cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26037/yareta:pbdtwm7tzbh63iwcxb4gwaslhq
title: A FPGA-Based Post-Processing and Validation Platform for Random Number Generators

description: Computer security relies heavily on random numbers (RN) for key exchange of authentication algorithms. However, IoT device security is often based on poor quality PRNGs. This issue can be overcome using TRNGs that may offer better quality and higher security. Nonetheless, TRNG sources often provide slow throughput and require post-processing to correct hardware biases and ensure the desired statistical behavior. We propose a FPGA-based hardware platform able to validate and post-process multiple TRNG sources. A provably secure post-processing algorithm (SPRG) is implemented in hardware. This algorithm improves RN quality while maintaining high throughput. A platform providing hardware acceleration has been implemented on a Kintex-7 FPGA board. It tests the validity of the generated numbers through X² and SP800-90B online statistical tests and improves the bitstream randomness by using AIS-31 or SPRG post-processing hardware cores. The platform is modular and targets both back-end servers and IoT edge devices.
DOI: 10.26037/yareta:pxt3we2i4rf37f5unvelwjzsym
title: AAM for Remote Functionalization of α,β-Unsaturated Carbonyls by Multimetallic Sequential Catalysis

description: publication 63 - Mazet Group
DOI: 10.26037/yareta:qs3vif5ggbc6jpanuricxbkrbm
title: Dataset for Synthesis and Properties of Chiral Fluorescent Helicene-BODIPY Conjugates

description: A series of chiral fluorescent helicene-BODIPY conjugates was prepared by the regioselective formylation of aza[4]helicene precursors and then an efficient one-pot two-step BODIPY synthesis (13 examples, 28-82%). Fused conjugates exhibit absorption and fluorescence properties (ΦF 30-45%) in the red visible domain, and a CPL signature could be measured at 605 nm (glum ±5·10-4). Photophysical and electronic properties were investigated and rationalized through first principles
DOI: 10.26037/yareta:uxnr3yhgsvfzvo6trneyyglzvm
title: Low-temperature monoclinic layer stacking in atomically thin CrI3 crystals

description: Data set for: 2D Materials 7, 015007 (2020) https://doi.org/10.1088/2053-1583/ab4c64
DOI: 10.26037/yareta:w76n2owonzc6dj2bkbzexpek3u
title: Corpus C-PROM - PFC

description: The C-PROM-PFC corpus consists in a subpart of the material recorded within the Phonologie du Français Contemporain framework (Durand, Laks, Lyche 2009). It includes 15 regional varieties of French, recorded in 3 different countries of Europe: 5 varieties spoken in the Northern part of metropolitan France (Béthune, Brécey, Lyon, Paris and Ogéviller); 5 varieties spoken in Switzerland (Fribourg, Geneva, Martigny, Neuchâtel and Nyon) and 5 varieties spoken in Belgium (Brussels, Gembloux, Liège, Marche-en-Famène and Tournai). For each of the 15 locales, 4 female and 4 male speakers were selected; they were born and raised in the city in which they were recorded. As for the age of the speakers, it ranges from 19 to 82 years. Each of the 120 speakers was recorded in a reading text task (the text is 398 words-long) and in semi-directed sociolinguistic interviews, in which the informant had minimal interaction with the interviewer.
DOI: 10.26037/yareta:xzczpcai2nbp5l4na7rx2oelse
title: Normalized Difference Water Index (NDWI) - Annual Mean - Switzerland

description: This dataset is an annual time-serie of Landsat Analysis Ready Data (ARD)-derived Normalized Difference Water Index (NDWI) computed from Landsat 5 Thematic Mapper (TM) and Landsat 8 Opeational Land Imager (OLI). To ensure a consistent dataset, Landsat 7 has not been used because the Scan Line Correct (SLC) failure creates gaps into the data.
NDWI quantifies plant water content by measuring the difference between Near-Infrared (NIR) and Short Wave Infrared (SWIR) (or Green) channels using this generic formula:
(NIR - SWIR) / (NIR + SWIR)
For Landsat sensors, this corresponds to the following bands:
Landsat 5, NDVI = (Band 4 – Band 2) / (Band 4 + Band 2).
Landsat 8, NDVI = (Band 5 – Band 3) / (Band 5 + Band 3).
NDWI values ranges from -1 to +1.
NDWI is a good proxy for plant water stress and therefore useful for drought monitoring and early warning.
NDWI is sometimes alos refered as Normalized Difference Moisture Index (NDMI)

Standard Deviation is also provided for each time step.

