cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26037/yareta:naegejqvxzfwliu236pxn5lus4
title: Geneva University Hospitals Common Problem List

description: ****************************************
This work is licensed under CC BY-SA 4.0
****************************************

LPH - Problem list 

Version: 20220401

This archive contains the problem list developed by the Division of Medical Information Sciences (SIMED) of the Geneva University Hospitals (HUG).
This list has been manually developed and encoded in various international standards. 

Any use of this work has to be in the limitations of the CC BY-SA 4.0 licence.

Citing this work: 
To cite this work in a publication, please cite the following article: 

Gaudet-Blavignac C, Rudaz A, Lovis C
Building a Shared, Scalable, and Sustainable Source for the Problem-Oriented Medical Record: Developmental Study
JMIR Med Inform 2021;9(10):e29174
URL: https://medinform.jmir.org/2021/10/e29174
DOI: 10.2196/29174
DOI: 10.26037/yareta:o2emb6ihavdq7ki6fifwoqc2m4
title: Differences in Resting State Aftermath of Arousing Odorant #2

description: Data for the EmOdor study, 'Differences in Resting State Aftermath of Arousing Odorant'
DOI: 10.26037/yareta:tctxn4dctvdajok4vluflecgsa
title: PACE REFIT building archetype load curves

description: Description: This dataset was generated as part of the PACE REFITS project. Contract number SI/501883-01. This data is a sample dataset. The complete data for all retrofit cases is used in the MANGOret Optimisation Framework. 

For more information contact Ivalin Petkov - epetkov@ethz.ch

Publications: 
Ivalin Petkov, Georgios Mavromatidis, Christof Knoeri, James Allan, Volker H. Hoffmann
MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits. 2022

Authors: James Allan, Portia Murray, Ivalin Petkov, Georgios Mavromatidis

Source Data: OpenSteetMap; Gebäude und wohnungsregister, BFS; STATENT, BFS; swissBoundaries3D, SwissTopo)

Contents:
		Name:annual_simulation_summary,
		 Details: Aggregated annual space heating, cooling and electricity demands.

		Name:centroid_details,	
		 Details: Details about each centroid/archetype and their percentage assignment to the building stock of Switzerland

		Name:regional_boundaries_with_canton_name
		Details: An ESRI Shapefile containing the boundaries of the climatic zones used for simulation

		Name: profiles
		Details: Daily and hourly demand profiles for each centroid/archetype listed in the annual_simulation_summary. 
		The name convention for each file is based on the fields in the annual simulation summary. 
		profiles/<demand_resolution>/<centroid_fid>_<retrofit_measure>_<rcp>_<simulation_year>_<demand_resolution> where demand 
		resolution is either daily or hourly. The rest of the fields are explained in the accompanying field_descriptors.csv.
		

Institute: UESL, EMPA

Corresponding author: James Allan
DOI: 10.26037/yareta:txap4ayzibcm5hcipvvzufx72a
title: Quenching the band gap of 2D semiconductors with a perpendicular electric field

description: Data set for: Nature Nanotechnology volume 17, pages1078–1083 (2022)
https://doi.org/10.1038/s41565-022-01183-4
DOI: 10.26037/yareta:x7tizmmxhra5nmi23lju37c54m
title: Gliadin-reactive vitamin D-sensitive proinflammatory ILCPs are enriched in celiac patients

description: Data collection
DOI: 10.26037/yareta:yufxeb5ip5efdhw4rham2233ga
title: PLK1 controls centriole distal appendage formation and centrobin removal via independent pathways #3

description: Secondary data linked  to the publication "PLK1 controls centriole distal appendage formation and centrobin removal via independent pathways" in Journal of Cell Science, Le Roux-Bourdieu et., 2022, Part 3 of 6
DOI: 10.26037/yareta:z6t22eyzfjghleyte4lord23la
title: Gomes et al.  2022. ANGPTL4 is a potential driver of HCV-induced peripheral insulin resistance

description: Deposit includes raw data, intermediate and final analysis files used to build up figures and tables of the paper "ANGPTL4 is a potential driver of HCV-induced peripheral insulin resistance" by Gomes et al.  2022.
DOI: 10.26037/yareta:zegqbhauk5dm3mfvndtlrfmgg4
title: Open data to “Room-temperature superconductivity — or not? Comment on Nature 586, 373 (2020) by E. Snider et al.”

description: Data files pertaining to International Journal of Modern Physics B,  2375001 (2022), https://www.worldscientific.com/doi/10.1142/S0217979223750012

