cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25439/rmt.12325436
title: Happy Feet - research into baby footprints as an infant biometric

description: The pressing infant biometric problem is to find a biometric means to identify infants cheaply, reliably, and automatically. Physical traits of infants are tiny, delicate, and grow rapidly. The authors focus on a novel area of friction-ridge skin as a potential answer: the ball under the big toe. The ballprint is readily accessible, with more features and larger ridges than a fingerprint. The authors followed 54 newborns for 2 years, capturing their ballprints with an adult fingerprint scanner within 3 days of birth, at 2 months, at 6 months, and at 2 years. The authors show the growth of the ballprint is isotropic rather than affine during infancy. The isotropic growth rate from birth can be measured by the change in inter-ridge spacing, which the authors show precisely mirrors change in physical length from birth, as recorded by World Health Organisation for large, diverse infant populations. From 2 months of age, by using isotropic scaling to compensate for growth, the authors successfully matched good quality images with 0% equal error rate using existing adult fingerprint technology, even for captures 22 months apart. These findings flag the value of ballprints as a practical means of infant identification, by themselves, or together or sequentially with other biometrics.<br>Sample data attached, full data set available from author on request.
DOI: 10.25439/rmt.12325436.v1
title: Happy Feet - research into baby footprints as an infant biometric

description: The pressing infant biometric problem is to find a biometric means to identify infants cheaply, reliably, and automatically. Physical traits of infants are tiny, delicate, and grow rapidly. The authors focus on a novel area of friction-ridge skin as a potential answer: the ball under the big toe. The ballprint is readily accessible, with more features and larger ridges than a fingerprint. The authors followed 54 newborns for 2 years, capturing their ballprints with an adult fingerprint scanner within 3 days of birth, at 2 months, at 6 months, and at 2 years. The authors show the growth of the ballprint is isotropic rather than affine during infancy. The isotropic growth rate from birth can be measured by the change in inter-ridge spacing, which the authors show precisely mirrors change in physical length from birth, as recorded by World Health Organisation for large, diverse infant populations. From 2 months of age, by using isotropic scaling to compensate for growth, the authors successfully matched good quality images with 0% equal error rate using existing adult fingerprint technology, even for captures 22 months apart. These findings flag the value of ballprints as a practical means of infant identification, by themselves, or together or sequentially with other biometrics.<br>Sample data attached, full data set available from author on request.
DOI: 10.7295/w9cil51210
title: CIL:51210, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51211
title: CIL:51211, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51212
title: CIL:51212, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51213
title: CIL:51213, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51214
title: CIL:51214, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51215
title: CIL:51215, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51216
title: CIL:51216, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51217
title: CIL:51217, Mus musculus, RAW 264.7 TIB-71
DOI: 10.5519/qd.r7hzwrr4
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (11 records)
DOI: 10.7295/w9cil51218
title: CIL:51218, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51219
title: CIL:51219, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51221
title: CIL:51221, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51222
title: CIL:51222, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51223
title: CIL:51223, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51224
title: CIL:51224, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51225
title: CIL:51225, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51226
title: CIL:51226, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51227
title: CIL:51227, Mus musculus, RAW 264.7 TIB-71
DOI: 10.5281/zenodo.3859632
title: Microsatellite genotypes of bearded vultures (Gypaetus barbatus)

description: Multilocus microsatellite genotypes of 236 <em>Gypaetus barbatus </em>individuals. This first column contains the individual sample identity as provided in Supplementary Data 1 and subsequent columns are allele scores in single row format. Alleles are scored according to their molecular size (in base pairs); missing data is given as "0".
DOI: 10.5281/zenodo.3859633
title: Microsatellite genotypes of bearded vultures (Gypaetus barbatus)

description: Multilocus microsatellite genotypes of 236 <em>Gypaetus barbatus </em>individuals. This first column contains the individual sample identity as provided in Supplementary Data 1 and subsequent columns are allele scores in single row format. Alleles are scored according to their molecular size (in base pairs); missing data is given as "0".
DOI: 10.7295/w9cil51228
title: CIL:51228, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51229
title: CIL:51229, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51230
title: CIL:51230, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51231
title: CIL:51231, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51232
title: CIL:51232, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51233
title: CIL:51233, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51235
title: CIL:51235, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51236
title: CIL:51236, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51237
title: CIL:51237, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51238
title: CIL:51238, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51239
title: CIL:51239, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51240
title: CIL:51240, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51241
title: CIL:51241, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51242
title: CIL:51242, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51243
title: CIL:51243, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51244
title: CIL:51244, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51245
title: CIL:51245, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51246
title: CIL:51246, Mus musculus, RAW 264.7 TIB-71
DOI: 10.5281/zenodo.3786977
title: Data to Richter et al. 2020 Comparative analysis of worker head anatomy of Formica and Brachyponera (Hymenoptera: Formicidae) in Arthropod Systematics & Phylogeny

