dataesr/scientific-paragraphs-categorization huggingface dataset:
Zhang et al. [34]  hacks CPU registers, dedicated to the MMIO mechanism, to conceal a memory portion used to store malicious code. In-guest software is unable to access the hidden memory portions since all their operations pass through the CPU. The proposed mechanism is also able to evade physical memory forensic though DMA. Since HyBIS operates at the hypervisor level, it does not rely on the guest machine and it is then able to access the whole physical memory without restrictions. Hence, HyBIS is not affected by this kind of rootkit. Furthermore, at the hypervisor level it is possible to monitor the CPU in order to detect possibly unsolicited register modification and then to prevent this type of hacks. Harrison  [7]  suggests an approach that is somewhat similar to HyBIS. However it aims to be integrated into other IDS solutions and mostly focuses on the analysis phase.
Background Long-term benthic monitoring is rewarding in terms of science, but labour-intensive, whether in the field, the laboratory, or behind the computer. Building and managing databases require multiple skills, including consistency over time as well as organisation via a systematic approach. Here, we introduce and share our spatially explicit benthic database, comprising 11 years of benthic data. It is the result of intensive benthic sampling that has been conducted on a regular grid (259 stations) covering the intertidal mudflats of the Pertuis-Charentais (Marennes-Oléron Bay and Aiguillon Bay). Samples were taken by foot or by boats during winter depending on tidal height, from December 2003 to February 2014. The present dataset includes abundances and biomass densities of all mollusc species of the study regions and principal polychaetes as well as their length, accessibility.
Funding: The monitoring was funded by the University of La Rochelle and the CNRS via laboratory donations and staff time. Financial support was received from the Région Poitou-Charentes through a thesis grant to Anne Philippe  (2013) (2014) (2015) (2016). LIENSs laboratory and the DYFEA team also provided help with field work costs. The Office National de la Chasse et de la Faune Sauvage (ONCFS) and the Ligue pour la Protection des Oiseaux (LPO) supported this monitoring via staff time and nautical resources dedicated to sample collections.
5902 × AFDM -0. 0006 (R² = 0. 98; N=60 ind. sampled in July 2014 in Aiguillon Bay). For the same reason, dry mass of the shell (DM ) of small bivalves (< 8. 0 mm) was extrapolated using the following regression: DM = 8E-05 × Length 2,6523 (R² = 0,8274, N=122 Scrobiculalidae ind. and 51 M. balthica ind. sampled in January 2014 in Aiguillon Bay). Processing of benthic samples in the laboratory. In the present dataset, we removed all epibenthic species (e. g. crustaceans) since they are nearly absent from the feeding regime of shorebirds in our study area  (Bocher et al. (2014). Polychaetes were also identified and counted, but their length and AFDM were not determined due to insufficient numbers of entire individuals to build regression equations. Nearly all polychaete species were removed from the database because the precision of sample sorting and determination varied widely between the years depending on the laboratory operator.
Only individuals from the Genus Nephtys (more than 90% of which were Nepthys hombergii) and individuals of the Family Nereididae (more than 90% of which were either Alitta succinea or Hediste diversicolor) were kept because they represent more than 80% of polychaetes in our study sites and comprise the biggest annelids and therefore biomass and prey for shorebirds (pers. observation). They are mentioned under the abbreviations "nepsp" (for Nephtys genus) and "neresp" (for Nereididae family) in the database. For these two polychaete taxa, the columns "length", "width", as well as "AFDM", "Biomass_dens" and "NewTBH" are not available, only densities are available.
Taxonomic coverage Description: The dataset includes all occurrences of individuals for the taxa listed in Table  2. Annelids were grouped under the Genus Nephtys of the Family Nerididae. Ruditapes sp. was essentially individuals of the species Ruditapes philippinarum. Scientific names and corresponding AphiaID were derived from the World Register of Marine Species (WoRMS). The combination of both scientific name and AphiaID prevents any confusion when names change over time. Indeed, in ten years of collecting data, various operators have used a list of species that was an extract of a taxonomy (with description of phylum, subphylum, class, subclass, infraclass, superorder, order, suborder, infraorder, superfamily, family, genus, species, and authority). However, classification evolves throughout time, and our dataset needed to be backed to an official registry of species linked to the semantic Web.