Supplementary data pertaining to Fig.20e, Fig30h, Fig30i are available on https://doi.org/10.26037/yareta:tvifxa7pwzcqhb3ydw6yjjsjbu
DOI: 10.26037/yareta:zpdgigxnbbbankvjmtau44gn2a
title: PLK1 controls centriole distal appendage formation and centrobin removal via independent pathways #6

description: Secondary data linked  to the publication "PLK1 controls centriole distal appendage formation and centrobin removal via independent pathways" in Journal of Cell Science, Le Roux-Bourdieu et., 2022, Part 6 of 6
DOI: 10.26037/yareta:awkph7hc4vgb3mz2ws6ufwihny
title: Python et al., Development and standardization of a Computerized Test of Syntactic Comprehension in French

description: The Computerized Test of Syntactic Comprehension in French (TICSf) was created to address the lack of standardized tests in aphasiology, especially in syntactic comprehension. The TICSf includes two parallel lists of 50 sentences presented by oral or in writing evaluating equally 5 types of syntactic structures (active, passive, relative, negative and containing a preposition). The task is a sentence-picture matching task, including an “other” response to be selected when none of the four images is appropriate, in order to reduce random guess. A lexical assessment containing the lexical items used in the syntactic assessment is also available. The TICSf has been normalized on an adult population of 152 French speakers, aged from 19 to 88 years old. The analyzes using a generalized linear mixed model (1) confirm that the syntactic comprehension is an amodal process, (2) show an effect of age after 60 years old, (3) an effect of the educational level and (4) an effect of the type of sentences, especially for relative sentences, which are more difficult to understand. Norms are provided by modality, age group, educational level and type of sentences.
DOI: 10.26037/yareta:caj5wsstqzaybovbnierjy4lqa
title: PGE2-Cacialli-EMBOJ-2022

description: all the raw data attached to the paper from Caicialli et al. EMBOJ, 2022.
DOI: 10.26037/yareta:cex2s3ha25c6xngsonbmrsaftm
title: Bouzidi_Gendolla_Action-State_Orientation_Difficulty

description: Data and data coding of the experiment reported in:

Yann S. Bouzidi & Guido H. E. Gendolla

Individual Differences in Action-State Orientation Moderate Task Difficulty Effects on Effort-Related Cardiac Response
DOI: 10.26037/yareta:crpgoixl6rg5xbxisjburpdrba
title: PLK1 controls centriole distal appendage formation and centrobin removal via independent pathways #1

description: Secondary data linked  to the publication "PLK1 controls centriole distal appendage formation and centrobin removal via independent pathways" in Journal of Cell Science, Le Roux-Bourdieu et., 2022, Part 1 of 6
DOI: 10.26037/yareta:dvmtwmu6y5eafp7oz6q7wnac7u
title: Mitochondrial copy number variation as a surrogate marker of acquired resistance to the bifunctional electrophilic chemotherapeutic drug busulfan

description: Experimental data obtained that was used to prepare the report. The study used MOLM13 cells. cells were exposed to Busulfan and also to cytarabine to develop acquired resistance, and was correlated with that of dynamic changes observed in mitochondrial copy number. lymphoblastoid cells (LCLs) were also used as normal cells to test the impact of mitochondrial copy number in relation to the cellular sensitivities to busulfan.
DOI: 10.26037/yareta:e7zicea2vrgnbb5bhy3thlkkv4
title: AMYPAD Diagnostic and Patient Management Study (DPMS)

description: The AMYPAD Diagnostic and Patient Management Study (DPMS) has selected and is following-up a memory clinic population suspected of possible Alzheimer's disease (AD), focusing on those with subjective cognitive decline (SCD), mild cognitive impairment (MCI) and dementia where Alzheimer's disease is in the differential diagnosis, to determine the usefulness of β-amyloid imaging with regards to diagnostic confidence, decision trees, change in diagnosis, and alterations between planned and actual patient management plans.
DOI: 10.26037/yareta:isfd5jbykfbfnnzdjx7pnmtdhe
title: Band Gap Opening in Bilayer Graphene-CrCl3/CrBr3/CrI3 van der Waals Interfaces

description: Data set for: Nano Lett. 22, 16, 6760–6766 (2022)
https://doi.org/10.1021/acs.nanolett.2c02369
DOI: 10.26037/yareta:j4xly5vdijcaphbruprfr6fmmq
title: Anthocyanin Reflectance Index (ARI) - Switzerland [2018]

description: This dataset is an time-serie of Sentinel-2 Analysis Ready Data (ARD)- derived Anthocyanin Reflectance Index (ARI) computed from Sentinel-2 data. ARI is is a reflectance measurement, sensitive to vegetation pigment content using the forumla ARI = (1/b3) - (1/b5). See Gitelson et al. (2009) DOI: 10.3732/ajb.0800395. 

Anthocyanins are pigments common in higher plants, causing their red, blue and purple coloration. They provide valuable information about the physiological status of plants, as they are considered indicators of various types of plant stresses.
The reflectance of anthocyanin is highest around 550nm. However, the same wavelengths are reflected by chlorophyll as well. To isolate the anthocyanins, the 700nm spectral band, that reflects only chlorophyll and not anthocyanins, is subtracted.

