dataesr/scientific-paragraphs-categorization huggingface dataset:
1, corresponds to the formation energy of Cu5-(O2)n and is defined as. This is a convenient way of separating the overall free energy change into pressureindependent and pressure-dependent contributions. The change of enthalpy is given by ∆ℎ  2 = ℎ( 0 , ) -ℎ( 0 ,  = 0 ). For maximum accuracy, values for ℎ  2 and   2 taken from the NIST database are used. [36] [37] he (, )-phase diagram is created by determining the number n of adsorbed O2 molecules which minimizes  for a specified temperature and a given oxygen pressure (see Figure  6  and SI Figure  17 ). Measurable oxidation states [Cu(0), Cu(I), Cu(II)] can be assigned to each copper atom for a given Cu5-(O2)n complex (see, e. g. , SI Figures  11  and 16 ). To fully account for the thermodynamical conditions, a Boltzmann-weighted average of their Helmholtz free energies and associated distributions of oxidation states is carried out for each variable pair (, ) (see Figure  6  and SI Table  2 ).
We corrected the aberrations of the condenser lenses up to fourth order, using the Zemlin tableau to obtain a sub-Angstrom electron probe. A condenser aperture of 50 μm yielding an electron probe with a convergence angle of 20 mrad was used. To limit the damage by the electron beam, a fast image recording protocol was used by combining a beam current of 25 pA, a 2. 5 μs dwell time and an automated finetuning alignment of A1 and C1 using the OptiSTEM software. To obtain images with good quality, the beam current and image acquisition time should be optimized according to the stability of the sample under the beam. Aimed to quantitatively characterize the Cu clusters, a specific methodology for the digital analysis of the experimental images has been developed and coded in a home-made MATLAB script.
The X-ray Absorption spectra were normalized by standard methods using the ATHENA software which is part of the IFFEFIT package  [41]  in order to obtained the normalized XANES spectra. The NAP-XPS experiments were carried out at the CIRCE beamline of the ALBA Synchrotron Light Source. [42] The acquisition was performed using a PHOIBOS 150 NAP electron energy analyser (SPECS GmbH) equipped with four differential pumping stages and a set of electrostatic lenses which enable the performance of XPS measurements with the sample at pressures from ultrahigh vacuum (UHV, with a base pressure of 10 -10 mbar) up to 20 mbar. Most of the experiments were performed at a chamber pressure of 1. 5×10 -7 mbar, which we call High Vacuum (HV). All NAP-XPS measurements have been acquired with 1350 eV photon energy.
Acknowledgements This research used resources of the  Brazilian Synchrotron Light Laboratory (LNLS) , an open national facility operated by the  CNPEM for the Brazilian Ministry for Science, Technology, Innovations and Communications. The  XAFS2  beamline staff is acknowledged for the assistance during the experiments (proposals 20160754 and 20170907). The authors thank the support of  ALBA  staff for the successful performance of the measurements at CIRCE beamline (proposal 2018093158) of the  ALBA Synchrotron Light Source. The STEM studies were performed at the  DME-UCA  node of the  National Unique Infrastructure for Electron Microscopy of Materials, ELECMI. CESGA (Galicia, Spain)  and  CTI (CSIC)  supercomputer centers are acknowledged for providing computational resources. Funding: This work has been partly supported by the  Spanish Ministerio de Ciencia e Innovación  ( TED2021-131899B-I00/MCIN/AEI/10.
13039/501100011033/Unión Europea NextGenerationEU/PRTR ), and the  Agencia Estatal de Investigación (AEI)  and the  Fondo Europeo de Desarrollo Regional (FEDER, UE)  under Grants Nos,  PID2019-107115GB-C21 , and  PID2020-117605GB-I00 ; the  EU Doctoral Network   PHYMOL 101073474  (project call reference  HORIZON-MSCA-2021-DN-01 ); the  Austrian Science Fund (FWF)  under Grant  P29893-N36 ;  XUNTA DE GALICIA (Spain) (Grupos Ref. Comp. 2021 -ED431C 2021/16 );  ANPCyT PICT  ( 2017-1220  and  2017-3944 ) and  UNLP  (Project  11/X937 ), Argentina. This publication is also based upon work of  COST Action   CA21101  "Confined molecular systems: from a new generation of materials to the stars" (COSY) supported by  COST (European Cooperation in Science and Technology).