Temporal coverage Notes: Sampling was undertaken during winter (between December and February) each year, starting in December 2003. The fieldwork team (about five to seven people) spent a yearly average of three weeks per year sampling during spring tides in winter season. For convenience in analyses, if sampling took place in December, the year associated with sampling corresponds with the following year in the dataset. For example samples taken in December 2006 will be attached to year 2007, together with samples from January 2007 or February 2007. First samples were taken in December 2003 (sampling year 2004) and last samples in this database in February 2014 (year 2014). In the subsequent years (starting in winter 2015 until now) sampling was performed over a reduced spatial extent (only 'AIC' subsite, i.
Usage rights Use license: Open Data Commons Attribution License IP rights notes: This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4. 0 International License. The associated dataset can be freely used for non-commercial purpose, provided it is cited. We draw your attention to the fact that you can contact the authors of the "benthos" dataset to prevent any misuse of the data. You can also consult us for reviewing articles based on our dataset.
Additional information Steps of database building The dataset was built via the association of different original tables at different steps of the collection, sorting, measuring and weighing of samples. After each sampling session, we filled in a table "CORE" with columns: "year", "Poskey", "car_mod_id" and "car_top_bottom" as well as unique identifier "core_id". Subsequently, in the laboratory while determining and measuring the samples, we filled in a table "BENTHOS" with columns: "abr", "number", "length", "width", "class_length", "T. B. H", "AFDM", "Shell_DM" and "core_id". A unique idendifier "id" was associated to each row in this table "BENTHOS". Preliminary to the tables "CORE" and "BENTHOS", a table "SPECIES" with the scientific name associated to the abbrevation "abr" was built, together with a table "STATIONS" with coordinates, site and subsite associated to the sampling station identifier "Poskey".
All these tables were associated together, in strict respect of integrity criteria specified through 'foreign keys' and 'primary keys' constraints, to form a relational database (Fig. 4 ) and later the flattened database "benthos" presented in this datapaper. Foreign key "core_id" in table "BENTHOS" had to match primary key "core_id" in table "CORE". Primary key "Poskey" of table "STATIONS" had to match foreign key "Poskey" of table "CORE". Primary key "abr" in table "SPECIES" had to match foreign key "abr" in table "BENTHOS". Benthic monitoring database building steps, from data collection to the production of results.
Cleaning process Step 1. Check integrity constraints, misspellings, uniqueness: The preparation of the dataset (e. g. checking for data types, misspelling, uniqueness of identifiers and integrity constraints as described in Table  2 ) was performed through building the relational database under PostGreSQL, following the principles described in Chapman 2005. The work was performed using SQL scripts, during and after the import of all raw CSV tables (BENTHOS, CORE, STATIONS, SPECIES), that were not linked together at the beginning, nor respecting all constraints for attributes type for instance. Some insights of the work led during this step are given in Table  3. Step Step 2. Outliers and NA, using regression models on data: Once the relational database was built, and integrity criteria met, the database was cleaned in a systematic way, species by species, site by site and year by year.
This step was done using R statistical software (R Development Core Team 2015). The first step aimed at unfolding the dataframe (i. e. producing one row for each sampled individual). Then the column "length" was cleaned from its outliers or potential typing errors using common methods (Hawkins 1980). Subsequently, using extrapolation curves, outliers and missing values were derived from the column "length" for "width", "DM " and "AFDM" following the R code (Suppl.
Shell Table  3. Integrity checks, data cleaning and inferring of missing data material 1), see (Fig. 5 ). This process led to substantial improvement of the dataset, see Table  3. Assigning sampling area to each station in each year, depending on the sampling method: The columns "area" and "area_hyd" were filled in according to the sampling method (either by boat "car_mod_id"= 2 or by foot "car_mod_id"=1). By boat, two cores are taken using a corer with a radius of 0. 050 m (area= 2 x (PI x 0. 050²)); by foot, one core was taken with radius of 0. 075 m (area= PI x 0. 075²). Hydrobia ulvae were counted in one of the two cores taken by boat (area= PI x 0. 050²), and if sampling was made by foot an additional smaller core was taken, with a radius of 0. 035 m (area= PI x 0. 035²), see Table  1.