Data format: GeoTiff

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch)
DOI: 10.26037/yareta:4pgtzcafazbkle2a7qkvytvk3i
title: EPstudy

description: Mass spectrometry-based clinical metabolomics data of urine from 100 human subjects
DOI: 10.26037/yareta:aotwkmfdm5dvtmraoicfrp26ea
title: Reward biases spontaneous neural reactivation during sleep

description: Sleep favors the reactivation and consolidation of newly acquired memories. Yet, how our brain selects the noteworthy information to be reprocessed during sleep remains largely unknown. From an evolutionary perspective, individuals must retain information that promotes survival, such as avoiding dangers, finding food, or obtaining praise or money. Here, we test whether neural representations of rewarded (compared to non-rewarded) events have priority for reactivation during sleep. Using functional MRI and a brain decoding approach, we show that patterns of brain activity observed during waking behavior spontaneously reemerge during slow-wave sleep. Critically, we report a privileged reactivation of neural patterns previously associated with a rewarded task (i.e., winning at a complex game). Moreover, during sleep, activity in task-related brain regions correlates with better subsequent memory performance. Our study uncovers a neural mechanism whereby rewarded life experiences are preferentially replayed and consolidated while we sleep.
DOI: 10.26037/yareta:bkiubi7cczhgbjkkzx2h6mpzya
title: CURRENT-BIOLOGY-D-20-0012

description: Lipidomics dataset related to the paper: Conserved functions of ether lipids and sphingolipids in the early secretory pathway.
PCPE= Glycerophospholipid measurements in positive mode.
PIPSCL=Glycerophospholipid measurements in negative mode.
SMCER= Sphingolipid measurements in positive mode.

Please refer to Data S3 in the paper to see how the lipids have been categorized.
DOI: 10.26037/yareta:cyluachonne73ijg6udtmfm44a
title:  Episodic Future Thinking in Autism Spectrum Disorder and 22q11.2 Deletion Syndrome: Association with Anticipatory Pleasure and Social Functioning

description: Episodic future thinking (EFT) has been suggested to underlie anticipatory pleasure (AP), itself known to play a crucial role in social functioning (SF). Both AP and SF are impaired in various clinical populations, including autism spectrum disorders (ASD) and 22q11.2 deletion syndrome (22q11DS). Therefore, the relationship between EFT, AP and SF was investigated, as well as the potential role of projecting oneself in a social vs. non-social context. Seventy-seven participants [24 with 22q11DS, 20 with ASD, 33 typically developing controls (TDs)] (aged 12–25) were included. They were assessed with a future thinking task in which they were asked to recall a memory and produce a likely event. Narratives were rated based of specificity, richness and imaginability. Participants completed questionnaires assessing AP and SF. Narratives from ASD and 22q11DS participants were rated as less vivid compared to TDs. However, the characteristics of the narratives differed between ASD and 22q11DS participants in terms of specificity and level of details, as well as in reaction to social condition. Moreover, correlations were found between AP and EFT in both ASD and 22q11DS participants, and between SF and EFT in ASD participants. These results point towards impairments in EFT in both ASD and 22q11DS participants but with a specific profile in each condition. The observed associations between EFT and AP suggest that decreased autonoetic consciousness might underlie AP impairments. In ASD individuals, the association between SF and EFT highlights the need to better characterize EFT since EFT could be another mechanism contributing to social difficulties.
DOI: 10.26037/yareta:fpeinkcpvfeexowfxlb5rx7wki
title: Landsat 7 Surface Reflectance Analysis Ready Data 2009

description: Data for 2009
This dataset is the Landsat 7 Surface Reflectance Analysis Ready Data (ARD) collection archive for Switzerland, generated by USGS (https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance).

Detailed description about the Landsat 7 mission can be found in the CEOS Earth Observation Handbook: http://database.eohandbook.com/database/missionsummary.aspx?missionID=349.

