dataesr/scientific-paragraphs-categorization huggingface dataset:
AI-Assisted Materials Characterization While there is a substantial focus on developing machine/deep learning models aiming at predicting new materials with better functional properties as described above, there is a significant research direction of using AI-based models for automated/ autonomous materials data analysis. Namely, there has been an over 15 year-long development of tools for the automatic phase mapping problem in combinatorial materials science. Based on spectral decomposition,  [73]  there has been tremendous  [74] [75] [76]  progress for phase mapping with, for example, the Gibbs' Phase Rule as a constraint. Moreover, it is even possible to extract optimized crystal structures with physically correct orientation and phase amounts from high-throughput X-ray diffraction. [77] rior to applying any machine learning approach, there is typically much data curation needed like noise filtering or background subtraction, with the latter one recently being demonstrated with algorithms that consider the entire dataset taken going beyond single spectra.
Some tools are already deployed, for example, the metadata extraction module,  [90]  and these will be further developed within BIG-MAP to strive to make the generation of simulation and laboratory workflows a facile task. Examples include the Materials Cloud  [91] [92] [93]  and Simstack for computational workflows and ChemMotion  [94]  for organic chemistry-focused research, and KADI4Mat  [95]  as an institution-wide deployed data management platform for materials science. Other examples include MEAD  [96, 97]  and HTEM  [98]  for the experimental sciences showcasing the combined community efforts to develop interoperable data repositories. However, self-driving labs  [81]  require more than high-level data management, that is, the capabilities of orchestrating the machines. [88] rchestration in a laboratory context means that there are interfaces for different hardware, automated data analysis, and autonomous planning  [99]  of the next Pareto optimal  [100]  experiment(s) using domain-specific optimizers.
[101] A strong focus is placed in BATTERY 2030+ on ensuring the transition from automated to autonomous materials discovery. Given the community adoption of these data management or even workflow tools, hardware and software interconnection remains the most significant remaining challenge. While commercial frameworks like LabView exist, scientists developed tailored solutions like ChemOS,  [102]  ESCALATE,  [103]  and the Chemputer. [83, 104] Within BIG-MAP, there is a strong push to integrate closed-loop capable workflows in the simulation and experimental realm. To this end, not just transfer learning across the approaches is necessary, but also a new level of cooperation between experimental and computational groups is necessary. Within BIG-MAP, these collaborations are fostered through the construction of work packages (Figure  1 ) that distribute work across the disciplines equally and through a publicly available app store.
Summary and Outlook BATTERY 2030+ has embarked on a mission to develop a versatile and chemistry-neutral platform for the accelerated discovery of novel battery materials and interfaces. While the initial focus in the six research projects is on demonstrating the capabilities of next-generation Li-ion batteries, the machine and deep learning methodologies, autonomous orchestration software, and integrated research infrastructure is developed as modular and externalizable tools, which will enable accelerated discovery and development of emerging, low-TRL ultra-high performant battery concepts and designs. Here, we have illustrated how the AI-based modules and tools being developed to address the main challenges for "nearest in time chemistries" (European SET-Plan action 7  [216]  ), (e. g. , generalizable workflows and recipes for developing NNPs for long time-and lengthscale predictions of the evolution of the battery SEI), can also be applied to resolve the most pertinent challenges for emerging low-TRL battery concepts and designs.
It should also be noted that the success will depend critically on the methods themselves, as well as the availability of (very) large curated and FAIR datasets from all parts of the discovery cycle. The latter is critical to get sufficient data to obtain reliable statistics to derive hyperparameters and descriptors of the materials in their more complicated electrochemical environments. Developing physics-and uncertainty-aware datadriven methods  [217]  capable of training on such multi-sourced experimental and simulational data will strongly enhance the quality of the deep interface descriptors and features that play a critical role in shortening the path to realizing emerging battery technologies and concepts. Tejs Vegge heads the Section for Autonomous Materials Discovery at the Technical University of Denmark, working on the development of autonomous methodologies for accelerating materials discovery and innovation processes.
