dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Raw and processed bulk and single-nucleus RNA-seq data have been deposited in ArrayExpress with the accession codes E-MTAB-11063 (human snRNA-seq), E-MTAB-11064 (chimpanzee snRNAseq), E-MTAB-11067 (bonobo snRNA-seq), E-MTAB-11065 (gorilla snRNA-seq), E-MTAB-11066 (gibbon snRNA-seq), E-MTAB-11068 (macaque snRNA-seq), E-MTAB-11069 (marmoset snRNAseq), E-MTAB-11071 (mouse snRNA-seq), E-MTAB-11072 (opossum snRNA-seq), E-MTAB-11070 (platypus snRNA-seq), E-MTAB-11073 (chicken snRNA-seq) and E-MTAB-11074 (chimpanzee, gorilla, gibbon and marmoset bulk RNA-seq) (https://www. ebi. ac. uk/arrayexpress/). All other data are available as supplementary information or available upon request. The testis gene expression at the single-nucleus level across the eleven studied species can be visualized using the shiny app we developed: https://apps.
Acknowledgements We thank all members of the Kaessmann group,  Stephan Tirier  for discussions, and  Nils Trost  for the administration of the Kaessmann lab server. Computations were performed on the Kaessmann lab server and the bwForCluster from the  Heidelberg University Computational Center  (supported by the state of  Baden-Württemberg  through bwHPC and the  German Research Foundation - INST   35/1134-1 FUGG ). This research was supported by grants from the  European Research Council  ( 615253 , OntoTransEvol) and  German Research Council (DFG ,  SFB 873 ) to H. K. , by the  CellNetworks Postdoc Fellowship  and  EMBO Long-Term Fellowship  to F. M. ( ALTF 591-2017 ), and by the  Australian Research Council  ( FT160100267 ) to F. G.
Description of the participatory design toolkit In the previous section, we detailed LAD design space, as a framework to organize decisions to be made. To be effective, a participatory design method needs to be properly instrumented. We therefore need to identify tools that can support dashboard users in better expressing their expectations and needs. In this section, we propose to combine suitable tools according to the dimensions of the design space to foster meaningful participatory creativity. Our aim is to provide support for collaborative workshops. We distinguish three phases to support the process: (1) Identification of the LAD's context and goal; (2) Data & Visualisation to explore useful data; and (3) Sensemaking Sketchup to explore LAD organisation and interaction to support sensemaking.
These phases are materialized by dedicated boards that group various cards representing the dimensions of the design space (figure  2 ). Identification board. This board gathers the Who?, When? and Why? questions. A persona form enables to personify the stakeholders and to collect relevant information, such as expertise, visual literacy, or motivation. The goal being pivotal to guide the ideation process, we dedicate a specific domain card to support goal expression. Participants are invited to express their own goal and to relate it to focus and situation awareness level. Those lists may be adapted to participant profiles, being expressed differently whether they are learners, teachers, or other types. Additional domain cards are proposed to inform additional information: Circulation, answer to When? question, Data Scope.
Data & visualization board. This board is intended to answer to the What? question. Participants are invited to identify relevant data that are useful to attain their goal. They fill a specific data card for each. They are also invited to associate visualisation they feel relevant. For this, a set of technology cards proposing classical visualizations is provided. Sensemaking Sketchup board. As LAD is a visual object, participants are willing to sketch a representation of aimed LAD. Proposing a sketching phase in a participatory workshop is essential. Sensemaking is supported in three ways. First, in order to foster browsing of the different levels of Situation Awareness, mockups of different colours have been provided: red for monitoring, blue for analysing and green for action. Participants are invited to associate data and visualisations to the different mockups.
First, copy-paste operations in texture synthesis require that the source terrain be indexed in an acceleration database: a pre-processing step that can take several minutes. Second, any operation outside the texture synthesis toolchain effectively puts a halt to further texture edits, making it difficult to switch between paradigms. Finally, there are issues of extensibility. For instance, our approach allows gradient constraints to be set independently of elevation (see Figure  4 ). An equivalent tool does not exist in [GMM15]'s system and would require a redesign of the underlying texture synthesis process. In contrast, gradient authoring benefits from ease of toolset configurability and seamless interoperation with other authoring paradigms. Table 2 (see Appendix) provides a feature-wise comparison of existing terrain authoring systems, including our approach.