, 2022)  including a reduction of epipelagic stocks and disruption of diel vertical migration of mesopelagic organisms  (Ariza et al. , 2014)  and species richness decline during the eruptive phase  (Ferrera et al. , 2015). The present work focuses on biological and optical properties extracted by Sea Data Analysis Software (SeaDAS) applied to a time series of National Aeronautics and Space Administration (NASA) satellite images acquired pre-and post-eruption (15 January -2 February 2022). Importantly, the study aims at explaining the apparently high Chl-a values occurring post-eruption. We hypothesize that the origin of the material expelled by the volcano and the elevated Chl-a values are not related to phytoplankton growth but rather to the ash itself deposited in the upper ocean and, consequently, is the result of an erroneous calculation due to a specific optical signature confounded with the Chl-a signature.
Satellite data (MODIS, VIIRS and OLCI) Ocean color products of NASA and Copernicus Marine Environment Monitoring Service (CMEMS) were used in this study. A series of ocean color images from 14 January to 2 February 2022 were examined. Daily retrievals of satellite images from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites at spatial resolution of 1 km and Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi-NPP at 750 m were selected and extracted from NASA's Ocean Color website (Table  1 ). Level 2 (L2) images were created using the NASA SeaDAS software package. For higher resolution viewing, a true color RGB Sentinel-2 mosaic image at a 60 m resolution acquired on 17 January 2022 was taken from the Copernicus Open Access Hub and visualized using the Sentinel Application Platform (SNAP) package (Figure  1A ).
Satellite data analysis tools Individual MODIS/VIIRS images were extracted and reprojected onto a Plate Carreé grid to allow comparisons for each date by applying the Cartopy geospatial data processing package into Python 3. As in  Whiteside et al. (2021) , we examined various daily products derived from the L2 product in SeaDAS using standard MODIS satellite processing. These included the Rrs suite and inherent optical properties in MODIS and VIIRS, i. e. , Rrs (Rrs 443, Rrs 551 or 555, 671 or 678 nm in sr - 1 ), backscattering coefficient at 443 nm, b bp (443) ("b bp_giop " GIOP), Chl-a, K d (490) and normalized fluorescence line height or nFLH. It should be noted that caution must be taken with K d values presented in this paper, which were derived from Rrs ratios in the blue and green using the operational NASA algorithm based on  (Austin and Petzold, 1981).
05 to 0. 12 m -1 at the center of the patch) were lower in comparison to MODIS (0. 05 to 0. 2 m -1 ), and this discrepancy can result from both inversion algorithms and/or from the spatial averaging. Using SeaDAS software, all MODIS-VIIRS optical parameters (Rrs, b bp , Chl-a, K d , nFLH) were extracted at   1C ), to illustrate the distribution of the patch for each day in January from east to west and north to south. The diagrams were generated using the Matplotlib visualization library and Xarray package in Python 3. Time-evolution of the penetration depth (1/K d ) average values along the horizontal line transect was presented over time, and over longitude for the same period as the Hovmöller diagrams. It was checked that the same transect could be kept throughout the post-eruption study period as the patch moved sufficiently slowly.
Then, the penetration depth (1/K d (490)) was used to give an estimate of the volume of water impacted by the total amount of material deposited using the formula: Volume of patch = o 1 K d x pixel width x pixel length where the discolored patch was present. Daily mean surface current velocities were superimposed onto the b bp L2 images on a Plate Carreé grid with the already available 'quiver' and 'quiverkey' functions within the Matplotlib library and generated in Python, displaying ocean current direction in meters per second (m. s -1 ). This function allowed superimposing 9 km x 9 km current data over 4 km x 4 km pixels.
CMEMS nutrient and ocean surface current data To further evaluate whether the high levels in the Chl-a band around the volcano (described in Section 3) were due to an unusual phytoplankton bloom (despite the time lag seeming inconsistent with growth) a modeled forecasted dataset from the CMEMS database for 4 types of nutrients were explored to identify whether available pre-eruption nutrients allowed phytoplankton growth. These nutrient parameters included dissolved iron (Fe(II)), nitrates(NO 3-), phosphate (PO 4 ), and dissolved silicate (Si(OH) 4 ). Daily mean levels of Fe(II), Si(OH) 4 , PO 4 and NO 3-were extracted from CMEMS over a 4-month period from December 2021 to March 2022. The dataset is part of the output of a biogeochemical global ocean analysis and forecast system at 1/4-degree resolution that provides files of daily and monthly mean biogeochemical variables.