Acknowledgements The authors acknowledge the European Union's  Horizon 2020 research and innovation program  under grant agreements no. 957189  ( BIG-MAP ) and no. 957213  ( BATTERY 2030+ ). K. H. acknowledges the  Swedish Research Council (Vetenskapsrådet)  and the  Swedish national Strategic e-Science programme   eSSENCE. R. D. acknowledges the financial support from the  Slovenian Research Agency  (research core funding  P2-0393  and Project  N2-0214 ). H. S. S. acknowledges funding from  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy-EXC 2154-Project number  390874152. M. C. -C. acknowledges the  Spanish Ministerio de Ciencia, Innovación y Universidades  (Project reference number:  PID2019-107106RB-C33 ). M. R. P. acknowledges the  Spanish Agencia Estatal de Investigación Severo Ochoa Programme for Centres of Excellence in R&D  ( CEX2019-000917-S ).
Patients and data collection All patients with confirmed COVID-19 admitted to the IDU medical wards and ICU of the Gabriel-Montpied Teaching Hospital in Clermont-Ferrand, France, from March to the end of November 2020 were eligible for enrolment. A total of 134 patients admitted to the ICU were thus included in a preliminary prospective monocentric observational study and followed up until ICU discharge and/or death. In our hospital, as in the rest of France, we experienced two distinctly separate waves of patient hospitalizations: the months of March to June (03/08/2020 to 06/02/2020), a period called Wave 1, during which 34 patients were enrolled, and from September to November (09/14/2020 À 11/23/2020), a period called Wave 2, during which 99 others were included. A single patient was hospitalized in August between the two waves, but for the purpose of the study was included in the Wave 1 group.
After a washing step, cells were acquired on a BD LSR II cytometer (BD Biosciences, San Jose, CA, USA) and flow data were analysed with FlowLogic software (version 7. 3 software, Miltenyi Biotec, Germany). The total number of antibodies bound per monocyte (Ab/cell), defined as total CD14+ cells, were quantified by calibration with a standard curve determined with BD QuantiBRITE phycoerythrin (PE) beads (BD Biosciences, San Jose, CA, USA) (see Figure  S1  for gating strategy). Antibodies, reagents and the respective suppliers are shown in Supplemental Table  1.
Plasma cytokine analysis Plasma was obtained through centrifugation of EDTA samples within 4 h of phlebotomy at the indicated time points following inclusion of patients in the study. Plasma was placed in aliquots and stored at À20°C until use. Human pro-inflammatory cytometric bead array (CBA) and human IFN-a flex kits (BD Biosciences, San Jose, CA, USA) were used. The human pro-inflammatory cytokine kit simultaneously detects IL-6, IL-10 and CXCL8 cytokines in a single sample whereas the IFN-a flex kit detects IFN-a. The assays were performed according to the manufacturer's instructions. Samples and standards were acquired on the BD LSR II cytometer (BD Biosciences, San Jose, CA, USA) and the generated FSC files were analysed with FCAP Array version 3. 0 software. Plasma IL-1Ra levels were measured with a commercial enzymelinked immunoassay (ELISA) kit (human IL-1Ra ELISA from Invitrogen, Villebon-sur-Yvette, France).
Statistics Statistical analyses and graphical presentations were conducted with GraphPad Prism version 8. 0 software (Graph-Pad Software). The population was expressed as numbers and percentages for categorical variables. For quantitative variables, the results were expressed in terms of mean § standard deviation in the Figures or mean and IC95 in the Tables. Categorical data were analysed with Chi-square test with a 5% risk of type 1 error. Statistical analysis of continuous variables, ie the different severity groups, mild versus severe forms, survivors versus non-survivors, was performed with one-way MannÀWhitney U test. For the longitudinal follow-up of patients, statistical analysis from baseline was performed with a Wilcoxon matched pairs test. Pearson's correlation coefficient was used to assess the relationship between the PaO2/FiO2 ratio and mHLA-DR expression or IL-6 and mHLA-DR expression.