Example-based cGAN synthesis While Conditional Generative Adversarial Networks (cGANs) have found success in the example-based synthesis of terrains from sketches [GDG * 17], our experiments indicate (see Figure  9 ) that substantial improvement can be achieved by switching from elevation images to gradient images. Adding the input sketches as constraints emphasizes local relief and leads to even better results. The overall reduction in cGAN artefacts can be explained, in part, by the elevation invariance of gradient images leading to improved pattern matching. In these experiments, we used the identical set-up of a pix2pix cGAN using two datasets: pairs of sketch/elevation maps, and pairs of sketch/gradient maps. We used a total of 135 pairs of size 512 × 512 cropped to 256 × 256 resolution to train over 600 epochs.
So, we remove the introduced bias by adjusting the level of each 4 × 4 patch of the super-resolution output so that its average matches the initial low-resolution input. This bias is equal to g l -g h where g h and g l are the high-and low-resolution gradients, respectively and g h is the mean gradient of the patch. This enables a better correspondence with the initial low-resolution terrain, as can be seen in Figure  13. Training required a dataset of 440 pairs of low resolution 128 × 128 gradient images and high resolution 512 × 512 gradient images, a random crop of size 24, a VGG54 perceptual mode, and a total of 200 k iterations.
Implementation and results We implemented our algorithms in C++ and OpenGL using the compute shader, which enables real-time editing of terrains. Performance was measured on an Intel i7 R CPU clocked at 4GHz and equipped with 32GB RAM and an NVidia GeForce R GTX 970 GPU with 4GB RAM. Reconstructed terrains were directly streamed to E-On Vue R to produce the photorealistic renderings shown throughout the paper. The source code for our multigrid solver is available at https://github. com/ eric-guerin/gradient-terrains.
This demonstrates the value of dimensional elevation data as compared to greyscale images. The second set of qualitative user tests was conducted with 7 non-artists. They were tasked with producing three scenes based on short textual descriptions, each within a 10 minute time limit. The scenes, assigned in random order so as to minimize any learning effects, were: an open volcano with a breach in the caldera, a Y-shaped canyon, and a mountain range with two prominent peaks. Demonstration videos were embedded with the application to explain the controls, and an expert was on hand to provide answers and help where required. For all experiments, we collected both the participants' final terrains (see Figure  17 ) and qualitative feedback regarding the strengths and weaknesses of the different tools and their general impressions.
Acknowledgments This work was part of  the  project  AMPLI ANR-20-CE23-0001 , supported by  Agence Nationale de la Recherche Française , and also by the  National Research Foundation of South Africa  (Grant Number:  129257 ). We thank  Matthieu Beaud  for the early developments of the editing interface. We also credit  Benoît Martinez  and  Mathieu Gasperin  from  Ubisoft  for their useful feedback on the user interface.
The date allows to take into account the solar modulation described by Papini et al. [34]. It is possible to use pre-calculated tables from GEANT4 to take into account the configuration of the detector  [35]. CRY has limitations when you investigate multi-track events in a cosmic ray experiment or identifying background events in muography. • CORSIKA (COsmic Ray SImulations for KAscade)  [36, 37]  is a Monte Carlo code for simulating atmospheric particle showers initiated by high-energy cosmic ray particles. Primary particles (protons or light nuclei) are tracked in the atmosphere until they interact, decay or are absorbed. All secondary particles are explicitly followed along their trajectories and their parameters are stored when they reach an observational level. All the models presented above have their own particularity and the choice of the fluxes generator is essential.
