 1950) to 2019 (figure 1 in https://doi.org/10.3133/sir20235143). The water-level measurements used to calculate historical water-level changes from predevelopment are (1) 218 wells measured in predevelopment and in 2018, but not measured in 2019, which are used to calculate water-level change, predevelopment to 2018, (2) 152 wells measured in predevelopment and in 2017, but not measured in 2018 or 2019, which are used to calculate water-level change, predevelopment to 2017, (3) 117 wells measured in predevelopment and in 2016, but not measured in 2017, 2018, or 2019, which are used to calculate water-level change, predevelopment to 2016, and (4) 60 wells measured in predevelopment and in 2015, but not measured in 2016, 2017, 2018, or 2019, which are used to calculate water-level change, predevelopment to 2015. The second and third sets of water-level measurements were used to approximate water-level change, predevelopment to 2019, but did not have predevelopment water-level measurements. The second set included 292 wells, which were located in areas where water level declines from predevelopment to 1980 were 50 feet or more (Luckey and others, 1981; Cederstrand and Becker, 1999) and were measured in 1980 and in 2019, but not measured in the predevelopment period. For these wells, approximate water-level changes, predevelopment to 2019, were calculated as the starting value of the polygon range (for example 50 ft for the polygon of declines from 50 to 75 ft) from the map of water-level change, predevelopment to 1980, plus measured water-level change from 1980 to 2019. The third set of water-level measurements used to calculate approximate water-level changes were from 1,213 wells that were measured on or before 6/15/1978 (termed post-development) and in 2019, but not in the predevelopment period. For these wells, approximate water-level changes, predevelopment to 2019, were calculated as the water level, 2019, minus water level, post-development.

HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) provides hydrographic information in a consistent and comprehensive format for regional and global-scale applications. HydroSHEDS offers a suite of geo-referenced data sets (vector and raster), including stream networks, watershed boundaries, drainage directions, and ancillary data layers such as flow accumulations, distances, and river topology information. HydroSHEDS is derived from elevation data of the Shuttle Radar Topography Mission (SRTM) at 3 arc-second resolution. Available HydroSHEDS resolutions range from 3 arc-second (approx. 90 meters at the equator) to 5 minute (approx. 10 km at the equator) with seamless near-global extent.

Citation:Title: HydroSHEDS (BAS) - Africa drainage basins (watershed boundaries) at 30s resolutionCredits: World Wildlife Fund (WWF)Publication Date: 2006Publisher: U.S. Geological SurveyOnline Linkages: http://hydrosheds.cr.usgs.govhttp://www.worldwildlife.org/hydroshedsOther Citation Info: Please cite HydroSHEDS as: Lehner, B., Verdin, K., Jarvis, A. (2006): HydroSHEDS Technical Documentation. World Wildlife Fund US, Washington, DC. Available at http://hydrosheds.cr.usgs.gov.

This layer package was loaded using Data Basin.Click here to go to the detail page for this layer package in Data Basin, where you can find out more information, such as full metadata, or use it to create a live web map.

Overview:
ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.

Processing steps:
The original hourly ERA5-Land air temperature 2 m above ground and dewpoint temperature 2 m data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:
1. spatially aggregate CHELSA to the resolution of ERA5-Land
2. calculate difference of ERA5-Land - aggregated CHELSA
3. interpolate differences with a Gaussian filter to 30 arc seconds
4. add the interpolated differences to CHELSA

Subsequently, the temperature time series have been aggregated on a daily basis. From these, daily relative humidity has been calculated for the time period 01/2000 - 07/2021.

Relative humidity (rh2m) has been calculated from air temperature 2 m above ground (Ta) and dewpoint temperature 2 m above ground (Td) using the formula for saturated water pressure from Wright (1997):

maximum water pressure = 611.21 * exp(17.502 * Ta / (240.97 + Ta))

actual water pressure = 611.21 * exp(17.502 * Td / (240.97 + Td))

relative humidity = actual water pressure / maximum water pressure

Data provided is the daily averages of relative humidity. This set provides data for the years 2000 - 2004. For other time periods, please see further linked data sets.

Resultant values have been converted to represent percent * 10, thus covering a theoretical range of [0, 1000].

File naming scheme (YYYY = year; MM = month; DD = day):
ERA5_land_rh2m_avg_daily_YYYYMMDD.tif

Projection + EPSG code:
Latitude-Longitude/WGS84 (EPSG: 4326)

Spatial extent:
north: 82:00:30N
south: 18N
west: 32:00:30W
east: 70E

Spatial resolution:
30 arc seconds (approx. 1000 m)

Temporal resolution:
Daily

Pixel values:
Percent * 10 (scaled to Integer; example: value 738 = 73.8 %)

Software used:
GDAL 3.2.2 and GRASS GIS 8.0.0

Original ERA5-Land dataset license:
https://apps.ecmwf.int/datasets/licences/copernicus/

CHELSA climatologies (V1.2):
Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. http://dx.doi.org/doi:10.5061/dryad.kd1d4
Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. https://doi.org/10.1038/sdata.2017.122

Processed by:
mundialis GmbH & Co. KG, Germany (https://www.mundialis.de/)

Reference: Wright, J.M. (1997): Federal meteorological handbook no. 3 (FCM-H3-1997). Office of Federal Coordinator for Meteorological Services and Supporting Research. Washington, DC

Data is also available in EU LAEA (EPSG: 3035) projection: https://zenodo.org/record/7434396

Flow cytometry

Modality / instrument: Flow cytometer (CytoFLEX LX, Beckman Coulter)

File format: FCS + XIT (CytExpert, Beckman Coulter).