Analyses were performed with no correction for multiplicity. The receiver operating characteristics (ROC) curve analysis was performed using the "pROC" package on R software to identify the optimal cut-off values of baseline and D7-10 mHLA-DR, IL-6 or IL-6/mHLA-DR*10 3 parameters for prediction of fatal COVID-19 or secondary infections. The optimal cutÀoff points to predict the severity of COVIDÀ19 were determined by Youden's index using R software. Sensitivity, specificity and the corresponding IC95 of optimal cut-off points were determined using the "pROC" package.
Acknowledgements We thank the promoters of the  CORIMUNO-19  project ( INSERM-Assistance Publique des Hôpitaux de Paris ) for their help, the patients of our study for being part of this cohort (screening with signature of a consent) without being included in the associated trials (due to competition with other studies in progress). We thank  Xavier Daudet ,  Estelle, Chapon, Charl ene Pierson, Aur elie Briançon ,  Brigitte Goutte ,  Marl ene Ravel ,  Julia Mercier ,  Marion Gaudard , Sol ene Revy,  Maud Junda  and Fr ed eric Du ee for their immense assistance in the laboratory during patient material collection. Thank you to the entire staff of the  Department of Infectious Diseases and intensive care unit  for their feedback and scientific discussions. We thank the AFRRI association, who kindly provided the additional doses of IL-1ra.
1. 8) and dried. The weight loss of the material being dried was measured automatically throughout the whole operation using a PS 6000. R2 laboratory balance (Fig. 1. 10, precision 0. 01 g) produced by Radwag (Poland). The temperature of the sample's surface was measured constantly with a CT LT15 pyrometer (Fig. 1. 7, precision 0. 1 • C) produced by Optris (Germany). The whole drying procedure proceeded automatically and was controlled by an industrial PLC driver (Fig. 1. 12) produced by WAGO (Poland). All the drying parameters were recorded at a predetermined time interval (every 5 min) and stored in the data acquisition software accompanying the drying system. Two variants of drying were tested -CD and US-assisted CD (CDUS). In both cases, the air temperature was 333 K and the air velocity was 2 m/s.
Introduction In this paper, we present a dataset of global flight movements derived from crowdsourced air traffic control data collected by the OpenSky Network  (Schäfer et al. , 2014) , which are widely used in many fields, including several areas pertaining to Earth system sciences. With the spread of COVID-19, they are furthermore widely used in the understanding of the pandemic and its effects. OpenSky flight data have regularly been used in analysing environmental issues such as noise emissions  (Tengzelius and Abom, 2019)  or black carbon particulate emissions  (Zhang et al. , 2019)  to name but a few. In the wake of the pandemic, OpenSky has received a surge of more than 70 requests for air traffic data specifically related to COVID-19. The research behind these requests can be largely separated into two different areas, epidemiological modelling and understanding the systemic impact of the pandemic.
The first category, modelling of the possible spread of COVID-19, was of crucial interest early in the stages of the pandemic and will again gain importance in estimating travel safety in the future. The utility of flight data for this purpose was illustrated for example in widely circulated studies such as  Bogoch et al. (2020)  but has been known to be useful in the context of pandemics for much longer (e. g. Mao et al. , 2015). The second main category comprises the analysis of the socio-ecological impact of COVID-19 and measures implemented to fight it. It uses flights for example as an indicator of economic activity (at a given airport or region or globally) as illustrated in  Miller et al. (2020). Examples of such use of data provided by OpenSky can be found in the follow-Published by Copernicus Publications.
ing: Bank of England, Monetary Policy Committee (2020), International Monetary Fund (2020), or United Nations Department of  Economic and Social Affairs (2020). Flight data can further be used to understand the impact of the sudden drop in air traffic in many global systems. For example,  Lecocq et al. (2020)  employed OpenSky data recently in order to analyse the impact of COVID-19 mitigation measures on high-frequency seismic noise and we have received several requests relating to research specifically on the impact of COVID-19. This present dataset, available at https://doi. org/10. 5281/zenodo. 3931948 (last access: 9 February 2021), was created in order to make it easier for researchers to access air traffic data for their own systemic analyses.
