cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/c1h8-kf39
title: modified CASIA

description: The modified CASIA dataset is created for research topics like: percetual image hash, image tampering detection, user-device physical unclonable function and so on. The ground truth images are taken from the CASIA image tampering detection evaluation (ITDE) v1.0 database (J.Dong, et. al, CASIA image tampering detection evaluation database, ChinaSIP2013), which contains images from eight categories (animal, architecture, article, character, nature, plant, scene and texture) of size 384X256 or 256X384. Instead of directly using the tampered image set from CASIA ITDE v1.0, the tampered versions of those authentic images are selected from CASIA ITDE V2.0, which are more challenging and comprehensive since it considers post-processing like blurring or filtering over the tampered regions to make the tampered images appear realistic to human eyes. For one authentic image, there may be several tampered versions in the CASIA ITDE v2.0 dataset. To increase the diversity, only one tampered version is kept for each authentic image. As a result, the modified CASIA database contains 400 (8 categories X 50 per category) authentic images and their corresponding tampered versions. According to CASIA ITDE v2.0, the tampered images are generated using crop-and-paste operation under Adobe Photoshop on the authentic images, and the tampered regions may have random shapes and different sizes, rotations or distortions. In order to evaluate the proposed system performance over content-preserving manipulations, we enrich the modified CASIA dataset by adding content-preserving manipulations to the authentic images using Matlab and ImageJ. Common image processing techniques like rotation, scaling, filtering and JPEG compression, and unavoidable process/transmission noises like Gaussian, Salt&Pepper and speckle noise are considered. Furthermore, the abovementioned content-preserving manipulations are also applied to the tampered dataset to evaluate if their combination can evade detection. As a result, the modified CASIA database D contains: 1) D_au: 400 authentic images in 8 categories, each with 50 images;2) D_tampered: 400 tampered images corresponding to the authentic ones from D_au;3) D_au_cp: 3600 (400x 9) images generated by adding a single content-preserving manipulation (9 types: Gaussian noise, salt&pepper noise, speckle noise, Gaussian filter, motion blur, JPEG compression, gamma correction, rotation and scaling) to every image of D_au;4) D_tampered_cp: 3600 (400x9) tampered images by applying those 9 content-preserving manipulations listed in 3) to the images of Dtampered. To use the dataset, please cite: [1] Y. Zheng, Y. Cao and C. Chang, "A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification," in IEEE Transactions on Information Forensics and Security, vol. 15, pp. 620-634, 2020.[2]J. Dong, W. Wang and T. Tan, "CASIA Image Tampering Detection Evaluation Database," 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, 2013, pp. 422-426.
DOI: 10.21227/cte6-2846
title: modified CASIA