Note that there is no evidence of a fluorescence peak in the discolored water patch. One would expect a dip at 667 nm due to Chl-a absorption, but the Rrs values at 667 nm and 678 nm are similar in the brown curve, even slightly lower at 678 nm, suggesting that the baseline reflectance at 678 nm, estimated from Rrs values at 667 and 748 nm, was too low. This might be due to an underestimation of the Rrs at 748 nm in the iterative scheme designed to account for non-negligible Rrs in the near infrared  (Bailey et al. , 2010). In that scheme, the slope of the backscattering coefficient in the red and near infrared is estimated empirically using a dataset  (Werdell and Bailey, 2005)  that did not include the type of waters with volcanic ash considered in the present study. Table  2  summarizes the optical properties of discolored waters pre-(29 December 2021), and post-eruption (2 days after, i.
Confusion with an atmospheric patch Confirmation that the observed plume was not a result of atmospheric release through the observation of sulfur dioxide (SO 2 ) was given by the TROPOspheric Monitoring Instrument (TROPOMI). The TROPOMI data showed that a SO 2 patch over Tonga was much greater than the oceanic patch and had drifted to the west in less than two days toward Fiji and Australia (https://so2. gsfc. nasa. gov/). Within four days of the eruption, no SO 2 signal could be recorded over the area of the discolored water patch.
Comparison with the 2019 eruption It is remarkable that discolored water signatures of the 2022 eruption are different from the ones in 2019. The 2019 eruption was accompanied by a spread of discolored waters which could be delineated and followed for 2 weeks  (Whiteside et al. , 2021)  in addition to large consolidated and fragmented pumice rafts. The spectral signature of the discolored waters was described as "milky", as previously reported in  Mantas et al. (2011). In 2019, Rrs in the 443-channel appeared higher than normal and calculated Chl-a was lower than the surrounding environment. Such discolored oceanic patches ("milky waters") were well visible from December 2021 on the NASA Worldview Earthdata website (https://worldview. earthdata. nasa. gov/), as first indications of eruptive events, following atmospheric effects above the HTHH volcano on 20 December, 22 December 2022.
All the proposed Chl-a algorithms, however, have not been established using data collected in waters contaminated by volcanic ash and are not expected to provide reliable results in such situations. It is therefore highly probable that those are incorrect Chl-a values, and that ashes themselves were controlling the ocean color signature. To provide additional evidence about the non-biogenic nature of the highly reflective patch, we compared the b bp (443)/Chl-a values retrieved by SeaDAS software from the various Rrs spectra of January 17 (Figure  3 ) to those of the  (Morel and Maritorena, 2001)  model developed for Case 1 waters, the type of waters encountered around Tonga. The average satellite-derived and model b bp (443)/Chl-a values were 0. 0187 ± 0. 0002 m 2. mg -  and 0. 0023 ± 0. 0001 m 2.
Acknowledgments The authors thank  NASA  for making available the MODIS/ VIIRS Aqua data and the SeaDAS data processing software. We acknowledge CMEMS Copernicus data (modeled currents, modeled nutrients, interpolated data of Kd and bbp). We deeply thank  Geo-ocean (GO) -UMR 6538 Institut Universitaire Europeen de la Mer (IUEM)  and  LAMA (Laboratoire des Moyens Analytiques of UAR IMAGO) at the Center IRD of Noumea (New Caledonia ) for ash analyses. We also acknowledge.
Methods Sea Hero Quest game design. The research questions were tested using a large-scale dataset collected via Sea Hero Quest-a mobile game application designed to obtain benchmarks of typical spatial abilities and navigation behaviours of healthy populations worldwide. The game contains eighty wayfinding, path integration, radial maze and other, research-unrelated, game engagement levels along with additional optional socio- demographic questions which asked players about their age, gender, country, education, rating of their navigation skills, hand used for writing, the environment they grew up in as well as their average daily travel time and a typical amount of sleep at night. A detailed description of the game design is provided in Coutrot et al. 19. The ecological validity of Sea Hero Quest has already been confirmed by Coutrot et al.