Standard Use Cases Several developments use CORSIKA for the muon fluxes simulation and for various applications. In Biallass et al. [39] , they based their parametrization of the differential muon flux at ground level on a flux obtained from the air shower simulation program CORSIKA. According to Wentz et al. [40] , the number of simulated particles should be normalized by taking into account the primary flux. In Mitrica et al. [41] , they used this generator to determine accurately the overburden in mwe (meter water equivalent) of an underground laboratory. Pethuraj et al. [42]  used CORSIKA to generate secondary particles at an experimental site where atmospheric neutrinos oscillations are studied in order to assess the long-term stability of their RPCs detectors. They studied the azimuthal dependence of the muon flux at different zenith angles and they compared they results using CORSIKA with Honda et al.
Hadronic Interaction Models When a primary particle reaches the top of the atmosphere, it undergoes "hadronic" interactions leading to the production of secondary CR and among those particles, pions and kaons decay into muons. Different types of primary particle interaction models are available on CORSIKA. We chose FLUKA for the low-energy interactions and QGSJET-II-4 for high energies, the best candidates in their energy domains for our expected accuracy and computational time. To be more specific, FLUKA is a hadronic interaction model of low energy particles used by Usokin et al. [51] , Apel et al. [52]  and Heck et al. [53]. Its main strength is to allow for rapid simulation. QGSJET is a high energy hadronic interaction model. It has a high level of sophistication, approximates the data very well but is a bit slow especially because it allows few free parameters and deals with the effect of saturation by partons  [54].
Atmosphere Parameters An input to CORSIKA is the state of the atmosphere in which the CR ray showers are generated. The CORSIKA atmosphere model is composed of N 2 , O 2 and Ar with volume fractions of 78. 1%, 21. 0% and 0. 9%, respectively. The density variation of the atmosphere with altitude is modeled by 5 layers. The state of the atmosphere is described by the density of the air at each altitude level. This parameter is calculated by converting the relative humidity into saturation vapor pressure with the Magnus formula  [61]. We computed the parameters and altitudes of the layer boundaries from ERA5 data, the latest climate reanalysis produced by the ECMWF (European Centre for Medium-Range Weather Forecasts), which combines large amounts of meteorological observations with estimates made from advanced modeling and data assimilation systems  [62].
Comparison with Real Data The best cross-checks for any simulation are the comparison to real data. Data presented here were taken in Lyon (France, latitude 45°and close to the sea level), in open sky conditions, with a 3-planes muon tracker (so-called muon telescope). We tilted the telescope progressively by step of 15°from the zenith (θ = 0°) to the horizontal (θ = 90°) directions. Multi-tracks are suppressed, electrons are partly suppressed by the multiplicity cuts on the detection planes, but there is no PID in the system. The muon flux was simulated in Lyon (France) respecting the geodesic constraints. Figure  7  displays the data/simulation comparison. Experimental points are represented with crosses in blue and the flux from CORSIKA's simulations is represented in black. A fit was made on the real dataset with a simple cos 2 (θ) at first order (in red).
Geodesic and Atmospheric Factors In this section, we used our CORSIKA model, validated on a large dataset to simulate muon fluxes for various particle detectionaltitude, Earth magnetic field B and atmosphere conditions. We wanted to quantify those effects on the measurable muon flux at open sky conditions. To achieve this, we varied one parameter at the time while keeping the others constant. All the results presented in this section are subject to significant uncertainty. It is statistical and increases with the energy. Indeed, we simulate much less high-energy muons and extreme-angles muons. We have limits imposed by computational time and linked to the randomness of CORSIKA. Our errors are all set to 1σ.
Atmospheric Thermodynamics Effects We have tested the effects of altitude and magnetic field and their components on the muon flux. Finally we studied the impact of atmospheric density on the flux, which is ultimately controlled by the temperature and pressure state of the atmosphere. Hence, at the same location, seasonal variations affect the muon flux over time. For these tests we chose Dallol (Ethiopia) and Oimiakon (Russia) to test the effects of extreme temperature. These atmospheres are presented in the next subsection. The atmosphere density parameters used to simulate muon fluxes with CORSIKA are determined by using ERA5 datasets.