description: The modified CASIA dataset is created for research topics like: percetual image hash, image tampering detection, user-device physical unclonable function and so on. The ground truth images are taken from the CASIA image tampering detection evaluation (ITDE) v1.0 database (J.Dong, et. al, CASIA image tampering detection evaluation database, ChinaSIP2013), which contains images from eight categories (animal, architecture, article, character, nature, plant, scene and texture) of size 384X256 or 256X384. Instead of directly using the tampered image set from CASIA ITDE v1.0, the tampered versions of those authentic images are selected from CASIA ITDE V2.0, which are more challenging and comprehensive since it considers post-processing like blurring or filtering over the tampered regions to make the tampered images appear realistic to human eyes. For one authentic image, there may be several tampered versions in the CASIA ITDE v2.0 dataset. To increase the diversity, only one tampered version is kept for each authentic image. As a result, the modified CASIA database contains 400 (8 categories X 50 per category) authentic images and their corresponding tampered versions. According to CASIA ITDE v2.0, the tampered images are generated using crop-and-paste operation under Adobe Photoshop on the authentic images, and the tampered regions may have random shapes and different sizes, rotations or distortions. In order to evaluate the proposed system performance over content-preserving manipulations, we enrich the modified CASIA dataset by adding content-preserving manipulations to the authentic images using Matlab and ImageJ. Common image processing techniques like rotation, scaling, filtering and JPEG compression, and unavoidable process/transmission noises like Gaussian, Salt&Pepper and speckle noise are considered. Furthermore, the abovementioned content-preserving manipulations are also applied to the tampered dataset to evaluate if their combination can evade detection. As a result, the modified CASIA database D contains: 1) D_au: 400 authentic images in 8 categories, each with 50 images;2) D_tampered: 400 tampered images corresponding to the authentic ones from D_au;3) D_au_cp: 3600 (400x 9) images generated by adding a single content-preserving manipulation (9 types: Gaussian noise, salt&pepper noise, speckle noise, Gaussian filter, motion blur, JPEG compression, gamma correction, rotation and scaling) to every image of D_au;4) D_tampered_cp: 3600 (400x9) tampered images by applying those 9 content-preserving manipulations listed in 3) to the images of Dtampered. To use the dataset, please cite: [1] Y. Zheng, Y. Cao and C. Chang, "A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification," in IEEE Transactions on Information Forensics and Security, vol. 15, pp. 620-634, 2020.[2]J. Dong, W. Wang and T. Tan, "CASIA Image Tampering Detection Evaluation Database," 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, 2013, pp. 422-426.
DOI: 10.17171/1-13-34266-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 90thPercentile SURFACE-PRESSURE

description: Climate Models
DOI: 10.17171/1-13-34267-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 90thPercentile TOTAL-CLOUD-COVER

description: Climate Models
DOI: 10.17171/1-13-34268-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 90thPercentile TOTAL-PRECIPITATION

description: Climate Models
DOI: 10.17171/1-13-34269-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile 10M-WIND-DIRECTION

description: Climate Models
DOI: 10.17171/1-13-34270-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile 10M-WINDSPEED

description: Climate Models
DOI: 10.17171/1-13-34271-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile 2M-DEW-POINT-TEMPERATURE

description: Climate Models
DOI: 10.17171/1-13-34272-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile 2M-RELATIVE-HUMIDITY

description: Climate Models
DOI: 10.17171/1-13-34273-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile 2M-TEMPERATURE

description: Climate Models
DOI: 10.21227/p7ca-9p45
title: modified CASIA

description: The modified CASIA dataset is created for research topics like: percetual image hash, image tampering detection, user-device physical unclonable function and so on. The ground truth images are taken from the CASIA image tampering detection evaluation (ITDE) v1.0 database (J.Dong, et. al, CASIA image tampering detection evaluation database, ChinaSIP2013), which contains images from eight categories (animal, architecture, article, character, nature, plant, scene and texture) of size 384X256 or 256X384. Instead of directly using the tampered image set from CASIA ITDE v1.0, the tampered versions of those authentic images are selected from CASIA ITDE V2.0, which are more challenging and comprehensive since it considers post-processing like blurring or filtering over the tampered regions to make the tampered images appear realistic to human eyes. For one authentic image, there may be several tampered versions in the CASIA ITDE v2.0 dataset. To increase the diversity, only one tampered version is kept for each authentic image. As a result, the modified CASIA database contains 400 (8 categories X 50 per category) authentic images and their corresponding tampered versions.According to CASIA ITDE v2.0, the tampered images are generated using crop-and-paste operation under Adobe Photoshop on the authentic images, and the tampered regions may have random shapes and different sizes, rotations or distortions. In order to evaluate the proposed system performance over content-preserving manipulations, we enrich the modified CASIA dataset by adding content-preserving manipulations to the authentic images using Matlab and ImageJ. Common image processing techniques like rotation, scaling, filtering and JPEG compression, and unavoidable process/transmission noises like Gaussian, Salt&Pepper and speckle noise are considered. Furthermore, the abovementioned content-preserving manipulations are also applied to the tampered dataset to evaluate if their combination can evade detection. As a result, the modified CASIA database D contains:1) D_au: 400 authentic images in 8 categories, each with 50 images;2) D_tampered: 400 tampered images corresponding to the authentic ones from D_au;3) D_au_cp: 3600 (400x 9) images generated by adding a single content-preserving manipulation (9 types: Gaussian noise, salt&pepper noise, speckle noise, Gaussian filter, motion blur, JPEG compression, gamma correction, rotation and scaling) to every image of D_au;4) D_tampered_cp: 3600 (400x9) tampered images by applying those 9 content-preserving manipulations listed in 3) to the images of Dtampered.To use the dataset, please cite: [1] Y. Zheng, Y. Cao and C. Chang, "A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification," in IEEE Transactions on Information Forensics and Security, vol. 15, pp. 620-634, 2020.[2]J. Dong, W. Wang and T. Tan, "CASIA Image Tampering Detection Evaluation Database," 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, 2013, pp. 422-426.
DOI: 10.17171/1-13-34274-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile EVAPORATION