63  who found that the navigational performance recorded using the game reflects real-life wayfinding behaviour. The full Sea Hero Quest dataset contains behavioural and socio-demographic data collected between April 2016 and April 2019 from over 4. 3 million players globally with over 50 million gameplays recorded across all wayfinding and path integration tasks. Access to the full dataset is described in the Data and code availability statement of this manuscript. Informed consent and ethical considerations. Informed consent was obtained from the participants. The starting screen of the Sea Hero Quest game included debriefing and informed consent sections with clearly identified goals of the study as well as the purpose and extent of data collection. Additionally, the full explanation of the study was provided within the gameplay (via the 'journal' icon) and the consenting players could access this section and withdraw from playing at any point of the game.
All consenting participants voluntarily downloaded and played the Sea Hero Quest mobile application game. This study was conducted as part of a larger research project which has been approved by the UCL Ethics Research Committee under the Project Number: CPB/2013/015. All experiments and methods including data collection, processing and analysis were carried out in accordance with relevant guidelines and regulations. Participants and sample size. A subset of 383,187 individuals (representing 46 countries; N Females = 172,969, MeanAge Females = 37. 08 ± 14. 53, N Males = 210,218, MeanAge Males = 37. 14 ± 13. 39) out of the total number of over 4. 3 million people who downloaded and played the Sea Hero Quest mobile game application was used in the analysis. The sample size was restricted by selecting only those players who provided the self-estimate scores of their navigation skills along with other socio-demographic information such as age and gender and by narrowing the age range of individuals to between 19 and 70 years old in order to control for fake age demographics as well as previously reported selection bias  19  which was found to translate to an unusual spike in performance in older players.
These two data processing operations lead to 34,065 participants excluded from the sample. Finally, we removed gameplays of individuals representing countries (e. g. Vietnam, Albania, Saudi Arabia, and Croatia) which didn't feature in any of the cultural clusters described in the Cultural clustering of countries subsection below (2454 excluded). Following these data cleaning activities, the resulting final sample size for testing the hypotheses was 383,187 individuals representing 46 countries. The country-by-country descriptives of the sample are provided in the Supplementary Information, Appendix A, Table  S1.
This numeric-type measurement of navigation skills was applied to quantify the average (arithmetic mean) self-estimates for each gender, age band and country as well as to calculate the country-specific gap between the self-reported ability and measured wayfinding performance. The average self-estimated navigation skills for each country and cultural cluster in the sample were estimated with conditional modes by extracting the intercepts for 46 countries in the sample and 11 cultural clusters from linear mixed models with fixed effects for age and gender and random effect for country (or cluster, respectively): Self-Estimated Navigation Skills ~ Age + Gender + (1|Country) and Self-Estimated Navigation Skills ~ Age + Gender + (1|Cluster). We used lme4  65  and lmerTest 66 packages for the R programming language to implement these models.
Finally, as the estimated metric was in fact a mean distance travelled from the origin to the destination (i. e. the smaller the value, the better the performance), we reversed its sign for easier interpretation (i. e. the larger the value, the better the performance). It represented a single and unbiased measurement of one's wayfinding performance across multiple wayfinding tasks. Wayfinding performance for countries and cultural clusters was estimated with conditional modes by extracting the intercepts for 46 countries in the sample and 11 cultural clusters from linear mixed models with fixed effects for age and gender and random effect for country (or cluster, respectively): Wayfinding Performance ~ Age + Gender + (1|Country) and Wayfinding Performance ~ Age + Gender + (1|Cluster). We used lme4  65  and lmerTest 66 packages for the R programming language to implement these models.
Acknowledgements This manuscript was written as part of the Ph. D. research conducted by S. W. whose work was funded by the  Alan Turing Institute  (Grant No. 3340710 ) in London. S. W. would like to thank the  Alan Turing Institute  for academic support and financial contribution during his doctoral research. Furthermore, the authors wish to thank  Deutsche Telekom  and  Alzheimer's Research UK  ( ARUK-DT2016-1 ) for funding the research and analysis based on the  Sea Hero Quest mobile game application. Finally , the authors are grateful to  Glitchers Limited  for the SHQ game production, and to  Saatchi & Saatchi London  for the project management and creative input.
