dataesr/scientific-paragraphs-categorization huggingface dataset:
We refer the reader to App. A. 3 for more details on the origin of this uncertainty. • DATES: list of data taking periods for the instrument taken from the publication or, if not reported in old balloon flights, retrieved from the StratoCat database 6. • DISTANCE: most of the experiments are located on Earth, or are orbiting Earth. However, a few satellites (e. g. Ulysses, Voyager, etc. ) took data at various locations in the Solar system. We provide in CRDB the average distance to the Sun in a. u. (an astronomical unit is the distance Sun-Earth) during the data taking period, as provided in the publications. • SMALL_PHI: Accurate Solar modulation models are complex and depend on many parameters. The simplest model, the Force-Field approximation  [28] [29] [30] , depends on a single parameter φ(t), and remains useful for many applications despite its limitations  [31].
Therefore we provide the mean modulation level φ FF calculated externally from φ time series (see below) over the sub-experiment data taking periods. From interstellar (IS) to top-of-atmosphere (TOA) fluxes GCR fluxes are Solar modulated, the modulation level depending on the Solar cycle  [32]  and the position in the Solar cavity. IS fluxes refer to fluxes outside of the solar cavity while TOA fluxes correspond to modulated fluxes at Earth. The impact of the Solar modulation decreases with energy and can only be disregarded when it becomes much smaller than the data precision 7. Most balloon and space experiments in CRDB are TOA data from experiments inside the solar cavity. To be compared to them, IS fluxes in GCR propagation models must therefore be Solar modulated. This makes the bookkeeping of the experiment data taking periods and position in the Solar cavity mandatory.
PUBLI and SUBEXP_PUBLI tables and meta-data CR data are published by experimental teams and collaborations in scientific publications. To save time and avoid confusion, we have a strong preference to include only final data published in peer-reviewed journals, and most data in CRDB come from refereed journals. An important exception is the proceedings of the biyearly International Cosmic-Ray Conference (ICRC, started in 1947): until the 90's, many balloon flight results were published in the ICRC proceedings only, and even nowadays these proceedings still often contain the latest preliminary results and updates of previously published results.
4. 1). Deprecated keys in CRDB v4. 0 One major modification in the PUBLI table in this release was to abandon book-keeping Solar modulation informations from the publication, i. e. Solar modulation levels calculated by the authors for their data. This feature was introduced in CRDB v2. 1-requiring many extra keys in the PUBLI table, see Maurin et al. [22] -in order to highlight the various assumptions behind the modulation levels provided. The motivation was to ease comparisons between modulation levels for data taken at similar periods but provided by different experimental teams. However, picking these informations from the publication was time consuming for very little practical use in retrospect 15. Moreover, as stressed in the previous section, modern CR propagation model studies either make use of φ FF or rely on public Solar modulation codes, and the informations stores in the SUBEXP table allow both these practices (see Sect.
New data in CRDB v4. 0 We now turn to the database content. As much as possible, data are added in CRDB on a continuous basis (by one of us); new data can also be submitted (see Sect. 4. 4). Thanks to the feedback of many users, typos and mismatches in the data and meta-data are regularly corrected-the list of corrections and people who helped us (see acknowledgements) are available online (see CRDB log file). In this section, we highlight the main datasets added since the first release  [22] , in particular in the two most recent releases CRDB v3. 1 ( §3. 1) and v4. 0; for the latter we detail the motivation and specifics of UHCR data ( §3. 2), antinuclei upper limits ( §3. 3), and UHECR data ( §3. 4). The complete list of experiments and publications (presently in CRDB) is gathered in App.
B. 13 www. bibtex. org 14 https://github. com/adsabs/adsabs-dev-api 15 Indeed, the IS spectra and the Solar modulation models used are too patchy. For IS fluxes, publications used spectra derived from their data, from other authors, of from inputs of GCR propagation models. For Solar modulation, the models found in the publications were (i) for very old data, the outdated diffusion/convection model and its η parameter  [39] [40] [41] , (ii) since the 70's, the still widely used Force-Field approximation and its φ FF parameter, or the more evolved spherically symmetric model and its effective φ 1D free parameter  [42] [43] [44] , (iii) in more recent studies, the modern sign-charge dependent 3D model with parameters for the current sheet, tilt angle, diffusion tensor, etc. [27, 45, 46]  or, most of the time, no modulation value at all (just the data).