Discussion Opportunities: There are multiple examples of the potential uses of those data for further exploitation. The multidimensional dataset can be explored for various questions and following spatial or temporal dimension. For instance, in Fig. 6 , the plots exemplify how spatial and temporal dimensions are associated, since we have time series for each station of each site. We can also examine how the harvestable biomass for sandpipers C. canutus or C. alpina (which means accessible because found in the "top" and respecting certain criteria for length (Fig. 2 ) is distributed amongst the various species, on a site for a given year (Fig. 7 ). The plot on the left shows that mean total biomass of the bivalve S. plana was 0. 24 g. m in 2014, varying between 0. 10 and 0. 25 g. m following site stations, with a mean value lower than biomass of M.
Limits of data use: The data was designed to estimate temporal changes and spatial differences in biomass and densities, as well as possible changes in quality, depth or community composition in our study sites. However, for predictions at unsampled locations, additional analyses are required, such as investigating spatial auto-correlation  (Bijleveld et al. 2012 , Kraan et al. 2010). Regression lines derived from the present dataset shall be valid only for the particular species, site and year and should not be used to extrapolate missing data in any other context. However, the scripts we provide in supplementary materials can be adapted to other database architectures to clean the data and produce results. help from Anne Philippe. Jérôme Jourde was in charge of taxonomy training and helped with identification and cleaning of the database when necessary.
In the present datapaper the column refers to the column class'. family Family associated with the individual. In the present datapaper the column refers to the column 'family'. genus Genus associated with the individual. In the present datapaper the column refers to the column genus'. English name English common name associated with the individual. In the present datapaper the column refers to the column 'English name'. authority The person credited to have formally named the species for the first time. In the present datapaper the column refers to the column 'authority'. nr Total individuals of this species in the database. In the present datapaper the column refers to the column 'nr'.
Acknowledgements: Precious support in the field was provided by the staff of the  Ligue pour la Protection des Oiseaux (LPO)  and the  Office National de la Chasse et de la Faune Sauvage (ONCFS) , working in National Nature Reserves of the  Region Poitou-Charentes  ( L. Jomat ,  S. Gueneteau ,  S. Haye  and many others). We thank the managers of the  National Nature Reserve of Aiguillon Bay ,  E. Joyeux  and  F. Corre  and the manager of the  National Nature Reserve of Moëze-Oléron P. Delaporte  for their support and help. Sorting, identifying, measuring and weighing of benthic invertebrates were also the tasks of several interns and PhD candidates. Many colleagues from the laboratory helped occasionally: V. Huet regularly helped us with weighing samples and  T. Guyot  with sieving of cores.
The World Register of Marine Species (WoRMS, World Register of Marine Species 2016b) provides unique AphiaID for identifying marine species, and maintains a historic of changes of naming or classification. Furthermore, it provides semantic export of species description in RDF compatible with Darwin Core or Dublin Core metadata standards. We used this register in order to disambiguate the identification of species found in the dataset. For that, Attribute Column_name Description Units Constraints AphiaID AphiaID Unique number - Integer corresponding to a described species in WoRMS Period "Period" Winter period - Text, ["Winter 2003-2004" to "Winter corresponding to 2013-2014"] the sampling we have written a Python program querying the Web Service (World Register of Marine Species 2016a) provided by the World Register of Marine Species (WoRMS), for searching and resolving AphiaIDs of our species list, using the WSDL interface (World Register of Marine Species 2016c).
For each record in our table, the program sends the name of the species in parameter of the querymatchAphiaRecordsByNames and the service finds for us matching taxa; the program asks then for the full AphiaRecord of the taxon ( getAphiaRecordByID), parses it and checks it against our own classification. When a AphiaRecord matches our own table record, we retain it, else we log the uncertain case in order to search manually in the web interface. Only few cases (around 20), identified in the logs of our program, have been searched and solved manually using the web interface of WoRMS. The result is that we provide within our dataset the actual taxonomy of our species extracted from WoRMS, and most importantly their corresponding AphiaID in WoRMS.