Data availability Access to the full Sea Hero Quest dataset (including all wayfinding trajectories generated by the players) is available upon registration through a dedicated server at https:// shqda ta. z6. web. core. windo ws. net/. Please contact the Lead Contacts directly (H. J. S. and E. M. ) to obtain the sample of the full dataset used in this study. Lead Contacts: Hugo J. Spiers (h. spiers@ucl. ac. uk) and Ed Manley (e. j. manley@leeds. ac. uk). Additionally, researchers interested in using the Sea Hero Quest mobile game application can invite participants to play the game and generate wayfinding research data for non-commercial purposes via the SHQ game portal at https:// seahe roque st. alzhe imers resea rchuk. org/. Access information to secondary data sources can be obtained from the following URLs: Hofstede's cultural dimensions (2016)-https:// geert hofst ede.
com/ resea rch-and-vsm/ dimen sion-datamatrix/. Gallup World Poll 2005-2017 (Life Satisfaction)-https:// www. gallup. com/ 178667/ gallup-world-pollwork. aspx. Gallup Religiosity Index (2009)-https:// ratio nalwi ki. org/ wiki/ Impor tance_ of_ relig ion_ by_ count ry. United Nations Development Programme (Education Index 2018, Gender Development Index 2019, and Gender Inequality Index 2018)-http:// hdr. undp. org/ en/ data. Global Gender Gap Index 2020-https:// www. wefor um. org/ repor ts/ gender-gap-2020-report-100-years-pay-equal ity. GDP per capita (2019)-https:// data. world bank. org/. All data pre-processing operations, statistical analyses and figures included in this manuscript were made using the R and Python programming languages. The custom code scripts are available from the Lead Contacts (H.
Context. Comparing theoretical predictions to real data is crucial to properly formulate galaxy formation theories. However, this is usually done naively considering the direct output of simulations and quantities inferred from observations, which can lead to severe inconsistencies. Aims. We present FORECAST, a new flexible and adaptable software package that performs forward modeling of the output of any cosmological hydrodynamical simulations to create a wide range of realistic synthetic astronomical images, and thus providing a robust foundation for accurate comparison with observational data. With customizable options for filters, field-of-view size, and survey parameters, it allows users to tailor the synthetic images to their specific requirements. Methods. FORECAST constructs a light cone centered on the observer's position exploiting the output snapshots of a simulation and computes the observed flux of each simulated stellar element, modeled as a single stellar population, in any chosen set of passband filters, including k correction, intergalactic medium absorption, and dust attenuation.
These fluxes are then used to create an image on a grid of pixels, to which observational features such as background noise and PSF blurring can be added. This allows simulated galaxies to be obtained with realistic morphologies and star formation histories. Results. As a first application, we present a set of images obtained exploiting the ILLUSTRISTNG simulation, emulating the GOODS-South field as observed for the CANDELS survey. We produced images of ∼200 sq. arcmin, in 13 bands (eight Hubble Space Telescope optical and near-infrared bands from ACS B435 to WFC3 H160, the VLT HAWK-I K s band, and the four IRAC filters from Spitzer), with depths consistent with the real data. We analyzed the images with the same processing pipeline adopted for real data in CANDELS and ASTRODEEP publications, and we compared the results against both the input data used to create the images and the real data, generally finding good agreement with both, with some interesting exceptions which we discuss.
As part of this work, we have released the FORECAST code and two datasets. The first is the CANDELS dataset analyzed in this study, and the second dataset emulates the JWST CEERS survey images in ten filters (eight NIRCam and two MIRI) in a field of view of 200 sq. arcmin between z = 0-20. Conclusions. FORECAST is a flexible tool: it creates images that can then be processed and analyzed using standard photometric algorithms, allowing for a consistent comparison among observations and models, and for a direct estimation of the biases introduced by such techniques.
2023 ), FORECAST adopts a particle-based approach, translating the physical properties of individual resolution elements (particles or cells) into observed fluxes. This approach enables the creation of simulated images with realistic galaxy morphologies, interactions, and star formation histories, improving upon standard image simulation software tools, which typically adopt analytical functional forms to render galactic light profiles; readers can refer to GALSIM  (Rowe et al. 2015) , SKYMAKER  (Bertin 2009) , and SKYLENS  (Plazas et al. 2019) , for example. The use of forward modeling techniques based on numerical simulations is a well-established practice in the literature. They have been employed to assess the reliability of photometric methods  (Price et al. 2017; Parsotan et al. 2021 ) and to evaluate the performance of SED fitting  (Laigle et al.