3. 1. Relevant data updates before CRDB v4. 0 Since the first release of CRDB seven years ago, two major data updates are worth highlighting. The first, tagged as CRDB v3. 0, added H and He data from a large number of balloon flights going back to the 50's. These data sets were used to derive Solar modulation levels over sixty years and compared to values derived from neutron monitor data in Ghelfi et al. [1]. The second major upload, tagged CRDB v3. 1, was made a few weeks before preparing this release. The update is the result of a tremendous concentrated effort to include a wealth of data released in the last two years from various experiments (AMS-02, CALET, NUCLEON, TRACER, Voyager, and a few others); they correspond to some of the most interesting and high-precision CR data ever recorded.
We added in CRDB v4. 0 most of the CR data found for Z > 30. Most measurements do not resolve elements, and we needed to create new name holders for groups of charges, for instance Pt-group (74 ≤ Z ≤ 80), Pb-group (81 ≤ Z ≤ 87), Subactinides (74 ≤ Z ≤ 87), and Actinides (Z ≥ 88)-see Table  A2. We note that in the literature different groups sometimes used slightly different charge range in their definition, but we stick to this one for all implemented data-data uncertainties are larger than the error made from using slightly enlarged or reduced charge groups. The data themselves mostly consist of ratios of elements (or of groups of elements) in an unspecified energy band (related to the detector capabilities). In practice, CR fluxes are expected to be maximal at a few GeV/n, and several experiments reported no energy evolution (within the uncertainties) of their measured ratios.
For this reason, and for simplification, we set all UHCR data Z > 50 to a generic energy bin around 1. 5 GeV/n. More than sixty years after their discovery, only very few UHCR data are available and almost no new experiment is running or planned. Except for the relatively recent superTIGER experiment in the Z = 30 -40 range  [65] , the experiments in the high charge range date back to the end of the seventies (Ariel6  [63] , HEAO3-HNE  [66, 67] ), the early eighties (UHCRE-LDEF [3,  68, 69] ), or at best the nineties (Trek  [70, 71] ). The most recent results on subactinides and actinides come from indirect measurements of the CRs from their impact on meteorite olivines  [72] [73] [74] , namely the Olimpiya Experiment (not implemented yet in CRDB). The solution to the origin of these elements may come from yet another route: recently, optical follow-up observations of a gravitational wave event have shown that binary star mergers could be a major r-process contributor to UHCRs  [75].
Whether these events are real, and whether this is compatible with no detection of antideuterons and can be accounted for physics scenario is still at the exploratory stage  [82] [83] [84] ; a confirmation of these events would have huge consequences. To tackle upper limits, a new key in the CR database table was added (see Sect. 2. 1). Limits on antielements are also usually derived with respect to elements, and we added in CRDB v4. 0 new names to cover all associated data (see Table  A2 ). The best limits so far come from the BESS balloon flights  [10]  and PAMELA mission  [103] , as shown in Fig. 2  with results from other experiments derived over the years (all included in CRDB v4. 0).
Ultra-high energy CRs A major extension of the already comprehensive CR database is the inclusion of data from air shower experiments. High-energy CRs are measured indirectly with ground-based experiments. They detect the particle cascades initiated by CRs in Earth's atmosphere (air showers). Since the CR flux drops rapidly with increasing energy, the air-shower technique is the only feasible way to obtain CR data above PeV energies. The air-showers allow for large aperture experiments due to their extensive footprint on the ground in charged particles and Cherenkov light, and their visibility from a distance in ultra-violet light. The energy and direction of a CR can be well inferred from air showers, but the identity of each individual CR cannot be determined accurately. The identity, more specifically the mass A of the CR, has to be inferred from air shower properties that naturally fluctuate, like the slant depth X max of shower maximum measured from the top of the atmosphere or the total number N µ of muons produced in the shower.