description: This dataset contains the ant head µCT-Sanning Datasets used for the article "Comparative analysis of worker head anatomy of <em>Formica</em> and <em>Brachyponera</em> (Hymenoptera: Formicidae)" by Richter et al. published in <em>Arthropod Systematics & Phylogeny </em>2020. In addition, the image plates from that article (in highest resolution as TIF files) and a series of supplementary 3D-volume render video files are deposited. The datasets for CASENT0709409, CASENT0709411 and CASENT0790267 are deposited as they were used in this study (with transformation to adjust the orientation of the ant head to the global axes and cropped to reduce file size) while CASENT0709419 is deposited as the original scanning result as it was not modified for the work in this article. Scanning details can be found in the materials and methods section of the article. Scanning parameters were as follows: Power (W): 3W all specimens Voltage (kV): 40kV all specimens Voxel Size: CASENT0709409: 1,1557 µm<sup>3</sup>; CASENT0709419: 1,2244 µm<sup>3</sup>; CASENT0709411: 2,5527µm<sup>3 </sup>; CASENT0790267: 2,8337 µm<sup>3</sup> Exposure Time (s): CASENT0709409: 25 s; CASENT0709419: 15 s; CASENT0709411: 7 s<sup> </sup>; CASENT0790267: 5 s Source Distance (mm): CASENT0709409: -9,5038 mm; CASENT0709419: -9,5321 mm; CASENT0709411: 10,038 mm<sup> </sup>; CASENT0790267: 13,0037 mm Detector Distance (mm): CASENT0709409: 46 mm; CASENT0709419: 43,0166 mm; CASENT0709411: 16,5043 mm<sup> </sup>; CASENT0790267: 18,0015 mm
DOI: 10.7295/w9cil51247
title: CIL:51247, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51248
title: CIL:51248, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51249
title: CIL:51249, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51250
title: CIL:51250, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51251
title: CIL:51251, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51254
title: CIL:51254, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51255
title: CIL:51255, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51257
title: CIL:51257, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51258
title: CIL:51258, Mus musculus, RAW 264.7 TIB-71
DOI: 10.5281/zenodo.3786976
title: Data to Richter et al. 2020 Comparative analysis of worker head anatomy of Formica and Brachyponera (Hymenoptera: Formicidae) in Arthropod Systematics & Phylogeny

description: This dataset contains the ant head µCT-Sanning Datasets used for the article "Comparative analysis of worker head anatomy of <em>Formica</em> and <em>Brachyponera</em> (Hymenoptera: Formicidae)" by Richter et al. published in <em>Arthropod Systematics & Phylogeny </em>2020. In addition, the image plates from that article (in highest resolution as TIF files) and a series of supplementary 3D-volume render video files are deposited. The datasets for CASENT0709409, CASENT0709411 and CASENT0790267 are deposited as they were used in this study (with transformation to adjust the orientation of the ant head to the global axes and cropped to reduce file size) while CASENT0709419 is deposited as the original scanning result as it was not modified for the work in this article. Scanning details can be found in the materials and methods section of the article. Scanning parameters were as follows: Power (W): 3W all specimens Voltage (kV): 40kV all specimens Voxel Size: CASENT0709409: 1,1557 µm<sup>3</sup>; CASENT0709419: 1,2244 µm<sup>3</sup>; CASENT0709411: 2,5527µm<sup>3 </sup>; CASENT0790267: 2,8337 µm<sup>3</sup> Exposure Time (s): CASENT0709409: 25 s; CASENT0709419: 15 s; CASENT0709411: 7 s<sup> </sup>; CASENT0790267: 5 s Source Distance (mm): CASENT0709409: -9,5038 mm; CASENT0709419: -9,5321 mm; CASENT0709411: 10,038 mm<sup> </sup>; CASENT0790267: 13,0037 mm Detector Distance (mm): CASENT0709409: 46 mm; CASENT0709419: 43,0166 mm; CASENT0709411: 16,5043 mm<sup> </sup>; CASENT0790267: 18,0015 mm
DOI: 10.7295/w9cil51259
title: CIL:51259, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51260
title: CIL:51260, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51261
title: CIL:51261, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51262
title: CIL:51262, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51263
title: CIL:51263, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51264
title: CIL:51264, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51265
title: CIL:51265, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51267
title: CIL:51267, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51268
title: CIL:51268, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51269
title: CIL:51269, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51270
title: CIL:51270, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51271
title: CIL:51271, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51272
title: CIL:51272, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51274
title: CIL:51274, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51275
title: CIL:51275, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51276
title: CIL:51276, Mus musculus, RAW 264.7 TIB-71
DOI: 10.3886/e119603v1
title: Thermal images of the server in different running status