description: Climate Models
DOI: 10.17171/1-13-34275-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile ICE-COVER

description: Climate Models
DOI: 10.17171/1-13-34276-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile LATENT-HEAT-FLUX

description: Climate Models
DOI: 10.17171/1-13-34277-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile MEAN-SEA-LEVEL-PRESSURE

description: Climate Models
DOI: 10.17171/1-13-34278-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile SENSIBLE-HEAT-FLUX

description: Climate Models
DOI: 10.17171/1-13-34279-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile SNOW-DEPTH

description: Climate Models
DOI: 10.17171/1-13-34280-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile SNOW-FALL

description: Climate Models
DOI: 10.17171/1-13-34281-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile SURFACE-ALBEDO

description: Climate Models
DOI: 10.21227/0r7k-j185
title: modified CASIA

description: The modified CASIA dataset is created for research topics like: percetual image hash, image tampering detection, user-device physical unclonable function and so on. The ground truth images are taken from the CASIA image tampering detection evaluation (ITDE) v1.0 database (J.Dong, et. al, CASIA image tampering detection evaluation database, ChinaSIP2013), which contains images from eight categories (animal, architecture, article, character, nature, plant, scene and texture) of size 384X256 or 256X384. Instead of directly using the tampered image set from CASIA ITDE v1.0, the tampered versions of those authentic images are selected from CASIA ITDE V2.0, which are more challenging and comprehensive since it considers post-processing like blurring or filtering over the tampered regions to make the tampered images appear realistic to human eyes. For one authentic image, there may be several tampered versions in the CASIA ITDE v2.0 dataset. To increase the diversity, only one tampered version is kept for each authentic image. As a result, the modified CASIA database contains 400 (8 categories X 50 per category) authentic images and their corresponding tampered versions.According to CASIA ITDE v2.0, the tampered images are generated using crop-and-paste operation under Adobe Photoshop on the authentic images, and the tampered regions may have random shapes and different sizes, rotations or distortions. In order to evaluate the proposed system performance over content-preserving manipulations, we enrich the modified CASIA dataset by adding content-preserving manipulations to the authentic images using Matlab and ImageJ. Common image processing techniques like rotation, scaling, filtering and JPEG compression, and unavoidable process/transmission noises like Gaussian, Salt&Pepper and speckle noise are considered. Furthermore, the abovementioned content-preserving manipulations are also applied to the tampered dataset to evaluate if their combination can evade detection. As a result, the modified CASIA database D contains:1) D_au: 400 authentic images in 8 categories, each with 50 images;2) D_tampered: 400 tampered images corresponding to the authentic ones from D_au;3) D_au_cp: 3600 (400x 9) images generated by adding a single content-preserving manipulation (9 types: Gaussian noise, salt&pepper noise, speckle noise, Gaussian filter, motion blur, JPEG compression, gamma correction, rotation and scaling) to every image of D_au;4) D_tampered_cp: 3600 (400x9) tampered images by applying those 9 content-preserving manipulations listed in 3) to the images of Dtampered.To use the dataset, please cite: [1] Y. Zheng, Y. Cao and C. Chang, "A PUF-Based Data-Device Hash for Tampered Image Detection and Source Camera Identification," in IEEE Transactions on Information Forensics and Security, vol. 15, pp. 620-634, 2020.[2]J. Dong, W. Wang and T. Tan, "CASIA Image Tampering Detection Evaluation Database," 2013 IEEE China Summit and International Conference on Signal and Information Processing, Beijing, 2013, pp. 422-426.
DOI: 10.5281/zenodo.3479807
title: Dataset used in the article: Cunico, G., Aivazidou, E., Mollona, E. (2019), "European Cohesion Policy performance and citizens' awareness: A holistic System Dynamics framework", Investigaciones Regionales – Journal of Regional Research (In Press)