User-interface updates and new submission form CRDB is hosted by the LPSC laboratory website. It runs on a LAMP solution, i. e. a stack of free open source softwares: Linux (operating system), Apache HTTP (server), MySQL (database), and PHP (hypertext pre-processor language). The web interface relies on several third-party libraries (jquery, jquery-ui, jquery. cluetip, and table-sorter) that are used to sort and display the database content, as briefly presented in the various sections below. For efficiency and speed, all web pages make use of AJAX (Asynchronous JavaScript and XML) web development techniques. Accessing the data in CRDB can be achieved in two ways, either by accessing the resources via CRDB web pages, or via a REST (representational state transfer) interface. While web pages allow to access the fully contextualised data and meta-data (thanks to several sorting, selections, display, and download of data options), the REST interface is useful for users who want to access and retrieve data from command lines (i.
'Experiments/Data' tab This is the tab to go for users looking for a specific experiment, its meta-data, and the data it gathered; experiments can be sorted according to their name, starting date, or type. This tab lists the quantities they measured. It also provides links to the experiment web page, the associated publications, and pictures of the sub-detectors (by clicking on the 'magnifying glass' icon); see Fig. 5  for an illustration. Clicking on '[data]' for any given sub-exp gives further information on the CR data origin: data taking period, how data were retrieved (numbers from tables, extracted from figures, or from personal communications), details on the detector, etc. We stress that the data presented in this tab are 'native' data only (see App. A. 1), i. e. data as provided in publications and uploaded in the database without modification (which is not the case in the 'Data extraction' tab).
'Admin' tabs This tab can be accessed by authenticated users only (CRDB maintainers). In this page, various scripts provide internal checks of the database content (missing images for the detectors, orphan ID in the various tables, list of superseded publications, etc. ). This page used to handle validation forms for submitted data, but they were removed thanks to the new submission form in CRDB v4. 0 (see Sect. 4. 4). We can also monitor from this page the traffic on CRDB, stored in a specific LOG_QUERIES table (connection date, IP address, and page visited, see Fig. 1 ). This traffic since August 2013 is shown in Fig. 8. CRDB received more than a quarter million queries, from ∼ 20, 000 different IP addresses in over a hundred countries-most queries originate from Germany, USA, Italy, France, China, Switzerland, and Japan, i.
'REST access' tab The database offers a REST  19  interface, which makes it easy to download data sets according to selection criteria from another program. The interface has been available since version CRDB v1. 3, but the new help page in CRDB v4. 0 makes this feature more prominent. Data sets can be selected by quantity (element, isotope, electron or positron, or mass group), experiment, energy range, and time range. Users must specify the energy type in their query. As mentioned in previous sections, data sets are stored in their native energy type, which can be kinetic energy, kinetic energy per nucleon, total energy, rigidity. The native type is converted to match the request, and details on the conversion are given in App. A. 4. Other parameters of the interface control the output format, and whether ratios should be synthesised from individual flux measurements.
The available parameters are documented in Table  1  (also reproduced on the website). To give an example, the boron-to-carbon flux ratio as a function of the kinetic energy per nucleon can be queried with the command-line utility curl as follows: curl -L 'http://lpsc. in2p3. fr/crdb/rest. php?num=B&den=C&energy_type=EKN' > db. dat The output in USINE format (table with columns separated by spaces) is stored in the file db. dat. Queries can be made from any general purpose programming language. For Python users, we provide a simple module to run queries from Python and return the result as a numpy array. The code and a tutorial with example usage are available on GitHub  20  and linked to from the CRDB website. energy_convert_level c Add data obtained from an exact or approximate energy_type conversion (from native to queried).
Summary and future developments In this article, we have presented CRDB v4. 0, a new version of the CR database for charged species, highlighting several improvements on the database structure and content, and on the user interfaces. We summarise the changes made below: • Database structure: a few tables were simplified and three became deprecated (Solar modulation table from the publication, users and validation tables for the old data submission interface). The pre-defined key values were also modified for the experiment type (now balloon, ground, or space) and energy type (now also includes ETOTN). Two new keys were added in the in the SUBEX_PUBLI and PUBLI tables respectively, in order to keep track of the data origin (retrieved from from a table or figure in the publication, etc. ), and to store a possible energy scale uncertainty in the data (single number for a given sub-experiment).