This development has given rise to several global crowdsourced flight tracking efforts, from commercial to enthusiast and research use. The concept of flight tracking itself is based on several radar technologies. Traditionally, these were expensive and inaccurate non-cooperative radars developed for military purposes. With the explosive growth of global civil aviation, however, more accurate cooperative radar technologies have been deployed to ensure safety and efficiency of the airspace. For this dataset of flight movements, we use the data broadcast by aircraft with the modern Automatic Dependent Surveillance-Broadcast (ADS-B) protocol. These data include position, velocity, identification and flight status information broadcast up to twice a second (see  Schäfer et al. , 2014). The protocol is being made mandatory in many airspaces as of 2020, resulting in broad equipage among larger aircraft from industrialized countries and emerging economies as described by  Schäfer et al.
(2016). Figure  2  illustrates the principle of OpenSky in the abstract: the data are broadcast by ADS-B-equipped aircraft and received by crowdsourced receivers on the ground, which have typical ranges of 100-500 km in a line-of-sight environment. The data are then sent to the OpenSky Network, where they are processed and stored in a Cloudera Impala database. In line with its mission as a non-profit organization, OpenSky then grants researchers from academic and other institutions direct access to this database on request  (Schäfer et al. , 2014). Along with the global sensor coverage, the database has initially grown exponentially since its inception in 2014 (see Fig. 1 ) and currently comprises over 24 trillion messages, taking up around 2 PB. In peak pre-pandemic times, almost 100 000 flights were tracked per day.
The raw data available in the Impala database have been used in more than 150 academic publications as of 2020  (Strohmeier, 2020). However, despite available application programming interfaces and third-party tools, access to these data requires significant investment of time and resources to understand the availability and underlying structure of the database. With this dataset and its accompanying descriptor, we want to address this accessibility issue and make a relevant part of the OpenSky Network flight metadata accessible to all researchers.
Crowdsourced collection The raw data used to generate the dataset were recorded by more than 3000 crowdsourced sensors of the OpenSky Network. The network records the payloads of all 1090 MHz secondary surveillance radar downlink transmissions of aircraft along with the timestamps and signal strength indicators provided by each sensor on signal reception. Part of this data collection comprises the exact aircraft locations broadcast at 2 Hz by transponders using the ADS-B technology. As the data come from a crowdsourced system of receivers, their collection faces numerous challenges and difficulties found in such an organically grown, non-controlled set of receivers. However, it is the only feasible option for the large-scale collection of open research data as collecting data from a synchronized and controlled deployment would be less flexible and less widely applicable, in particular for a non-profit research endeavour.
Conversely, due to the high sensor density and high level of redundancy in the OpenSky Network, many well-covered regions of these data achieve the quality of controlled deployments on a nationwide level. The true coverage of the network, i. e. actually received positions of aeroplanes, is illustrated in Fig. 3 , both for 1 January 2019 and during the pandemic on 1 May 2020. Historic coverage for any given day is visible at https: //opensky-network. org/network/facts (last access: 9 February 2021). We compare this growing coverage to global commercial flight tracking website Flightradar24, which tracked 68 948 849 total flights in 2019. 1  Unfortunately, this number is not broken down into how many of these flights were tracked via ADS-B technology (versus other methods such as multilateration or non-crowdsourced approaches including the use of satellites and primary radar), which would make it possible to directly compare it with our dataset.
Derivation of flights We define a flight for the purpose of this dataset as the continuous time between the first received ADS-B contact of one specific aircraft and the last. Such a flight's length must be at least 15 min. This filter avoids noise from misconfigured transponders and real aircraft seen for only a very short time, which are generally not of significant value for the dataset. If an aircraft leaves OpenSky's coverage range for more than a threshold T = 10 min, this flight's track is considered finished at the point of last contact. We further want to avoid creating a new flight record in case an aircraft simply leaves the coverage and returns after some time larger than T (e. g. for any flight over the Atlantic Ocean) without actually having landed. We thus apply a simple check: if time, distance and reported velocity fit with a constant extrapolation based on the last-known values (minus a small threshold of 0.