The Landsat archive is extremely interesting because this is the longest EO program, initiated in 1972, it has provided continual and consistent observations for almost 50 years. Since 2008, the complete data archive has been provided under a free and open access policy. This has enabled dense time-series analysis, moving beyond simple diachronic comparison of a set of images, therefore dramatically improving capabilities to monitor environmental changes. To cover the whole of Switzerland, it requires eight Landsat 7 scenes (Path/Row: 193/027, 194/027, 195/027, 196/027, 193/028, 194/028, 195/028, 196/028) representing an area of latitude 44.9 to 48.7 and longitude 4.1 to 12.8. Data are downloaded as Collection 1/Tier 1 – Level 2 Surface Reflectance encompassing a surface of approximately 185km by 180km. Collection 1/Tier 1 scenes are data with the highest available data quality (e.g., geometric and radiometric corrections) and considered suitable for time-series analysis (https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1). Level 2 corresponds to surface reflectance (i.e., the estimate based on Landsat sensor observations of the fraction of incoming solar radiation reflected from Earth’s surface). These data are corrected for atmospheric perturbations (e.g., aerosol scattering, thin clouds) enabling direct comparison between multiple images and dates. This corresponds to the ARD level.

The sensor’s Scan-Line Corrector (SLC) failed in July 2003 and approximately 225 of the pixels per scene have since then not been scanned. However, the spatial and spectral quality of the remaining 78% of pixels images remain valid. Landsat data are provided by USGS with Quality Assessment (QA) information to help users to determine their suitability for specific applications. An 8-bit LandsatLook Quality Image and 16-bit Quality Assessment Band40 are also included. Details on each file are described at: https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band. Level 2 products are generated by the USGS from level 1 product and using the official LEDAPS/LASRC algorithm. "A Pixel Quality Assurance (pixel_qa) band is provided with all Landsat Surface Reflectance-derived Spectral Indices. The band is in unsigned 16-bit format, values are bit-packed and provide information pertaining to a pixel condition of fill, clear, water, cloud shadow, snow, cloud (yes/no), cloud confidence and cirrus cloud confidence (Landsat 8 only)" https://www.usgs.gov/land-resources/nli/landsat/landsat-sr-derived-spectral-indices-pixel-quality-band.
DOI: 10.26037/yareta:pkbkhpe2cje5ppryp4rnntozdq
title: Cellosaurus release 2

description: Release 2 of the Cellosaurus
DOI: 10.26037/yareta:pu6ol4sh6nhqnav5vjs326cx64
title: Atanasova_Dynamics_Of_Word_Production

description: Raw data of the Dynamics of Word Production study
DOI: 10.26037/yareta:qj3knmkdsba4lkicmaddmdcun4
title: Landsat 7 Surface Reflectance Analysis Ready Data 2019

description: Data for 2019
This dataset is the Landsat 7 Surface Reflectance Analysis Ready Data (ARD) collection archive for Switzerland, generated by USGS (https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance).

Detailed description about the Landsat 7 mission can be found in the CEOS Earth Observation Handbook: http://database.eohandbook.com/database/missionsummary.aspx?missionID=349.

The Landsat archive is extremely interesting because this is the longest EO program, initiated in 1972, it has provided continual and consistent observations for almost 50 years. Since 2008, the complete data archive has been provided under a free and open access policy. This has enabled dense time-series analysis, moving beyond simple diachronic comparison of a set of images, therefore dramatically improving capabilities to monitor environmental changes. To cover the whole of Switzerland, it requires eight Landsat 7 scenes (Path/Row: 193/027, 194/027, 195/027, 196/027, 193/028, 194/028, 195/028, 196/028) representing an area of latitude 44.9 to 48.7 and longitude 4.1 to 12.8. Data are downloaded as Collection 1/Tier 1 – Level 2 Surface Reflectance encompassing a surface of approximately 185km by 180km. Collection 1/Tier 1 scenes are data with the highest available data quality (e.g., geometric and radiometric corrections) and considered suitable for time-series analysis (https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1). Level 2 corresponds to surface reflectance (i.e., the estimate based on Landsat sensor observations of the fraction of incoming solar radiation reflected from Earth’s surface). These data are corrected for atmospheric perturbations (e.g., aerosol scattering, thin clouds) enabling direct comparison between multiple images and dates. This corresponds to the ARD level.