• Database content: this version is associated with a massive upload of CR data. For CRDB v3. 1, released a couple of weeks before this version, we filled important GCR high-precision data published in the last two years (AMS-02, DAMPE, NUCLEON, TRACER, Voyager, etc. ). For CRDB v4. 0, we extended the CR data content towards higher energies (KASCADE-GRANDE, Pierre Auger Observatory, etc. ), higher charges Z > 30 (LDEF UHCRE, TREK, etc. ), and lower charges Z < -1 (upper limits from AMS-01, BESS, PAMELA, etc. ). The database now contains more than 100 experiments and 350 publications, covering 14 decades in energy (from 10 6 to 10 20 eV). • User interfaces: the many improvements we implemented provide users with more options to retrieve data, along with a clearer presentation of the various interfaces.
These changes include: (i) more data selection options (e. g. on times series) and plotting/export options (. pdf,. csv), including the possibility to display several CR quantities on the same plot; (ii) new help page and an example python notebook script to retrieve data via the REST interface; (iii) updated interface to retrieve Solar modulation level φ FF time series (or average) from any time period since 1950 with its dedicated REST interface, (iv) simplified file format to submit new data; (v) extended list of links for online CR resources. We hope in particular that the new submission format, with clearer explanations about the data and meta-data to fill, will help us always keeping CRDB up-to-date in the future. Final thoughts on CRDB evolution CRDB was thought from the start as a service to the CR community.
In an ideal world, we would like experimentalists to submit their data to CRDB as soon as their results appear on the arXiv preprint server 21 or in a peer-review journal. There are several possible directions along which CRDB could be improved. On a technical aspect, we are planning to make the website responsive for a better rendering on diverse electronic devices. On the data content aspect, we will obviously continue to upload new data as they appear, as quickly as we can. We would also be grateful for any help to extend the datasets to new quantities. Indeed, without any change in the database structure, we could quite easily include (i) more relevant quantities for UHECRs, i. e. the mean logarithmic mass ln A , the mean depth of shower maximum X max , etc. ; (ii) dipole anisotropy spectral data as presented in Ahlers and Mertsch  [127] , (iii) upper limits or data on the neutrino CR spectrum discovered by the IceCube Collaboration  [128].
As a first step in this direction, near the completion of this article, a preliminary agreement was reached with the KCDC team  [24]  to include their data in a forthcoming CRDB release. Finally, we could also imagine CRDB as a portal to gather more CR-related data, for instance hosting the many nuclear cross-section data presented in Génolini et al. [129]. These future developments will depend on the workforce available and feedback from the CR community. To conclude, any help and feedback to further expand the database is welcome. Comments, questions, suggestions, and corrections are to be sent at crdb@lpsc. in2p3. fr. we use the text description to estimate the systematic uncertainties, combining quadratically the various sources of systematics. In case in which the systematics description is not clear enough, or when data are indirectly retrieved (i.
As this proves useful for several users, this approximate conversion is enabled since CRDB v1. 2. It takes as the dominant isotope the most abundant one in the Solar system  [130] , and this is defined, along with the associated values for A, Z, and m, in the ISOTOPE_PROXY table of the database (see Fig. 1 ). We list in Table  A2  the proxies enabled for the new CR_QUANTITY names introduced in CRDB v4. 0. • Balloon (1982/10): Garcia-Munoz et al. 1986  [191]  • Balloon (1984/04): Mueller and Tang 1987  [192]  • Balloon (1984/09): Garcia-Munoz et al. 1986  [191]  • Balloon (1987/08): Evenson et al. 1995  [193]  • Balloon (1990/08): Evenson et al. 1995  [193]  • Balloon (1992/08): Evenson et al. 1995  [193]  • Balloon (1994/08): Evenson et al. 1995  [193]  • AESOP94 (1994/08): Clem et al. 1996  [194]  • AESOP97+98 (1997/09+1998/08): Clem et al.