Conclusions In this paper, we presented a muon flux simulation workflow accounting for muonatmosphere interactions, based on the CORSIKA framework. We detailed our simulation strategy and the various relevant inputs from the hadronic interactions models to the atmosphere conditions. In particular, we used meteorological ERA5 pressure and temperature datasets to compute the required atmospheric density profiles. The workflow has been cross-validated against experimental evidences and standard semi-empiric models found in the literature. Simulations prove themselves to be a powerful tools to study and make predictions on tiny effects induced by the geomagnetic field, detection altitude or the atmospheric seasonal variations. The altitude effect seems to be the most important. In Lyon for example, at 1000 m altitude, the flux is 17% higher than at sea level.
g. Huss, 2012). In this context, it is important to note that since the uncertainties in glaciological mass balance series are cumulative, they can cause large biases if not corrected with geodetic estimates over longer periods  (Cox and March, 2004; Thibert et al. , 2008; Zemp et al. , 2010 Zemp et al. , , 2013)). For Swiss glaciers, such corrections have generally been based on mass changes derived from topographic maps before 1960 and later on from detailed geodetic surveys based on aerial imagery. However, uncertainties in the former datasets are large and sometimes difficult to quantify, especially for the first half of the 20th century. Modern reanalysis of the source material for these early maps, thus, holds great potential for further constraining the magnitude and lowering the uncertainty in estimates for past glacier changes.
About 86 % of the glacierised area is covered by at least two historical images from different physical locations, thus setting a constraint to the maximum theoretical coverage of the dataset. The actual coverage is lower as the incidence angle (i. e. the difference between the angle of the image and the terrain slope) is often low due to the photographs being acquired from the ground. This makes it difficult for feature-matching algorithms to obtain accurate results, thus lowering the coverage. The 13 cm × 18 cm terrestrial images contained in the TerrA archive are scanned by swisstopo at a resolution of 21 µm, yielding digital images of 53 Mpx. The metadata of each image include an identifier for which stereo pair they belong to, the acquisition date, the position, and the viewing direction of the image as determined from field measurements.
The photographs used in this study were acquired with 21 individual cameras between 1916 and 1947. Most photographs were acquired in the 1920s and 1930s, with a median year of 1931 and a standard deviation of 5 years. The cameras were of two different brands called Wild and Zeiss, which had different focal lengths, image dimensions, and frame geometry (Fig. 2c, d ). Within the 2008 "Action Plan for the Preservation of the Spatially Relevant Cultural Heritage of swisstopo", it was decided to preserve the images in the TerrA archive, to scan them all, and to make them available to the public  (Ryf and Klöti, 2008; Rickenbacher, 2012). After conceptual preparations, swisstopo started the project in 2013 and announced the release of the dataset in 2018 (swisstopo, 2018). To our knowledge, no scientific publication has used this unique digital dataset to date.
Auxiliary data Our processing workflow (see Sect. 3) requires a modern DEM as a reference for co-registration, as means of validation, and for calculating surface elevation changes with respect to the DEMs generated from the TerrA archive. We use the SwissALTI3D DEM  (swisstopo, 2019)  for this. This DEM is a 2 m × 2 m mosaic of lidar (below 2000 m a. s. l. ) and digital aerial photography. Over glaciers, acquisition years range from 2007 to 2018, with a median year of 2016. The reported absolute accuracy of the DEM is between 0. 1 and 3 m, depending on the location. To exclude areas of possible elevation change over the 85-year period, we mask glaciers, perennial snowfields, and lakes. To do so, we use (i) the glacier and snow mask that  Freudiger et al. (2018)  digitised from the "Siegfried maps" made in  1917-1944 and (ii)  the swissTLM3D product to mask natural and artificially dammed lakes  (swisstopo, 2020).
The TerrA images were previously used to create the first edition of 1 : 50 000 topographic maps (the so-called "LK50") in the alpine regions of Switzerland  (swisstopo, personal communication, 2021). We use georeferenced scans of these maps to derive glacier outlines for all glaciers, which are proven here to be concurrent with the TerrA dataset. To digitise glacier outlines from the LK50 map series (see Sect. 3. 1. 3 for details), we use glacier outlines from the Swiss Glacier Inventory 1973 (SGI1973;  Müller et al. , 1976)  as a template and follow the same naming convention for glacier identification. The SGI1973 is based on orthophotographs acquired in 1973 and represents the first accurate and complete mapping of Swiss glaciers. Glacier outlines corresponding to the SwissALTI3D DEM are taken from the SGI2016  (Linsbauer et al.