Samples and acquisitions:

Fluorescent fusions in live Chinese Hamster ovary (CHO) cells.

File

Cell line

Runs

Cells counted

CONTROL.fcs

CHO wild-type

1

7000

GFP-CCR5.fcs

CHO-GFP-CCR5

1

7000

Exp_20220916_1_GFP.xit

N/A - metadata

Approx. size 6 MB

PaTCH microscopy images

Imaging modality / instrument: Brightfield + PaTCH fluorescence microscopy

Image format: OME TIFF (16 bit) + MicroManager metadata files

Microscope settings:

488 nm triggered excitation; split red/green detection, cropped to green (GFP) channel only; 10 ms/frame laser exposure; 13.5 ms/frame-to-frame; 53 nm/px. Photometrics Prime95b CMOS.

Samples and acquisitions:

Fluorescent fusions of GFP-CCR5 receptor in live CHO cells imaged with and without 100 nM CCL5 ligand. Each subfolder corresponds to a field of view and contains one brightfield and one PaTCH acquisition of the same cell.

Folder

Condition

Fields of view

AC6 CONTROL sc

CCL5-

11

AC6 CCL5 sc

CCL5+ (100 nM)

10

Approx. size before compression: 14 GB

Structured illumination microscopy - volumetric stacks

Imaging modality / instrument: SIM fluorescence microscopy (custom setup at NPL based on Olympus IX71)

Image format: OME TIFF (16 bit) with intrinsic metadata (voxel size)

Microscope settings: 638 nm excitation; 60x/1.3 NA; Flash 4.0, Hamamatsu Photonics. For additional details see the reference below (Hunter et al, bioRxiv).

Samples and acquisitions:

Dylight 650-MC-5 labeled CCR5 receptor in fixed CHO-CCR5 cells, imaged with and without 100 nM CCL5 ligand. Each acquisition is of a unique field of view and contains one SIM reconstruction as an XYZ volumetric stack. ‘Basal membrane’ acquisitions consist of 5 slices at 200 nm z-intervals across the range of the basal membrane. ‘Whole cell' acquisitions are made up of 7 slices with 500 nm z-interval ranging from just below the basal membrane to just above the apical membrane.

    Folder
    Subfolder/condition
    Fields of view


    Basal membrane
    CCL5-
    5


    Basal membrane
    CCL5+ (100 nM)
    6


    Whole cells
    CCL5-
    5


    Whole cells
    CCL5+ (100 nM)
    8

Approx. size before compression: 300 MB

This dataset provides processed and normalized/standardized indices for the management tool 'Benchmarking'. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Benchmarking dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "benchmarking" + "benchmarking management". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Benchmarking. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Benchmarking-related keywords ["benchmarking" AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Benchmarking Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Benchmarking (1993, 1996, 1999, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2017). Note: Not reported in 2022 survey data. Processing: Normalization: Original usability percentages normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Benchmarking (1993-2017). Note: Not reported in 2022 survey data. Processing: Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Benchmarking dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

This dataset provides processed and normalized/standardized indices for the management tool group focused on approaches like 'Collaborative Innovation', 'Open Innovation', and 'Design Thinking'. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Collaborative Innovation dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "design thinking" + "open innovation" + "design thinking innovation" + "open innovation process". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Design Thinking + Open Innovation + Collaborative Innovation + Market Innovation + Crowdsourcing Innovation. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Innovation-related keywords [("design thinking" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Innovation Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Open-Market Innovation (2004); Collaborative Innovation (2006, 2008); Open Innovation (2010, 2012); Design Thinking (2022). Note gaps in reporting (2013-2021). Processing: Semantic Grouping: Data points across related names treated as a single conceptual series. Normalization: Combined series normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years (same names/years as Usability). Note gaps in reporting (2013-2021). Processing: Semantic Grouping: Data points treated as a single conceptual series. Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Collaborative Innovation dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

This dataset provides processed and normalized/standardized indices for the management tool group 'Strategic Planning', including related concepts like Dynamic Strategic Planning and Strategic Management. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Strategic Planning dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "strategic planning" + "strategic management" + "strategic planning process". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Strategic Planning + Dynamic Strategic Planning + Strategic Budgeting + Strategic Thinking + Strategic Management. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Strategic Planning-related keywords [("strategic planning" OR ...) AND (...) OR "strategic thinking" - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Strategic Planning Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Strategic Planning (1996, 1999, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2017); Dynamic Strategic Planning and Budgeting (2022). Processing: Semantic Grouping: Data points across the different naming conventions were treated as a single conceptual series. Normalization: Combined series normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Strategic Planning (1996-2017); Dynamic Strategic Planning and Budgeting (2022). Processing: Semantic Grouping: Data points treated as a single conceptual series. Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Strategic Planning dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