• E-AXIS: the database energy types are ETOT, EK, R, EKN, and ETOTN. In CRDB, they correspond to the total (E tot ) and kinetic energy (E k = E totm) set in GeV, rigidity (R = p/(Ze)) in GV, kinetic energy per nucleon (E k/n = E k /A) in GeV/n, and total energy per nucleon (E tot/n = E tot /A) in GeV/A. Depending on the detector principle (calorimeter, spectrometer, etc. ), different energy types are measured in experiments and provided in publications, and in CRDB, the data are always stored as published (see App. A. 4 for conversion). • E_MEAN, E_BIN_L, and E_BIN_U: fluxes are measured by counting events in energy bins. In CRDB, we include the energy interval range [E lo , E up ] when it is published. The measured flux obtained from a finite bin size neither exactly corresponds to the differential flux at the bin centre or at the mean energy E , unless a correction is applied.
Description of the 23 expected columns in the csv file prepared by the user to submit data. The table shows the column ID, the associated keyword in the database structure (see Fig. 1), the expected content of the column, and an example (and associated unit) for this column-entries corresponding to starred keywords can be left empty (i. e. " ") if the user is unsure. For more details on CRDB data and meta-data definitions, see §2. 1 (data points), §2.
Acknowledgments: We warmly thank  L. Baldini ,  F. Barao ,  M. Boudaud ,  B. Coste ,  C. Deil ,  T. Delahaye ,  L. Derome ,  F. Donato ,  M. Duranti ,  K. Egberts ,  C. Evoli ,  H. Gast ,  J. Gieseler ,  A. Gil-Swiderska ,  F. Giovacchini ,  I. Grenier ,  S. Haino ,  G. Jóhannesson ,  A. Karelin ,  J. Lavalle ,  M. Orcinha ,  M. Paniccia ,  A. Panov ,  A. Putze ,  T. Räihä ,  B. Shan ,  A. Strong ,  L. Sujie ,  M. Vecchi ,  A. Vincent ,  P. von Doetinchem ,  W. Xu , and  S. Zimmer  for their feedback that helped correct data in CRDB. D. M. thanks  C. Combet  for a careful reading of the manuscript, and  K. Lockhart  for her help on the use of the  ADS API. This research has made use of  NASA's Astrophysics Data System Bibliographic Services.
The different propositions of weights are presented in the "Methods" section. We studied the selection and prediction ability of these models and compared them to other frequentist approaches such as the IPF-Lasso, SGL, cMCP, and gel methods through a simulations study. The proposed methods were also applied to the real cancer data set mentioned above. Our results show that the adaptive lasso with the proposed product Max Single Wald by Single Wald weighting method outperformed the other methods in terms of reducing the false discovery rate without severely increasing the false negative rate.
The results show that the adaptive lasso with double weight (at individual and group level) is uniformly in the top-3 of the best performing methods. The cMCP and gel methods have in general very low FDR, but with a sensibly increased FNR, which reflects into a F1score which is at best in the average of all methods. Conversely, the SGL method has low FNR in general, but with relatively high FDR and then poor F1-score. In terms of selection of groups, the adaptive lasso with only group penalties perform the best, while the double penalties remain competitive. In a small high-dimensional sensitivity study, we created a more extreme case on the same dataset of simulation (a) with the number of biomarkers p = 1000 but only with Table  1  The (l = 1; q = 8) (l = 2; q = 8) (l = 1; q = 32) (l = 2; q = 32) (l = 2; q = 16) (l = 2; q = 8) HR∼ U(0.
S3 ). Enriched GO terms in the phylloxera genome, excluding TEs, related to host cell surface receptor binding, hydrogen ion Fig. 2  Comparative gene content across insects, with emphasis on Hemiptera. Total number of genes (right) or percentage of genome (left) are indicated transmembrane transporter activity, odorant binding, and olfactory receptor activity, which suggests that some of the phylloxera-specific gene expansions are involved in sensory perception (Additional File 1: Fig. S4 ). Among phylloxera-specific genes, 1115 had hits with InterProScan databases, indicating that they may have homologs outside the phylogenetic context of this study. This still leaves 5508 genes in the phylloxera genome with strictly no hit. These results are in line with those found for other aphids. For instance, 4530 genes were inferred as species-specific and/or orphan in Aphis glycines, which represents a 23.