dataesr/scientific-paragraphs-categorization huggingface dataset:
IntCal20 calibration curve  (Reimer et al. , 2020)  and the R package 'Clam' version 2. 2  (Blaauw, 2010). The time span of sedimentary units is: 13 100-12 000 cal a BP (Unit D), 12 000-3500 cal a BP (Unit C), 3500-430 cal a BP (Unit B) and after 430 cal a BP (Unit A). The calculated sedimentation rate shows two distinct periods. The first period with low sedimentation rate (<0. 2 mm yr -1 ) is in the lower part of the core between 13 100 and 600 cal a BP. After 600 cal a BP the sedimentation rate increased with the first peak (1. 6 mm yr -1 ) at 420 cal a BP. Note that this peak could also be artificially created by age-depth modelling. At 300 cal a BP the sedimentation rate dropped to 1. 2 mm yr -1 and gradually increased to 1. 8 mm yr -1 until the top of the core.
Acknowledgements. This research was funded by the  Slovenian Research Agency , programmes  P6-0064  ( Archaeological research )  P1-0195  (Geoenvironment and geomaterials) and  P1-0143  (Cycling of substances in the environment, mass balances, modelling of environmental processes and risk assessment), project  J7-1817  (5000 years of grazing and mining activities in the Julian Alps (Slovenia): climate-human interactions as reflected in lake sediments, manmodified landscape and archaeological findings) and funding a PhD project to  N. Caf. We would like to thank  M. -C. Bellinghery  and  K. Janeka  for the help on the coring campaign, and  T. Tolar  for identification of the macrofossils used for radiocarbon dating. We would also like to thank  U. Šilc  and  I. Sajko  for providing vegetation map (Fig.
We present a new approach for macromolecular structure determination from multiple particles in electron cryo-tomography (cryo-ET) data sets. Whereas existing subtomogram averaging approaches are based on 3D data models, we propose to optimise a regularised likelihood target that approximates a function of the 2D experimental images. In addition, analogous to Bayesian polishing and CTF refinement in singleparticle analysis, we describe approaches that exploit the increased signal-to-noise ratio in the averaged structure to optimise tilt series alignments, beam-induced motions of the particles throughout the tilt series acquisition, defoci of the individual particles, as well as higher-order optical aberrations of the microscope. Implementation of our approaches in the open-source software package RELION aims to facilitate their general use, in particular for those researchers who are already familiar with its single-particle analysis tools.
To deal with unknowns in the relative orientations of the particles from the tilt series images, as well as their CTFs, the program M recently introduced new optimisation approaches that compare reference projections against the 2D particle images  [21]. M still relies on RELION-3 for alignment and classification of 3D subtomograms that are recalculated from the optimised parameters in M. Nevertheless, this iterative approach allows subtomogram averaging to resolutions that approach those observed for single-particle analysis, even for particles in complex cellular environments  [21]. Here, we describe a new approach in RELION-4. 0 that approaches subtomogram averaging as the optimisation of a regularised likelihood function that depends directly on the 2D images of the tilt series. In order to do so at acceptable computational, as well as imple-mentation costs, we have altered the main refinement program in RELION-4.
HIV immature capsid We tested the workflow above on the cryo-ET dataset that was used to determine the structure of the immature capsid lattice and spacer peptide 1 (CA-SP1) regions of the Gag polyprotein from human immunodeficiency virus 1 (HIV-1)  [18]  (EMPIAR-10164). We used the same subset of 5 tomograms that were also used to assess the NovaCTF  [22]  and Warp  [20]  programs. Introducing 3D CTF correction, and using the alignment parameters from the original analysis by Schur et al, NovaCTF reported a resolution of 3. 9 Å  [22]. The Warp program introduced local and global motion correction in the tilt series images, as well as optimisation of CTF parameters. The combination of Warp and subtomogram alignment and averaging in RELION-3 led to a resolution of 3. 8 Å. We used tilt series projections after movie frame alignment from the original analysis  [18] , without any other preprocessing step, along with the tilt series alignment data, performed with IMOD package  [11] , and CTF parameters estimation using CTFFIND4  [15].