description: Balancing the uneven temperature distribution of data centers can reduce cooling energy consumption significantly, as the overheated surface of servers is one of the root causes of the uneven distribution. This paper presents a method for intelligently diagnosing server operating status based on the heat distribution of the server surface. Five types of server operation can be diagnosed: normal status, main fan failure, vice-fan failure, air vent blockage and low-load status. The method involves signal processing and pattern recognition techniques such as thermal image enhancement, region segmentation and image classification. First, thermal images of server outlets in running status are captured as data; second, the images are preprocessed for standardization; third, after homomorphic filtering enhancement, the images are subjected to one-dimensional maximum entropy segmentation to obtain server hotspot images; fourth, morphological features, texture features and statistical features are extracted from hotspot images; finally, the server status is diagnosed by a support vector machine.
DOI: 10.7295/w9cil51277
title: CIL:51277, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51278
title: CIL:51278, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51279
title: CIL:51279, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51280
title: CIL:51280, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51281
title: CIL:51281, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51282
title: CIL:51282, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51283
title: CIL:51283, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51284
title: CIL:51284, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51287
title: CIL:51287, Mus musculus, RAW 264.7 TIB-71
DOI: 10.3886/e119603v2
title: Thermal images of the server in different running status

description: Balancing the uneven temperature distribution of data centers can reduce cooling energy consumption significantly, as the overheated surface of servers is one of the root causes of the uneven distribution. This paper presents a method for intelligently diagnosing server operating status based on the heat distribution of the server surface. Five types of server operation can be diagnosed: normal status, main fan failure, vice-fan failure, air vent blockage and low-load status. The method involves signal processing and pattern recognition techniques such as thermal image enhancement, region segmentation and image classification. First, thermal images of server outlets in running status are captured as data; second, the images are preprocessed for standardization; third, after homomorphic filtering enhancement, the images are subjected to one-dimensional maximum entropy segmentation to obtain server hotspot images; fourth, morphological features, texture features and statistical features are extracted from hotspot images; finally, the server status is diagnosed by a support vector machine.
DOI: 10.7295/w9cil51285
title: CIL:51285, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51288
title: CIL:51288, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51289
title: CIL:51289, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51290
title: CIL:51290, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51291
title: CIL:51291, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51292
title: CIL:51292, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51294
title: CIL:51294, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51295
title: CIL:51295, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51296
title: CIL:51296, Mus musculus, RAW 264.7 TIB-71
DOI: 10.3886/e119603
title: Thermal images of the server in different running status

description: Balancing the uneven temperature distribution of data centers can reduce cooling energy consumption significantly, as the overheated surface of servers is one of the root causes of the uneven distribution. This paper presents a method for intelligently diagnosing server operating status based on the heat distribution of the server surface. Five types of server operation can be diagnosed: normal status, main fan failure, vice-fan failure, air vent blockage and low-load status. The method involves signal processing and pattern recognition techniques such as thermal image enhancement, region segmentation and image classification. First, thermal images of server outlets in running status are captured as data; second, the images are preprocessed for standardization; third, after homomorphic filtering enhancement, the images are subjected to one-dimensional maximum entropy segmentation to obtain server hotspot images; fourth, morphological features, texture features and statistical features are extracted from hotspot images; finally, the server status is diagnosed by a support vector machine.
DOI: 10.7295/w9cil51297
title: CIL:51297, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51298
title: CIL:51298, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51300
title: CIL:51300, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51301
title: CIL:51301, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51302
title: CIL:51302, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51303
title: CIL:51303, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51304
title: CIL:51304, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51306
title: CIL:51306, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51307
title: CIL:51307, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51135
title: CIL:51135, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51138
title: CIL:51138, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51184
title: CIL:51184, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51308
title: CIL:51308, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51309
title: CIL:51309, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51310
title: CIL:51310, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51312
title: CIL:51312, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51313
title: CIL:51313, Mus musculus, RAW 264.7 TIB-71
DOI: 10.5281/zenodo.3859735
title: Global ECMWF Fire Forecasting system - sample data for wildfires in Sweden on 15-20 July 2018