ARI values for the examined trees in this original article ranged in values from 0 to 0.2.
Values are provided as integer and multiplied by 1000 

Metrics: annual (_annual) and seasonal (_spring; _summer; _autumn; _winter) mean (_nanmean), standard dev (_nanstd), min (_nanmin), max (_nanmax), median (_nanmedian), and amplitude (_range)

Data format: GeoTiff  

This dataset has been genereated with the Swiss Data Cube (http://www.swissdatacube.ch) in the frame of the ValPar.CH project
DOI: 10.26037/yareta:o3t4cjzskrbxlpnz5rehlcatuu
title: Primary_DEER_data_Galazzoetal_SciAdv_2022

description: Primary DEER traces of the main figures are archived here
DOI: 10.26037/yareta:owndvtuf3vaxvozetmxd7xmma4
title: Data of "Second and third harmonic generation from simultaneous high peak‐ and high average‐power thin disk laser"

description: SHG and THG conversion efficiencies
Spectra
Beam profile
Data from U. Andral et al. "Second and third harmonic generation from simultaneous high peak‐ and high average‐power thin disk laser", Applied Physics B 128, 177 (2022), doi:10.1007/s00340-022-07887-8
DOI: 10.26037/yareta:p7umynex3jfotciq2pt6p5ezcu
title: Landsat 8 Surface Reflectance Analysis Ready Data 2021

description: Data for 2021
This dataset is the Landsat 8 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 8 mission can be found in the CEOS Earth Observation Handbook: http://database.eohandbook.com/database/missionsummary.aspx?missionID=547.

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 8 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:py3o7ztvrnfdtcatka6yfrz27e
title: Dataset of Chemo and Regioselective Multiple C(sp2)-H Insertions of Malonate Metal Carbenes for Late-Stage Functionalizations of Azahelicenes

description: Late-stage functionalization (LSF) strategies, which favor large scope of products and time-efficient syntheses, are particularly attractive. While proficient, these approaches promote often specific reactivities at single site only. Herein, in the context of heterohelicene chemistry, metal-catalyzed decompositions of alpha-diazomalonates are shown to afford, step-by-step, multi-functionalized chiral diaza [4]helicenes. By selecting the catalyst, [CpRu(CH3CN)3][PF6] (Cp = cyclopentadienyl) or Rh2(oct)4, chemo and regioselective insertions of derived metal carbenes are achieved in favor of mono- or bis-functionalized malonate derivatives respectively (r.r. > 49:1, up to 77% yield, 12 examples). Mechanism of formation and origin of selectivity are elucidated based on DFT calculations. Formation of products of tris- and tetra-malonate insertions can be further achieved thanks to the higher reactivity of rhodium carbenes. This multi-introduction of malonate groups is particularly useful to tune important properties of the [4]helicene core such as absorption, emission (quantum yield 5 to 22% and lifetime 3.0 to 8.4 ns, CH3CN) and Brønsted acidity (pKa 2•H+ to 6•H+, 6.63 to−0.42 and lower). TDDFT showcases the importance of conformations in the optical response. Cellular imaging studies evidence strong discriminations among various malonate esters or derived amides in the labelling of Hela-MZ cells.
DOI: 10.26037/yareta:rw7wdahcqrb5llpvpenlbaj4gu
title: Data

description: data
DOI: 10.26037/yareta:sb6xnhzxtfho5jw3dulziug4fa
title: PLK1 controls centriole distal appendage formation and centrobin removal via independent pathways #5

description: Secondary data linked  to the publication "PLK1 controls centriole distal appendage formation and centrobin removal via independent pathways" in Journal of Cell Science, Le Roux-Bourdieu et., 2022, Part 5 of 6
DOI: 10.26037/yareta:vgujly5xjjf75eeoxe72azdbm4
title: Landsat 7 Surface Reflectance Analysis Ready Data 2020

description: Data for 2020
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:vukf2hy33rcn3jvyuemseazr4y
title: Sentinel-2 Surface Reflectance Analysis Ready Data 2020-Q1

description: Data for 2020-Q1
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:x7tr5ur5onhatdk7qebkv2kx54
title: Landsat 8 Surface Reflectance Analysis Ready Data 2020

description: Data for 2020
This dataset is the Landsat 8 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 8 mission can be found in the CEOS Earth Observation Handbook: http://database.eohandbook.com/database/missionsummary.aspx?missionID=547.

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 8 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:ykswzqi5ybgwdmp54y5lwvqtea
title: (ERREUR) Bouzidi_Gendolla_Action-Station_Orientation_Difficulty

description: Data and data coding of the experiment reported in:

Yann S. Bouzidi & Guido H. E. Gendolla

Individual Differences in Action-State Orientation Moderate Task Difficulty Effects on Effort-Related Cardiac Response