2 Å (Figure  1a ). Geometrical refinement was performed estimating local particle motion. Considering deformations did not show additional improvement. In the first cycle, where improvements in both CTFs and geometry are most obvious, the order of applying those optimisations did not alter the final result for this data set. These data and results are also distributed as part of the subtomogram tutorial in RELION-4. 0, as described on https://relion. readthedocs. io/en/release-4. 0/. Analysis of the complete dataset generated a structure at 3. 0 Å resolution (not shown), which is the same resolution obtained using the M and RELION-3 workflow  [21] , and is likely limited by flexibility and asymmetry in the CA hexamer.
0, followed by multiple cycles of pseudo-subtomogram generation and refinement, analogous to the immature HIV dataset described above, leading to a 5. 6 Å reconstruction of the S-layer hexamer (Figure  2a ). Next, we defined a mask around the central pore of the S-layer, corresponding to the inner domain bound to LPS, to perform focused refinements. Another cycle of pseudosubtomogram reconstruction, CTF refinement and refinement within the new mask improved the resolution to 4. 4 Å. Accounting for per-particle motions with additional cycles of pseudo-subtomogram improvements and refinements increased the resolution of the central pore to 4. 0 Å, and the inner domain of the S-layer to 3. 7 Å. Further 3D classification without alignments identified a subset of 42,990 subtomograms that gave a 3.
By using a 2D data model, missing wedge correction is no longer required. Instead, the problem approaches that of single-particle analysis, where projections from different orientations and of different structural states are sorted out simultaneously. Provided the 3D Fourier transform of the distinct classes is fully sampled through the orientation distribution of the raw particles, likelihood optimisation techniques have been highly successful in tackling this problem in single-particle analysis  [16, 7]. Therefore, the methods presented here may be particularly advantageous over existing subtomogram averaging methods when multiple distinct structural states exist in the set of particles. In practice, the implementation in RELION-4. 0 does not use stacks of 2D projection images as input for the refinement program that performs alignment and classification.
Instead, the concept of pseudosubtomograms is introduced, where the tilt series images are Fourier transformed, pre-multiplied with their CTF, and inserted as a slice into a 3D Fourier volume according to the best current estimates for the tilt series geometry. In order to approach the likelihood of observing the individual 2D tilt series images, the likelihood calculation from the 3D pseudo subtomogram requires separate storage of the accumulated squares of the CTFs, as well as a multiplicity term that keeps track how often each pixel in Fourier space is sampled. In the limit of infinitely fine sampling of the Fourier transform, the resulting likelihood function only differs from the exact one by a constant offset. The use of pseudo subtomograms allowed re-using existing code for subtomogram averaging in RELION, while input stacks of 2D images would have required significant software development efforts.
In the meantime, external tools to convert from M parameters to RELION-4. 0 are already available (https://github. com/joton/reliontomotools). Besides the reduction in tunable parameters that is characteristic of the Bayesian approach, its uptake by researchers that are new to the field is further facilitated through the implementation of a graphical user-interface. This interface is already widely used for single-particle analysis, and has been extended for the processing of tomographic data in RELION-4. 0. Apart from the calculations that will be familiar to users of single-particle analysis, e. g. 3D classification, 3D initial model generation, 3D auto-refinement, the new interface also provides convenient access to the tomography-specific versions for CTF refinement and Bayesian polishing, as well as pre-processing operations to calculate the pseudo-subtomograms and to import data and metadata from pre-processing operations in IMOD  [11].
Acknowledgements We are grateful to the  MRC-LMB EM facility  for help with data acquisition; and to  Jake Grimmett  and  Toby Darling  for help with high-performance computing. This work was funded by the  UK Medical Research Council  ( MC UP A025 1013  to S. H. W. S. ; and  MC UP 1201/16  to J. A. G. B. ), the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  ( ERC-.