The sensor’s Scan-Line Corrector (SLC) failed in July 2003 and approximately 225 of the pixels per scene have since then not been scanned. However, the spatial and spectral quality of the remaining 78% of pixels images remain valid. Landsat data are provided by USGS with Quality Assessment (QA) information to help users to determine their suitability for specific applications. An 8-bit LandsatLook Quality Image and 16-bit Quality Assessment Band40 are also included. Details on each file are described at: https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band. Level 2 products are generated by the USGS from level 1 product and using the official LEDAPS/LASRC algorithm. "A Pixel Quality Assurance (pixel_qa) band is provided with all Landsat Surface Reflectance-derived Spectral Indices. The band is in unsigned 16-bit format, values are bit-packed and provide information pertaining to a pixel condition of fill, clear, water, cloud shadow, snow, cloud (yes/no), cloud confidence and cirrus cloud confidence (Landsat 8 only)" https://www.usgs.gov/land-resources/nli/landsat/landsat-sr-derived-spectral-indices-pixel-quality-band.
DOI: 10.26037/yareta:rb6tfhh6yjcidgxuan5auvmo4m
title: Dataset for A Catalytic Dual Isomerization/Allylboration Sequence for the Stereoselective Construction of Congested Secondary Homoallylic Alcohols

description: dataset for publication 66
DOI: 10.26037/yareta:sk5fxumr7zfzxd7z3ulezwdani
title: Landsat 5 Surface Reflectance Analysis Ready Data 2006

description: Data for 2006
This dataset is the Landsat 5 Surface Reflectance Analysis Ready Data (ARD) collection archive for Switzerland, generated by USGS (https://www.usgs.gov/land-resources/nli/landsat/landsat-surface-reflectance).

Detailed description about the Landsat 5 mission can be found in the CEOS Earth Observation Handbook: http://database.eohandbook.com/database/missionsummary.aspx?missionID=226.

The Landsat archive is extremely interesting because this is the longest EO program, initiated in 1972, it has provided continual and consistent observations for almost 50 years. Since 2008, the complete data archive has been provided under a free and open access policy. This has enabled dense time-series analysis, moving beyond simple diachronic comparison of a set of images, therefore dramatically improving capabilities to monitor environmental changes. To cover the whole of Switzerland, it requires eight Landsat 5 scenes (Path/Row: 193/027, 194/027, 195/027, 196/027, 193/028, 194/028, 195/028, 196/028) representing an area of latitude 44.9 to 48.7 and longitude 4.1 to 12.8. Data are downloaded as Collection 1/Tier 1 – Level 2 Surface Reflectance encompassing a surface of approximately 185km by 180km. Collection 1/Tier 1 scenes are data with the highest available data quality (e.g., geometric and radiometric corrections) and considered suitable for time-series analysis (https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1). Level 2 corresponds to surface reflectance (i.e., the estimate based on Landsat sensor observations of the fraction of incoming solar radiation reflected from Earth’s surface). These data are corrected for atmospheric perturbations (e.g., aerosol scattering, thin clouds) enabling direct comparison between multiple images and dates. This corresponds to the ARD level.

Landsat data are provided by USGS with Quality Assessment (QA) information to help users to determine their suitability for specific applications. An 8-bit LandsatLook Quality Image and 16-bit Quality Assessment Band40 are also included. Details on each file are described at: https://www.usgs.gov/land-resources/nli/landsat/landsat-collection-1-level-1-quality-assessment-band. Level 2 products are generated by the USGS from level 1 product and using the official LEDAPS/LASRC algorithm. "A Pixel Quality Assurance (pixel_qa) band is provided with all Landsat Surface Reflectance-derived Spectral Indices. The band is in unsigned 16-bit format, values are bit-packed and provide information pertaining to a pixel condition of fill, clear, water, cloud shadow, snow, cloud (yes/no), cloud confidence and cirrus cloud confidence (Landsat 8 only)" https://www.usgs.gov/land-resources/nli/landsat/landsat-sr-derived-spectral-indices-pixel-quality-band.
DOI: 10.26037/yareta:tooxt4tgmvgvjoylnemznheq3y
title: Sentinel-2 Surface Reflectance Analysis Ready Data 2018-Q4

description: Data for 2018-Q4
This dataset is the Sentinel-2 Surface Reflectance Analysis Ready Data (ARD) archive for Switzerland, generated sen2cor 2.5 (http://step.esa.int/main/third-party-plugins-2/sen2cor/).