This dataset is hosted by EurOBIS and can be downloaded using the link above (see section 'Usage rights' for more information). The column names used in the present dataset don not always match the ones in the EurOBIS dataset. The Tablebelowindicates this correspondance between the column names in the datapaper (including the names used in the script) and the column names in the EurOBIS dataset. Data package title: BenthosResource link: https://doi. org/10. 14284/247 Number of data sets: 1 Data set name: Benthos monitoring in the intertidal mudflats of Pertuis-Charentais (Bay of Biscay) from 2004 on Download URL: https://doi. org/10. 14284/247 Data format:. csv Description:.
In 2012, preliminary guidelines were published addressing sample quality, data acquisition and reduction, presentation of scattering data and validation, and modelling for biomolecular small-angle scattering (SAS) experiments. Biomolecular SAS has since continued to grow and authors have increasingly adopted the preliminary guidelines. In parallel, integrative/hybrid determination of biomolecular structures is a rapidly growing field that is expanding the scope of structural biology. For SAS to contribute maximally to this field, it is essential to ensure open access to the information required for evaluation of the quality of SAS samples and data, as well as the validity of SAS-based structural models. To this end, the preliminary guidelines for data presentation in a publication are reviewed and updated, and the deposition of data and associated models in a public archive is recommended.
Introduction The objective of publishing the preliminary guidelines for biomolecular small-angle scattering (SAS) experiments  (Jacques, Guss, Svergun et al. , 2012; Jacques, Guss & Trewhella, 2012)  was to provide a reporting framework so that 'readers can independently assess the quality of the data and the basis for any interpretations presented'. The focus was on  ISSN 2059-7983  solution SAS experiments, both small-angle X-ray scattering (SAXS) and small-angle neutron scattering (SANS), where the primary goal is the generation and testing of threedimensional models. The 2012 guidelines, which were developed in consultation with members of the SAS and Journals Commissions of the IUCr and other experts in the field, are now used by many authors and are endorsed by IUCr Journals (http://journals.
iucr. org/services/sas/). Since the preliminary publications appeared, the Worldwide Protein Data Bank (wwPDB) established the Small-Angle Scattering Validation Task Force (SASvtf; https:// www. wwpdb. org/task/sas), which has made recommendations regarding the archiving and validation of SAS data and models  (Trewhella et al. , 2013). Furthermore, the wwPDB Integrative/Hybrid Methods (IHM) Validation Task Force was formed  (Sali et al. , 2015)  to address the complex issues concerning the archiving and validation of models of biomolecular complexes and assemblies that depend upon computational methods and data from independent experimental techniques, including SAS. There also have been substantial advances in analysis tools for SAS  (Franke et al. , 2015; Rambo & Tainer, 2013b; Schneidman-Duhovny et al.
, 2013; Petoukhov & Svergun, 2015; Konarev & Svergun, 2015; Petoukhov et al. , 2012; Chen & Hub, 2015; Spinozzi et al. , 2014; Bizien et al. , 2016)  and instrumentation, in particular the growth of SAS experiments utilizing inline purification and characterization  (Blanchet et al. , 2015; Jordan et al. , 2016; Graewert et al. , 2015; Brookes et al. , 2013 Brookes et al. , , 2016;; Bras et al. , 2014; Meisburger et al. , 2016; Ibrahim et al. , 2017). In regard to modelling SAS data, there has been significant increased interest and methods development in multistate/ensemblebased methods for flexible biomolecules  (Tria et al. , 2015; Berlin et al. , 2013; Schneidman-Duhovny et al. , 2016; Perkins et al. , 2016; Kikhney & Svergun, 2015; Terakawa et al. , 2014)  and structural modelling based on combined SAS and NMR data  (Schwieters & Clore, 2014).