-2014, grant 648432, MEMBRANEFUSION to J. A. G. B. ); the Swiss National Science Foundation (grant 205321 179041/1 to D. C. -D. ) and the Max Planck Society (to J. A. G. B. ). T. A. M. B. is a recipient of a Sir Henry Dale Fellowship, jointly funded by the Wellcome Trust and the Royal Society (202231/Z/16/Z). J. Z. was partially funded by the European Union's Horizon 2020 research and innovation program (ERC-ADG-2015, grant 692726, GlobalBioIm to Michael Unser). T. A. M. B. thanks the Vallee Research Foundation, the Leverhulme Trust and the Lister Institute of Preventative Medicine for support.
Therefore, by applying CANYON-MED on the large network of O 2 -equipped Argo floats, biogeochemical variables can be derived and their temporal variations investigated, for example, in response to convection in a context of reduction of DWF events  (Pagès et al. , 2020; Somot et al. , 2006; Soto-Navarro et al. , 2020). This study aims to demonstrate how climate change can rapidly affect biogeochemical content throughout the water column (not only at the surface). Such trends have been studied mainly from fixed coastal sites or during research cruises with low vertical resolution. Furthermore, trends are often estimated only for surface waters. Here, the combination of high-frequency data (both in time and space) provided by BGC-Argo floats and fixed moorings with the outputs of the CANYON-MED neural networks provides new insight into basin-wide nutrient and carbonate trends for the intermediate and deep waters of the northwestern Mediterranean Sea which are poorly documented.
Argo Floats Data from 18 Argo (Argo, 2020) profiling floats equipped with O 2 sensors (Aanderaa optode) deployed in the Mediterranean Sea were used. Data span the period October 2012 (first deployments of Argo-O 2 floats in the area) to July 2020 (see Table  S1  in Supporting Information S1 and Figure  1 ). The data set is composed of floats that were not specifically deployed or configured for this study but rather this study takes advantage of the sustained effort to deploy Argo-O 2 floats in the Mediterranean Sea. For almost all Argo floats (except 6902803 and 6902804), temperature and salinity were adjusted during "Delayed Mode" procedures. For the two Argo floats that did not go through Delayed Mode quality control procedures, a verification was done to ensure that the temperature and salinity values were consistent with the other floats in the data set and did not present abnormal values.
Derived Variables Seawater pH on the total scale at in situ pressure and temperature was derived from A T and C T data for the MOOSE-GE cruises. Calculations were performed using CO2SYS-MATLAB  (Lewis et al. , 1998; van Heuven et al. , 2011)  using the carbonic acid dissociation constants of  Mehrbach et al. (1973)  as refit by  Dickson and Millero (1987) , the dissociation constant for bisulfate of  Dickson (1990)  and  Uppström (1974)  for the ratio of total boron to salinity. The MLD was estimated for each float profile based on potential density profiles calculated from pressure, temperature, and salinity data. The MLD was derived using a 0. 03 kg m -3 threshold density criterion with a reference depth of 10 m  (D'Ortenzio et al. , 2005). The maximum Argo-derived MLD is restricted to 2,000 m, that is, the maximum depth reached by the Argo floats.
, 2013). In this study, the depth of the LIW was determined as either the salinity or temperature maxima deeper than the 29 kg m -3 isopycnal. Individual points are bottle measurements and filled contours are Argo float measurements. For the Ligurian Sea, the median modeled MLD (bright red line) is presented when the MLD could not be derived from Argo floats. Margirier et al. (2020)  proposed this method as a way to better track the LIW in the T/S space rather than restricting it to a fixed depth. In some winter profiles, the water column is completely homogenized due to mixing, therefore the depth of the LIW was determined in this homogeneous layer. Furthermore, the OML was defined as the layer spanning 75 m above and below this depth. To study the impact of winter convection events on intermediate layers, Argo float data are compared with mooring data (DYFAMED for the Ligurian Sea profiles, and LION for the Gulf of Lion profiles) at the corresponding depth, and with bottle data less than 15 days apart and less than 20 km apart.