Detailed description about the Sentinel-2 A & B missions can be found in the CEOS Earth Observation Handbook:
http://database.eohandbook.com/database/missionsummary.aspx?missionID=552 
http://database.eohandbook.com/database/missionsummary.aspx?missionID=553 

Sentinel-2 (S2) is a constellation of two sun-synchronous satellites operated by ESA respectively from 2015 to 2022 (Sentinel-2A) and 2017-2024 (Sentinel-2B). Switzerland is covered by twelve Sentinel-2 scenes (ID: 31TGM, 31TGN, 32TLR, 32TLS, 32TLT, 32TMR, 32TMS, 32TMT, 32TNS, 32TNT, 32TPS, 32TPT). This corresponds to a spatial extent of latitude 45.0 to 47.9N, longitude 5.5 to 11.8E. Data are downloaded as L1C products corresponding to 100x100km2 geometrically corrected Top Of the Atmosphere (TOA) tiles.It supports land monitoring related services, including generation of generic land cover maps, risk mapping and fast images for disaster relief, generation of leaf coverage leaf chlorophyll content and leaf water content42,43. Both satellites carry the MultiSpectral Instrument (MSI).

Sentinel-2 processed to level 2A with Sen2Cor comes with a scene classification (similar to the pixel_qa band provided by USGS for Landsat data) with 12 classes as described at: https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm. It helps identifying pixels that are saturated or defective as well as cloud and cloud shadows that may affect the quality of the images. In addition, ESA provides a monthly status of the quality of Sentinel-2 data through Data Quality Reports (DQR). These reports document geometric and radiometric performances against initial sensor specifications together with observed anomalies and issues. DQR are available at: https://sentinels.copernicus.eu/web/sentinel/data-product-quality-reports.
DOI: 10.26037/yareta:x5i4d56hlbgpdpsr2i7jk7ai24
title: Ground-state Structural Disorder and Excited-state Symmetry Breaking in a Quadrupolar Molecule

description: Data shown in:
Soederberg, M.; Dereka, B.; Marrocchi, A.; Carlotti, B.; Vauthey, E. Ground-state Structural Disorder and Excited-state Symmetry Breaking in a Quadrupolar Molecule. J. Phys. Chem. Lett. 2019, 10, 2944-2948.
https://doi.org/10.1021/acs.jpclett.9b01024
DOI: 10.26037/yareta:y5k2fflz6za6fhjwhebch3drr4
title: Sentinel-2 Surface Reflectance Analysis Ready Data 2016-Q2

description: Data for 2016-Q2
This dataset is the Sentinel-2 Surface Reflectance Analysis Ready Data (ARD) archive for Switzerland, generated sen2cor 2.5 (http://step.esa.int/main/third-party-plugins-2/sen2cor/).

Detailed description about the Sentinel-2 A & B missions can be found in the CEOS Earth Observation Handbook:
http://database.eohandbook.com/database/missionsummary.aspx?missionID=552 
http://database.eohandbook.com/database/missionsummary.aspx?missionID=553 

Sentinel-2 (S2) is a constellation of two sun-synchronous satellites operated by ESA respectively from 2015 to 2022 (Sentinel-2A) and 2017-2024 (Sentinel-2B). Switzerland is covered by twelve Sentinel-2 scenes (ID: 31TGM, 31TGN, 32TLR, 32TLS, 32TLT, 32TMR, 32TMS, 32TMT, 32TNS, 32TNT, 32TPS, 32TPT). This corresponds to a spatial extent of latitude 45.0 to 47.9N, longitude 5.5 to 11.8E. Data are downloaded as L1C products corresponding to 100x100km2 geometrically corrected Top Of the Atmosphere (TOA) tiles.It supports land monitoring related services, including generation of generic land cover maps, risk mapping and fast images for disaster relief, generation of leaf coverage leaf chlorophyll content and leaf water content42,43. Both satellites carry the MultiSpectral Instrument (MSI).

Sentinel-2 processed to level 2A with Sen2Cor comes with a scene classification (similar to the pixel_qa band provided by USGS for Landsat data) with 12 classes as described at: https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm. It helps identifying pixels that are saturated or defective as well as cloud and cloud shadows that may affect the quality of the images. In addition, ESA provides a monthly status of the quality of Sentinel-2 data through Data Quality Reports (DQR). These reports document geometric and radiometric performances against initial sensor specifications together with observed anomalies and issues. DQR are available at: https://sentinels.copernicus.eu/web/sentinel/data-product-quality-reports.