description: The European Centre for Medium-Range Weather Forecasts (ECMWF) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (GEFF) model. Reanalysis is available through the Copernicus Climate Data Store (CDS) while the medium-range real-time forecast is available through the EFFIS and GWIS platforms. This repository provides FWI sample datasets for the assessment of the wildfires occurred in Sweden on 15-20 July 2018: GEFF-reanalysis, which provides historical records of fire danger conditions e5_hr, this folder contains deterministic model outputs e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members) GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 45r1 of the ECMWF’s Integrated Forecasting System (IFS). rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km) rt_en, this folder contains probabilistic forecasts (~18Km) Geographical bounding box: lon_min = 10.1, lon_max = 24.8, lat_min = 55, lat_max = 69 <strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong> These products have been developed as part of the EU-funded Copernicus Emergency Management Services (CEMS) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (CAMS). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (JRC). GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data. For more information, please refer to the documentation on the CDS and on the EFFIS website.
DOI: 10.5281/zenodo.3859736
title: Global ECMWF Fire Forecasting system - sample data for wildfires in Sweden on 15-20 July 2018

description: The European Centre for Medium-Range Weather Forecasts (ECMWF) produces daily fire danger forecasts and reanalysis products from the Global ECMWF Fire Forecast (GEFF) model. Reanalysis is available through the Copernicus Climate Data Store (CDS) while the medium-range real-time forecast is available through the EFFIS and GWIS platforms. This repository provides FWI sample datasets for the assessment of the wildfires occurred in Sweden on 15-20 July 2018: GEFF-reanalysis, which provides historical records of fire danger conditions e5_hr, this folder contains deterministic model outputs e5_en, this folder contains probabilistic model outputs (made of 10 ensemble members) GEFF-realtime provides real-time forecasts generated using weather forcings from the model cycle 45r1 of the ECMWF’s Integrated Forecasting System (IFS). rt_hr, this folder contains high-resolution deterministic forecasts (~9 Km) rt_en, this folder contains probabilistic forecasts (~18Km) Geographical bounding box: lon_min = 10.1, lon_max = 24.8, lat_min = 55, lat_max = 69 <strong>Please note, the sample data provided in this repository is intended to be used for education purposes only (e.g. training courses).</strong> These products have been developed as part of the EU-funded Copernicus Emergency Management Services (CEMS) and complement other Copernicus products related to fire, such as the biomass-burning emissions made available by the Copernicus Atmosphere Monitoring Service (CAMS). The development of the GEFF modelling system was funded through a third-party agreement with the European Commission’s Joint Research Centre (JRC). GEFF produces fire danger indices based on the Canadian Fire Weather index as well as the US and Australian fire danger models. GEFF datasets are under the Copernicus license, which provides users with free, full and open access to environmental data. For more information, please refer to the documentation on the CDS and on the EFFIS website.
DOI: 10.7295/w9cil51136
title: CIL:51136, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51137
title: CIL:51137, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51139
title: CIL:51139, Mus musculus, RAW 264.7 TIB-71
DOI: 10.7295/w9cil51158
title: CIL:51158, Mus musculus, RAW 264.7 TIB-71
DOI: 10.25592/uhhfdm.979
title: 3D Model of a plaster cast of Seer

description: 3D Model of a plaster cast of Seer
DOI: 10.25592/uhhfdm.980
title: 3D Model of a plaster cast of Seer

description: 3D Model of a plaster cast of Seer
DOI: 10.18465/mnt_estuaire_loire_2019
title: MNT Estuaire de la Loire 2019

description: Modèle Numérique de Terrain obtenue par LiDAR de l'estuaire de la Loire
DOI: 10.5519/qd.zygu6f3t
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (112 records)
DOI: 10.25592/uhhfdm.982
title: 3D Model of an Autopsy table

description: 3D Model of an Autopsy table