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Dataset corresponding to the figures of the article: Influence of vacuum thermal annealing and air exposure on the performance of single-layer MoS2 devices. Pablo Bastante et al 2024 Nanotechnology 35 485201 DOI 10.1088/1361-6528/ad77dc, Peer reviewed

[Data Access] The data processed in version 1.5.1 was a result of research undertaken in two separate projects. The processing and creation of new aerosol optical property products was done within the FOCI project, and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative. The processed data is designed to be complementary to the data provided in version 1.5 of GHOST. The data is separated out per network, per temporal resolution, per component, and is saved as netCDF4 files, per year and month. There is additionally one synthetic network entitled "GHOST", which aggregates data across all networks. The dataset is compressed as .zip files per network. Beneath each network, collections of files per temporal resolution, per component, are compressed as tar.xz files. Each network .zip file can be decompressed via the following syntax: unzip [network].zip Component tar.xz files can be decompressed via the following syntax: tar -xf [component].tar.xz, [How to Use] Inside the GHOST dataset are a plethora of variables, thus it can difficult to fully exploit the extent of the available information. For this reason a companion publication has been written, detailing every aspect of the GHOST dataset: https://doi.org/10.5194/essd-2023-397 If you have any other doubts of queries regarding the dataset, please email: dene.bowdalo@bsc.es, [How to Cite] If you plan to use this work please kindly cite both this dataset and the describing publication: Bowdalo, D.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Zenodo [data set], https://doi.org/10.5281/zenodo.10637449, 2024. Bowdalo, D., Basart, S., Guevara, M., Jorba, O., Pérez García-Pando, C., Jaimes Palomera, M., Rivera Hernandez, O., Puchalski, M., Gay, D., Klausen, J., Moreno, S., Netcheva, S., and Tarasova, O.: GHOST: A globally harmonised dataset of surface atmospheric composition measurements, Earth Syst. Sci. Data, 16, 4417–4495, https://doi.org/10.5194/essd-16-4417-2024, 2024., [GHOST] Globally Harmonised Observations in Space and Time, represents one of the biggest collection of harmonised measurements of atmospheric composition at the surface. In total, ~10 billion measurements from 1970-2025, of ~600 different components, from ~40 reporting networks, are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties. The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of provided information from the major public reporting networks, saved in 21 data variables, and 163 metadata variables. Extensive effort in particular is put towards the standardisation of measurement process information, and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use), and temporal classifications per measurement (e.g. day / night). A range of standardised network quality assurance flags are associated with each individual measurement. GHOST own quality assurance is also performed and associated with measurements. Measurements prefiltered by some default GHOST quality assurance are also provided., We gratefully acknowledge all data providers for the substantial work done in establishing and maintaining the measuring stations that provide the data contained in this dataset. We would also like to warmly thank all data providers who met with GHOST authors through this work, and for all support given, from helping resolve data rights issues, to giving suggestions for improvements. We acknowledge the computing resources of MareNostrum, and the technical support provided by the Barcelona Supercomputing Center (AECT-2020-1-0007, AECT-2021-1-0027, AECT-2022-1-0008, and AECT-2022-3-0013). We also acknowledge the Red Temática ACTRIS España (CGL2017-90884-REDT), and the H2020 project ACTRIS IMP (\#871115). The processing and creation of new aerosol optical property products was funded by EU HORIZON EUROPE under grant agreement no. 101056783 (FOCI project), and the processing and creation of precipitation chemistry and wet deposition products was funded by the World Meteorological Organization for the Measurement-Model Fusion for Total Global Atmospheric Deposition WMO Initiative. The research leading to the creation of this dataset has also received funding from the grant RTI2018-099894-BI00 funded by MCIN/AEI/ 10.13039/501100011033 (BROWNING), the EU H2020 Framework Programme under grant agreement No. GA 821205 (FORCES), the European Research Council under the Horizon 2020 research and innovation programme through the ERC Consolidator Grant grant agreement No. 773051 (FRAGMENT), the AXA Research Fund (AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center), and the Department of Research and Universities of the Government of Catalonia through the Atmospheric Composition Research Group (code 2021 SGR 01550)., Peer reviewed