Fiducial mark detection We use fiducial marks (small markers on the image frame as fixed references; red squares in Fig. 2c  and d ) to constrain the centre point and internal orientation of the imagery. The positions of the fiducial marks are used to calculate a transformation between the pixel coordinates and physical image coordinates. Undesired translations, rotations, or scaling effects introduced during scanning are removed though a similarity transformation estimated using the Python package "scikit-image" (van der  Walt et al. , 2014). Each image has four or more fiducial marks along its edges, with a size of about 350 px (∼ 7 mm). A theoretical minimum of two fiducial marks need to be identified, but more fiducial marks introduce redundancy and allow for exclusion of uncertain fiducial marks.
We use a semi-automatic model to identify fiducial marks in the 21 703 images processed in the study. To calibrate the model, we manually identify fiducial marks in 3395 images (∼ 162 images per camera). Such a large calibration set was required because the 21 unique cameras in the dataset had slightly or completely different fiducial marks. We then use the manually identified fiducial marks to calibrate the model to do the same on the remaining 81 % of the image archive. Using template matching to identify the fiducial marks, the model estimates a similarity transformation and validates it using the residual differences between the identified fiducial marks and the modelled ones. We use random sample consensus (RANSAC) to exclude fiducial marks with residuals of more than 10 px (0. 21 mm).
Image position correction A preliminary analysis of the digitised image metadata revealed spatially correlated biases in the image positions provided by swisstopo. We observe this by calculating the elevation difference between the image position and the SwissALTI3D elevation on stable ground. While this difference is expected to be the height of the tripod (∼ 1. 2 m; swisstopo, personal communication, 2021), the calculated differences has an absolute bias of 2. 66 ± 5. 91 (mean ± SD) after taking the tripod height into account and an average horizontal offset of 0. 66 ± 4. 49 m. A possible explanation for these biases is that camera positions were estimated by triangulation from reference points, which in turn were all positioned relatively to other reference points. Therefore a small positional error could accumulate as the point is further away from the main reference.
By sampling slope, aspect, and elevation values from the reference DEM at every recorded image position, we estimate the approximate 3D offsets from the image position data to the ground. We then partially correct the systematic component of the biases by averaging them in 1 km × 1 km grids and subtract the gridded offsets E. S. Mannerfelt et al. :  Halving of Swiss glacier volume since 1931 from the 3D positions of the images. This correction reduces the average difference between the DEM and the imaging locations to 0. 70 ± 5. 18 m (0. 39 ± 4. 15 m horizontally).
LK50 map series glacier outline digitisation We obtain glacier outlines that are concurrent with the TerrA dataset by digitising outlines on the scanned and georeferenced LK50 map series (in the Swiss CH1903+/LV95 coordinate system; EPSG:2056) provided by swisstopo. Digitising glacier outlines from orthoimages generated in this study was also a theoretical possibility, but sporadic image coverage and time constraints led to the search for more efficient approaches. These orthoimages are thus only used to draw outlines for 61 sites, which are in turn used to validate the glacier outlines obtained from the LK50 series (see Sect. 3. 5. 2). The LK50 maps, made in the 1950s, cover all of Switzerland and are based on the TerrA photographs among other data. We manually digitise the glacier outlines of the LK50 maps by modifying the 2491 outlines (with a mean area of 0.
60 km 2 ) of the Swiss Glacier Inventory 1973 to fit the LK50 map data. This allows for the SGI1973 metadata to be easily retained and ensures consistency when including or excluding perennial snowfields as well as when drawing ice divides. We acknowledge that in this way, there is a potential underestimation of the change in the accumulation areas. Indeed, the outlines are only modified where they deviate from the SGI1973 outlines, meaning that minor deviations may be missed. We assume these potential discrepancies to be accounted for in our uncertainty analysis (Sect. 3. 5. 2). To reduce subjective error, outlines are drawn by one person and validation is performed by another.