description: In the context of the abovementioned article, interviews with experts about EU funding, as well as a workshop, were performed to find any missing factors or links in the conceptual model developed. This dataset provides detailed information about the interviews and the workshop. The related research is conducted in the context of the Horizon 2020 project PERCEIVE (www.perceiveproject.eu).
DOI: 10.5281/zenodo.3479808
title: Dataset used in the article: Cunico, G., Aivazidou, E., Mollona, E. (2019), "European Cohesion Policy performance and citizens' awareness: A holistic System Dynamics framework", Investigaciones Regionales – Journal of Regional Research (In Press)

description: In the context of the abovementioned article, interviews with experts about EU funding, as well as a workshop, were performed to find any missing factors or links in the conceptual model developed. This dataset provides detailed information about the interviews and the workshop. The related research is conducted in the context of the Horizon 2020 project PERCEIVE (www.perceiveproject.eu).
DOI: 10.17171/1-13-34282-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile SURFACE-PRESSURE

description: Climate Models
DOI: 10.17171/1-13-34283-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile TOTAL-CLOUD-COVER

description: Climate Models
DOI: 10.17171/1-13-34284-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 95thPercentile TOTAL-PRECIPITATION

description: Climate Models
DOI: 10.17171/1-13-34285-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile 10M-WIND-DIRECTION

description: Climate Models
DOI: 10.17171/1-13-34286-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile 10M-WINDSPEED

description: Climate Models
DOI: 10.17171/1-13-34287-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile 2M-DEW-POINT-TEMPERATURE

description: Climate Models
DOI: 10.17171/1-13-34288-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile 2M-RELATIVE-HUMIDITY

description: Climate Models
DOI: 10.17171/1-13-34289-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile 2M-TEMPERATURE

description: Climate Models
DOI: 10.7910/dvn/pkskag/3l3yb0
title: cleaninddict_0720.sas7bdat

description: :unav
DOI: 10.7910/dvn/pkskag/53vvuw
title: 3IE SAS program.sas

description: :unav
DOI: 10.7910/dvn/pkskag/bspmef
title: cleaninddict_0720.tab

description: :unav
DOI: 10.17171/1-13-34290-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile EVAPORATION

description: Climate Models
DOI: 10.17171/1-13-34291-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile ICE-COVER

description: Climate Models
DOI: 10.17171/1-13-34292-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile LATENT-HEAT-FLUX

description: Climate Models
DOI: 10.17171/1-13-34293-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile MEAN-SEA-LEVEL-PRESSURE

description: Climate Models
DOI: 10.17171/1-13-34294-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile SENSIBLE-HEAT-FLUX

description: Climate Models
DOI: 10.17171/1-13-34295-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile SNOW-DEPTH

description: Climate Models
DOI: 10.17171/1-13-34296-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile SNOW-FALL

description: Climate Models
DOI: 10.17171/1-13-34297-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile SURFACE-ALBEDO

description: Climate Models
DOI: 10.17171/1-13-34298-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile SURFACE-PRESSURE

description: Climate Models
DOI: 10.17171/1-13-34299-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile TOTAL-CLOUD-COVER

description: Climate Models
DOI: 10.5281/zenodo.3473129
title: Final dataset used in our paper "Phenological shifts alter the seasonal structure of pollinator assemblages in Europe"