The latter places especially stringent requirements on the accuracy and precision of SAS data. The recommendations of the SASvtf  (Trewhella et al. , 2013 ) have progressed substantially with regard to model validation and archiving. Work also has begun on the community discussions and technical developments required to develop a federated system of data banks to support the dissemination and validation of integrative/hybrid models  (Sali et al. , 2015). In particular: (i) a standard dictionary with definitions of terms for collecting and managing SAS data as well as facilitating data exchange between laboratories and data banks has been developed  (Kachala et al. , 2016) , building upon the sasCIF  (Malfois & Svergun, 2000)  that was originally developed as an extension of the core Crystallographic Information Framework (CIF); (ii) a freely accessible and fully searchable SAS experimental data and model data bank (SASBDB; https:// www.
sasbdb. org/; Valentini et al. , 2015)  has been established to be part of an envisioned federated system of interoperable data banks supporting hybrid data and model validation. The SASvtf report reiterated the importance of the recommended preliminary publication guidelines and expanded on them, further stating that 'criteria need to be agreed upon for the assessment of the quality of deposited data, the accuracy of SAS-derived models, and the extent to which a given model fits the SAS data'. In the light of the above developments, it is timely to update the preliminary publication guidelines. We have followed the same structure as previously, with four sections covering (i) sample quality, (ii) data acquisition and reduction, (iii) the presentation of scattering data and validation, and (iv) structure modelling.
Each section briefly describes the relevant context with a tabulated summary of the specific information to be reported. Importantly, we have added a recommendation that SAS data and models, along with the details of the experiment as described in each of the four sections here, be deposited in a public data bank. An example report is provided at the end of these sections for a specific set of sizeexclusion chromatography SAXS (SEC-SAXS) experiments in a form that is consistent with the guidelines and demonstrates the value of complete reporting. While many of the recommended guidelines are best practice for biomolecular SAS generally, our main focus remains on experiments aimed at three-dimensional structural modelling from solution SAS data. As such, SAS experiments aimed at understanding highly heterogeneous mixtures, transient species using timeresolved data, or high-throughput screening experiments are not explicitly considered as each of these important applications would have distinct attributes that need to be considered separately in detail.
Context for the guidelines 2. 1. Sample quality Given the paramount importance of sample preparation and characterization for biomolecular structure modelling using SAS data, sample quality must continue to be emphasized. A SAS profile can be measured from any sample and, unlike crystallography and NMR where there are both quantitative standards and internal controls for assessing sample and data quality, a SAS profile by itself does not provide sufficient information for such assessment. Fundamental to the successful interpretation of a biomolecular SAS experiment in terms of structural models is that the scattering data are demonstrated to be from a highly purified solution of monodisperse particles in the dilute solution regime. This means that the SAS data are free of contributions from contaminants and the effects of nonspecific aggregation or inter-particle distance correlations.
Traditionally, solution SAS data for structural evaluation and modelling have been collected at multiple concentrations of the particle of interest to evaluate and eliminate concentration-dependent contributions to the scattering through the strategic choice of solvent conditions or extrapolation to infinite dilution. The molecular mass (M) or volume (V) of the scattering particle then can be estimated from the zero-angle scattering, I(0). The calculation of M or V from I(0) requires accurate concentrations of the sample constituents to be determined, which can be challenging. While UV-based determination of concentration can be difficult for some systems (for example proteins with few aromatics or with solvents containing UV-absorbing components), concentration can often be determined to better than 10% accuracy  (Gasteiger et al.
For SANS data, contributions to the background from incoherent scattering can also prove problematic as the incoherent scattering cross-section of 1 H is 10-20 times the total scattering cross-sections of other nuclei present in a biomolecule  (Jacrot, 1976). As a result, solvent subtractions for SANS data with significant 1 H often include adjustments by an ad hoc addition or subtraction of a constant to force the scattering at high angles to approximately zero. The need for this adjustment can be minimized by using a final dialysate as the solvent blank from dialysis that has been maintained in a closed environment to avoid differential 1 H-2 H exchange and calibrating sample and solvent transmissions against pure 1 H 2 O and 2 H 2 O. Developments of inline purification of samples using sizeexclusion chromatography (SEC) at synchrotron SAXS beamlines (see, for example,  Brennich et al.