Data cleaning To make the data accessible and meet requirements, complex pre-processing is needed in order to reduce the reduce the data volume and eliminate the need to understand all system aspects in order to use the data. Moreover, the inforhttps://doi. org/10. 5194/essd-13-357-2021 Earth Syst. Sci. Data, 13, 357-366, 2021 mation quality needs to be assessed and indicated, allowing researchers to choose subsets that match their own requirements. Therefore, we performed the following processing steps to prepare the unstructured OpenSky Network data and create a well-defined dataset for scientific analysis.
Quality assurance Crowdsourcing creates potential issues regarding the quality and integrity of location and timing information of certain aircraft and sensors. Such issues can range from faults in the transmission chain (i. e. aircraft transponder, ground station) to malicious injection of falsified aircraft data. To allow researchers to ignore these effects while still preserving them as a potential subject of research, OpenSky offers integrity checks to verify and judge the data correctness (see  Schäfer et al. , 2018). We also note that the abstracted nature of this dataset makes it more robust to any issues in the first place as low-quality data will be averaged out over time by the many involved receivers.
Data enrichment We use the OpenSky aircraft database to add aircraft types to our flight data and access publicly available open application programming interfaces (APIs) to match the commercial flight identifier, where available. The integration of aircraft types enables additional analysis such as gauging the popularity of different types and manufacturers across time, regions and use cases. Aircraft type designators follow the International Civil Aviation Organization (ICAO) convention (International Civil Aviation Union, 2020). The OpenSky aircraft database was created in 2017 as an additional crowdsourcing project. It joins different data sources, both official and unofficial ones. The official sources include the registration information from the flight authorities in the USA, UK, Ireland and Switzerland, which is downloaded and incorporated daily.
Data records Overall, the archive includes 41 900 660 flights, from 160 737 aircraft, which were seen to frequent 13 934 airports in 127 countries. One file per month is provided in commaseparated values (CSV) format. Table  1  provides a breakdown of the included CSV files and their contents, broken down into size, number of flights, number of unique aircraft, unique origins and destinations. Note the significant reduction in size and flights since the beginning of pandemic measures in March 2020. We describe the columns of the dataset in the following: 1. callsign. The identifier of the flight used for display on the radar screens of air traffic controllers or voice communication. For commercial flights, the first three letters are typically reserved for an airline, e. g. AFR for Air France or DLH for Lufthansa.
4. typecode. This is the aircraft model type if available through the aircraft database. 5. origin. This is a four-letter code for the origin airport of the flight if the trajectory was able to be matched successfully. 6. destination. This is a four-letter code for the destination airport of the flight if the trajectory was able to be matched successfully. 7. firstseen. This is the UTC timestamp of the first airborne message received by the OpenSky Network. 8. lastseen. This is the UTC timestamp of the last airborne message received by the OpenSky Network. 9. day. This is the UTC day of the last airborne message received by the OpenSky Network. 10. latitude_1, longitude_1, altitude_1. This is the position of the aircraft at the firstseen timestamps. The altitude is a barometric measurement based on a standard pressure of 1013 HPa.
Technical validation In the following, we provide some statistics showing that our flights dataset reflects the air traffic reality as different time series showing the effect of the COVID-19 pandemic at different airports and for different airlines. Table  2  shows the distribution of the top 25 aircraft types in the flight dataset over 1 month (January 2020). Overall, the top models are dominated by the four largest commercial aircraft manufacturers: Boeing with eight different types accounting for 663 308 flights, Airbus with seven models and 766 499 flights, Embraer with four models (120 163 flights), and Bombardier (three models, 126 052 flights). The 737-800 is the single most popular aircraft with 378 424 flights in January 2020 alone. Table  3  shows the distribution of the top 20 airport types in the flight dataset in January 2020 (based on recorded flight destinations).