Supplementary Material 3: Supplementary Table 2. List of conserved motifs on P. vulgaris v1.0 promoters., NSF, Peer reviewed

[List of Starch Biosynthesis Genes Analyzed] Phvul.004G029100 Phvul.009G052100 Phvul.003G078800 Phvul.008G033800 Phvul.011G044000 Phvul.007G139400 Phvul.003G069200 Phvul.002G096000, The reference genome used was Phaseolus vulgaris v2.1 (Phytozome genome ID: 442, NCBI taxonomy ID: 3885). Raw whole genome sequences of 126 common bean individual plants (NCBI, PRJNA471678) were processed using Trimmomatic and mapped to the v2.1 common bean reference genome with the BWA aligner. The alignments were then subjected to GATK MarkDuplicates to tag duplicated reads. Promoter regions of starch biosynthesis genes (2000 bp downstream to 200 bp upstream of the transcription start site) were extracted, and variants were called using BCFtools mpileup and call functions., Peer reviewed

[Figures S2:] Average contours of each cultivar classified by aspect ratio intwo groups and by solidity inside each group., Peer reviewed

[Figures S1:] Photographs of the cultivars. Please see also: https://zenodo.org/uploads/10628169 (for seeds of Vitis species other than V. vinifera) and https://zenodo.org/records/14610754 (for seeds harvested in 2024). [] Figures S2: Average contours of each cultivar classified by aspect ratio., Peer reviewed

Supplementary data of the analysis "A single-tree approach to determine climate-growth patterns of European beech and their seasonality in the species southern distribution area"., Peer reviewed

This study tested the feasibility of using the Crustastun™ device to stun the American lobster (Homarus americanus) in order to: (i) Determine the effect of the stunning duration on the recovery and behaviour; (ii) Asses physiological disturbances caused by the stunning protocol and subsequent recovery; and, (iii) Evaluate any impact on meat quality following electrical stunning. This study demonstrated the reversibility of CrustastunTM stunning, distinguishing it from terminal dispatch method. Stress induced by electrical stunning was evidenced by the elevated L-lactate levels in the haemolymph, highlighting the use of this metabolite as an indicator of secondary stress response, This study was supported by a company that preferred to remain uncited and co-funded by the Spanish government though the “Severo Ochoa Center of Excellence” accreditation (CEX2019-000928-S), Each of the following are separate pages of the excel file: Animal information; Activity; Righting; Physiology; Meatquality, Peer reviewed

This research used samples and/or data provided by the Arctic Century Expedition, a joint initiative led by the Swiss Polar Institute (SPI), the Antarctic and Arctic Research Institute (AARI), and GEOMAR Helmholtz Centre for Ocean Research Kiel (GEOMAR), and funded by the Swiss Polar Foundation. GL and ZAK acknowledge that this project has been made possible by a grant from the Swiss Polar Institute, Dr Frederik Paulsen. GPF acknowledges funding from the Academy of Finland (Grant No. 342227). ZAK acknowledges this project has received funding from the Horizon Europe program under Grant Agreement No. 101137680 (Project CERTAINTY), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed

The data include parameters of climate reconstructions|paleoceanography with a geographic location of Mediterranean Sea, North Atlantic Ocean. The time period coverage is from 16065 to 131 in calendar years before present (BP). See metadata information for parameter and study location details, This research was funded by ERC-Consolidator grant TIMED (Contract Agreement 683237), TRANSMOW project (PID2019-105523RB-I00) and MORIA project (PID2022-138010OB-I00) from the Agencia Estatal de Investigación (Spain). Members of the research group of Marine Geosciences from the Universitat de Barcelona thank the Generalitat de Catalunya for the 2021 SGR 01195 grant and IC for the ICREA-Academia Award. We thank the Universitat de Barcelona (Facultat de Ciències de la Terra, Departament de Dinàmica de la Terra I de l'Oceà), and the Sapienza Università di Roma (Department of Earth Sciences), which supported the research with the Doctoral School “Vito Volterra” Grant. We are grateful for the ship time provided by IPEV on the Marion Dufresne within the EuroFLEETS GATEWAY project (Grant Agreement 228344), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), Peer reviewed