The CANYON-MED neural networks are ensembles of two-hidden layers artificial neural networks developed specifically for the Mediterranean Sea that retrieve nutrients and carbonate system variables from temperature, salinity, O 2 , geolocation and date of sampling. They were trained on a data set of quality controlled "bottle" data from 1976 to 2018 and validated against a Mediterranean timeseries (see details in  Fourrier et al. [2020] ). CANYON-MED retrieved the variables with good accuracies for the global Mediterranean Sea (Root Mean Squared Error): 0. 78 μmol kg -1 for NO 3 -, 0. 043 μmol kg -1 for PO 4 3-and 0. 71 μmol kg -1 for Si(OH) 4 , 0. 014 units for pH T , 13 μmol kg -1 for A T and 12 μmol kg -1 for C T. By applying the CANYON-MED neural networks  (Fourrier et al. , 2020) , NO 3 -, PO 4 3-, Si(OH) 4 , A T , C T , and pH T were derived from float data.
We acknowledge the MOOSE program (Mediterranean Ocean Observing System for the Environment) coordinated by CNRS-INSU and the Research Infrastructure ILICO (CNRS-IFREMER). We are thankful to the European Multidisciplinary Seafloor and water-column Observatory (EMSO) European Research Infrastructure Consortium (ERIC) for ensuring long-term, sustained and continuous data streams from the DYFAMED and LION moorings. For seawater sample analyses, we also thank the SNAPO-CO2 at LOCEAN, Sorbonne Université-CNRS, France. Finally, we would like to thank the editors and the reviewers for their suggestions and comments which strongly improved the final version of the paper.
understand behaviors, trends, and patterns related to the phenomenon. To this aim, we introduce MiningVis, an exploratory visual analytics tool that focuses on Bitcoin mining activities and actors. Not only mining is crucial to the stability and future of most cryptocurrencies and all the people who heavily invested in those currencies, but it also has implications on society, economy, and the environment  [2, 21, 31]. As such, mining is an important aspect of cryptocurrencies to study. Bitcoin is the first and the highest valued cryptocurrency. To transfer Bitcoin users of the system generate transactions. These transactions, if valid, are stored in an open public blockchain-a type of database in which a certain number of transactions are grouped in blocks. The blockchain is maintained in a decentralized way by a network of peers.
The block is then proposed to the entire network of miners who verify the validity of all its transactions and only then the financial reward is given out. Clear economic incentives are, thus, involved in mining. Our work followed a data-first design methodology  [44] , motivated by the presence of an interesting real-world dataset from which we later derived specific stakeholder analysis questions. Our work began from a survey on Bitcoin visualization and analyses  [59]  during which we remarked a lack of tools and systems that help to analyze Bitcoin mining in detail. Due to the primary economic incentives of Bitcoin mining we partnered with an economist working on Bitcoin as our main stakeholder. To study this kind of activity, the economist needed to analyze multiple factors-both internal and external to the activitythat affect the evolution and dynamics of Bitcoin mining.
DATA ACQUISITION The first step in our development was to obtain the relevant data to analyze. Typically, economic analysis aims to explain a phenomenon and generalize it as a mathematical model. Therefore, the analysis our economist collaborator wanted to conduct required data acquisition from multiple sources with variables for which potential relationships could be established. As a consequence, we obtained five datasets related to our main analysis directions. The datasets are publicly available at  [61] , except for the data obtained from sources that are already public and which we cannot reshare (D3 and D4). D1: The mining pool distribution dataset gives information about the evolution of mining pools over time. It was derived from identifying mining pools that received the reward for each individual block in the Bitcoin blockchain.
We extended the initial dataset compiled by Romiti et al. [49]  (data up to: 2018-12) to extract the mining pool that mined each block. Blocks that could not be attributed to any known mining pool were marked as "unknown". Our final dataset at the time of writing includes data until 2020-09 and contains the mining power of each mining pool for each month. We computed multiple quantitative measures related to mining power: hash rate, market share (normalized hash rate), and total reward received from mining (in BTC and USD). Additionally, we calculated the estimated electricity consumption of each mining pool based on the CBECI index  [4]  to allow to judge the externality of mining on the environment. D2: The mining pool characteristics dataset includes external data about mining pools that we obtained from public sources  [6, 7]  and manually cleaned.