This dataset provides processed and normalized/standardized indices for the management tool group focused on 'Price Optimization', including related concepts like Dynamic Pricing and Price Optimization Models. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding Price Optimization dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "price optimization" + "dynamic pricing" + "price optimization strategy". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Price Optimization + Pricing Optimization + Dynamic Pricing Models + Optimal Pricing + Dynamic Pricing. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching Price Optimization-related keywords [("price optimization" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (Price Opt. Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years: Price Optimization Models (2004, 2008, 2010, 2012, 2014, 2017). Note: Not reported before 2004 or after 2017. Processing: Normalization: Original usability percentages normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years: Price Optimization Models (2004-2017). Note: Not reported before 2004 or after 2017. Processing: Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding Price Optimization dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.

This dataset provides processed and normalized/standardized indices for the management tool group focused on 'Customer Experience Management' (CEM) and 'Customer Relationship Management' (CRM), including related concepts like Customer Satisfaction. Derived from five distinct raw data sources, these indices are specifically designed for comparative longitudinal analysis, enabling the examination of trends and relationships across different empirical domains (web search, literature, academic publishing, and executive adoption). The data presented here represent transformed versions of the original source data, aimed at achieving metric comparability. Users requiring the unprocessed source data should consult the corresponding CEM/CRM dataset in the Management Tool Source Data (Raw Extracts) Dataverse. Data Files and Processing Methodologies: Google Trends File (Prefix: GT_): Normalized Relative Search Interest (RSI) Input Data: Native monthly RSI values from Google Trends (Jan 2004 - Jan 2025) for the query "customer relationship management" + "customer experience management" + "customer satisfaction". Processing: None. Utilizes the original base-100 normalized Google Trends index. Output Metric: Monthly Normalized RSI (Base 100). Frequency: Monthly. Google Books Ngram Viewer File (Prefix: GB_): Normalized Relative Frequency Input Data: Annual relative frequency values from Google Books Ngram Viewer (1950-2022, English corpus, no smoothing) for the query Customer Relationship Management+Customer Experience Management+Customer Satisfaction Measurement+Customer Satisfaction. Processing: Annual relative frequency series normalized (peak year = 100). Output Metric: Annual Normalized Relative Frequency Index (Base 100). Frequency: Annual. Crossref.org File (Prefix: CR_): Normalized Relative Publication Share Index Input Data: Absolute monthly publication counts matching CEM/CRM-related keywords [("customer relationship management" OR ...) AND (...) - see raw data for full query] in titles/abstracts (1950-2025), alongside total monthly Crossref publications. Deduplicated via DOIs. Processing: Monthly relative share calculated (CEM/CRM Count / Total Count). Monthly relative share series normalized (peak month's share = 100). Output Metric: Monthly Normalized Relative Publication Share Index (Base 100). Frequency: Monthly. Bain & Co. Survey - Usability File (Prefix: BU_): Normalized Usability Index Input Data: Original usability percentages (%) from Bain surveys for specific years under various related names: Customer Satisfaction Surveys (1993); Customer Satisfaction (1996); Customer Satisfaction Measurement (1999, 2000); Customer Relationship Management (2002, 2006, 2008, 2010, 2012, 2017); CRM (2004, 2014); Customer Experience Management (2022). Processing: Semantic Grouping: Data points across all related names were treated as a single conceptual series representing CEM/CRM evolution. Normalization: Combined series normalized relative to its historical peak (Max % = 100). Output Metric: Biennial Estimated Normalized Usability Index (Base 100 relative to historical peak). Frequency: Biennial (Approx.). Bain & Co. Survey - Satisfaction File (Prefix: BS_): Standardized Satisfaction Index Input Data: Original average satisfaction scores (1-5 scale) from Bain surveys for specific years under various related names (same as Usability: CSS, CS, CSM, CRM, CEM). Processing: Semantic Grouping: Data points treated as a single conceptual series. Standardization (Z-scores): Using Z = (X - 3.0) / 0.891609. Index Scale Transformation: Index = 50 + (Z * 22). Output Metric: Biennial Standardized Satisfaction Index (Center=50, Range?[1,100]). Frequency: Biennial (Approx.). File Naming Convention: Files generally follow the pattern: PREFIX_Tool_Processed.csv or similar, where the PREFIX indicates the data source (GT_, GB_, CR_, BU_, BS_). Consult the parent Dataverse description (Management Tool Comparative Indices) for general context and the methodological disclaimer. For original extraction details (specific keywords, URLs, etc.), refer to the corresponding CEM/CRM dataset in the Raw Extracts Dataverse. Comprehensive project documentation provides full details on all processing steps.