description: To build this dataset we merged records from 15 sources of data, listed in Extended Table 1. The way we mergre this database is described in the method part of the paper. Columns descriptor: Latitude and Longitude : WGS 84 coordinates Jday: Julian day of the record Species_mode: Species names and phenology mode (1,2,... or NA if the phenology is unimodal) Order: taxonomic order of the species Altitude: altitude got from spatial coordinates Source: Source of the data
description: Because of the protection of sensitive information about the exact location of recent populations of for instance rare butterflies to be available to everyone, we provide only 5x5km² location for data from the waarnemingen.be/observations.be databases.
DOI: 10.17171/1-13-34300-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES 98thPercentile TOTAL-PRECIPITATION

description: Climate Models
DOI: 10.17171/1-13-34301-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX 10M-WIND-DIRECTION

description: Climate Models
DOI: 10.17171/1-13-34302-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX 10M-WINDSPEED

description: Climate Models
DOI: 10.17171/1-13-34303-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX 2M-DEW-POINT-TEMPERATURE

description: Climate Models
DOI: 10.17171/1-13-34304-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX 2M-RELATIVE-HUMIDITY

description: Climate Models
DOI: 10.17171/1-13-34305-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX 2M-TEMPERATURE

description: Climate Models
DOI: 10.17171/1-13-34306-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX EVAPORATION

description: Climate Models
DOI: 10.17171/1-13-34307-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX ICE-COVER

description: Climate Models
DOI: 10.25345/c5fw9x
title: MassIVE MSV000084447 - Cyanobacteria extracts and fractions - LC-MSMS
title: Cyanobacteria extracts and SPE fractions. LC MSMS analysis was performed in an UltiMate 3000 UPLC system (Thermo Scientific) using a Kinetex 1.7 mm C18 reversed phase UHPLC column (50 X 2.1 mm) and Maxis Impact HD Q-TOF mass spectrometer (Bruker Daltonics) equipped with ESI source.
DOI: 10.5281/zenodo.3473128
title: Final dataset used in our paper "Phenological shifts alter the seasonal structure of pollinator assemblages in Europe"

description: To build this dataset we merged records from 15 sources of data, listed in Extended Table 1. The way we mergre this database is described in the method part of the paper. Columns descriptor: Latitude and Longitude : WGS 84 coordinates Jday: Julian day of the record Species_mode: Species names and phenology mode (1,2,... or NA if the phenology is unimodal) Order: taxonomic order of the species Altitude: altitude got from spatial coordinates Source: Source of the data
description: Because of the protection of sensitive information about the exact location of recent populations of for instance rare butterflies to be available to everyone, we provide only 5x5km² location for data from the waarnemingen.be/observations.be databases.
DOI: 10.5519/qd.45tylqk9
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (308 records)
DOI: 10.17171/1-13-34308-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX LATENT-HEAT-FLUX

description: Climate Models
DOI: 10.17171/1-13-34309-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX MEAN-SEA-LEVEL-PRESSURE

description: Climate Models
DOI: 10.17171/1-13-34310-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX SENSIBLE-HEAT-FLUX

description: Climate Models
DOI: 10.17171/1-13-34311-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX SNOW-DEPTH

description: Climate Models
DOI: 10.17171/1-13-34312-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX SNOW-FALL

description: Climate Models
DOI: 10.17171/1-13-34313-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX SURFACE-ALBEDO

description: Climate Models
DOI: 10.17171/1-13-34314-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX SURFACE-PRESSURE

description: Climate Models
DOI: 10.17171/1-13-34315-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX TOTAL-CLOUD-COVER

description: Climate Models
DOI: 10.17171/1-13-34316-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MAX TOTAL-PRECIPITATION

description: Climate Models
DOI: 10.17171/1-13-34317-1
title: WORLD GRIDDED-DATASET ECHAM5 6000BP MULTI-YEAR-SEASONAL-VALUES MEAN 10M-WIND-DIRECTION

description: Climate Models