The attributes include the primary location of the pool (nominal), payout scheme (nominal), pool fee (quantitative), and whether the pool kept transaction fees or shared it to miners (binary). We also extracted the wiki history to track attribute changes as mining pools adapt their payout policies to compete with other pools. D3: The Bitcoin network statistics dataset contains multiple quantitative measurements of Bitcoin network's state over time. We collected daily statistics from Blockchain. info  [10]  and averaged those measures to a monthly time window. Examples of network statistics for each month include the market price, total hash rate, total block reward, total transaction fees, mining difficulty, the number of transactions, electricity consumption  [4] , and the global energy price index  [27].
D4: The Bitcoin news dataset lists headlines from the Press forum in Bitcointalk. org [8] , where users posted links to news articles related to Bitcoin. Each news contains the date when the news was published by its source, the news headline, and the number of replies and views. We chose to use the number of views and replies as an attractiveness indicator of the news from the Bitcointalk's members. We derived an importance score of each news items as views × (replies + 1). The news we collected covered various Bitcoin-related topics. To group related articles, we used the Word Network Topic Model method  [71]  that proved to be simple and effective for short texts. We extracted 15 topics and the top-10 keywords for each topic. D5: The miners' migration dataset describes miners' migration between mining pools over months.
We detect miners who participate in more than one pool (cross-pooling) within a month and calculate the number of miners' addresses and the total reward those miners received from each pool. Besides, we also detect the flow of miners that join a mining pool for the first time (enter), move from a pool to another (pool hopping), and quit the mining pool (exit) between months. The process of obtaining this dataset is complex and covered in one of our previous articles focused on pool hopping  [62].
It became apparent, that some questions could not be solved with simple charts and simple datasets. For example, his questions regarding pool hopping required the development of a dedicated algorithm that we published with his findings and images of additional Jupyter notebook visualization prototypes at a Blockchain-focused conference  [62]. The work discussed in these two previous papers built the foundation for this present paper but only focused on specific narrow elements of Bitcoin mining. Here we go significantly beyond this prior work and discuss our final and encompassing web-based Visual Analytics MiningVis tool and the results from our user study. For the final design of MiningVis, we derived visualization analysis tasks by observing which actions the economist repeatedly performed on our prototypes.
In Table  1 , we summarize the relationship of our derived analysis factors (AF1-AF3), the data we collected (D1-D5), and the visualization tasks (T1. 1-T3. 2). The tasks are: 1. Temporal overview of mining pool rankings. Analyzing the Bitcoin mining activity requires gaining an overview of the emergence and evolution of mining pools (T1. 1) as well as assessing the competition among them by comparing market share, mining power and rank of pools over time (T1. 2). 2. Temporal zoom and filter to explore relevant time periods to investigate in more detail. The economist frequently searched for time periods of importance to Bitcoin mining (T1. 3). He looked in particular at periods where one or two mining pools were close to dominating the market share ("concentration index") as well as outliers in Bitcoin statistics (e.
Rank visualization Tasks T1. 1 and T1. 2 required the visualization of mining pool rankings over time. We considered several previous solutions: The Rank chart stacks ranked objects in multiple columns (e. g. , per year) and connects the same items with a line, similar to parallel coordinates with discrete rank steps [14, Fig. 63 ]. LineUp similarly ranks items in a table-like structure and focuses on exposing the comparison of multiple attributes in columns connected by ribbons. Rank charts recently inspired Gap Charts that use ribbons to encode both a rank and a quantitative difference between ranked items (y-axis) over time (x-axis)  [46]. For rank visualizations in large datasets, Xia et al. proposed another table-like visualization to show changing ranks (y-axis) of most viewed pages on Wikipedia.
Stock and flow visualization For Task T3. 1, we model miners' migration data as a flow relationship between mining pools with cross-pooling miners flowing between mining pools. Common visualization types that can encode this kind of data are Sankey and Chord Dependency diagrams. Sankey diagrams  [48, 55]  show the flow of values from one stock to another as a nodes and links where their width encodes a flow value. A famous example is Minard's map showing the number of French soldiers during Napoleon's invasion of Russia in 1812  [47]. It encodes the flow of men in both space and time. Telly's Stock and Flow Visualization  [17]  divides students into groups and tracks their high school advancement. Rosvall and Bergstrom  [51]  use an Alluvial diagram to show the scientific network community changing over time.