dataesr/scientific-paragraphs-categorization huggingface dataset:
Similarly, the simpler age-based stock assessments require growth parameters, size at maturity, spawning and natural mortality data  (Jardim et al. 2015)  while some surplus production models use the maximum intrinsic population growth that is based on several biological characteristics including growth, fecundity, maturity and natural mortality  (Froese et al. 2018a (Froese et al. ,b, 2020)). Among European Seas, the North East (NE) Atlantic Ocean is a marine ecosystem with a long scientific history of investigations across all marine science disciplines  (Lotze & Worm 2009). As a result, long time-series of biological, oceanographic and fisheries data exist, most of which are publicly available. These datasets have supported many ecosystem models  (Keramidas et al. 2023)  and the official full stock assessments in most marine ecosystems of the NE Atlantic (ICES 2022).
2023). This also restricted full analytical stock assessments to a proportionally low number of exploited stocks  (Piroddi et al. 2020 ) despite the increasing efforts of the Expert Working Groups (EWG) of the Scientific, Technical and Economic Committee for Fisheries (STECF) of the European Union and the General Fisheries Commission for the Mediterranean Sea (GFCM) of the FAO. However, the most important issues in Mediterranean fisheries are the northsouth gradient in marine research and data, with more scientific output along the northern Mediterranean coastline  (Stergiou & Tsikliras 2006) , and that valuable datasets are not openly available  (McManamay & Utz 2014). This is because some people/institutions (and countries, which were historically amongst the most scientifically advanced) do not believe in open science -a policy priority for the European Commission -even when supported by public funds  (Damalas et al.
Materials and methods This review was based on information that was extracted from the literature and captured in SeaLifeBase (www. sealifebase. org; Palomares & Pauly 2021, consulted in December 2021) for eight marine ecosystems (Adriatic Sea, Aegean Sea, Baltic Sea, Bay of Biscay/Celtic Sea/Iberian Coast, Black Sea, Levantine Sea, North Sea, and western Mediterranean Sea) (Figure  1 ). SeaLifeBase is a global biodiversity information system on non-fishes that covers a wide range of information on taxonomy, biology, trophic ecology, life history and uses  (Palomares & Pauly 2021 ). The extensiveness of information in the database has catered to a diversity of stakeholders (scientists, researchers, policy-makers, fisheries managers, donors, conservationists, teachers, and students) for various applications targeting sustainable fisheries management  (Froese et al.
The least-studied species were those that do not meet the above criteria (Table  1 ). Overall, the number of unique references for data on biological characteristics from the literature captured in SeaLifeBase total 637 records  (Palomares & Pauly 2021). Species were categorized as threatened/non-threatened according to the species conservation status in SeaLifeBase which follows the IUCN Red List of Threatened Species (version 2021-1) considering the global classification of species (EX: Extinct; EW: Extinct in the Wild; CR: Critically Endangered; EN: Endangered; VU: Vulnerable; NT: Near Threatened; LC: Least Concern; DD: Data deficient; NE: Not Evaluated). For this review, species categorized as threatened included only those flagged as Critically Endangered (CR), Endangered (EN), Vulnerable (VU) and Near-Threatened (NT).
There are seven well-studied species across the reviewed ecosystems: Common cuttlefish (Sepia officinalis), Giant red shrimp (Aristaeomorpha foliacea), Spottail mantis shrimp (Squilla mantis), Common spiny lobster (Palinurus elephas), Blue crab (Callinectes sapidus), Green sea turtle (Chelonia mydas) and Deep-water rose shrimp (Parapenaeus longirostris). These species have good coverage of biological information and sufficient data records for use in ecosystem assessments and modelling. Of these, the most common well-studied species include the Common cuttlefish (Sepia officinalis), which is reported within six sites and is highly commercial  (Pereira et al. 2015). The current absence of data for the studied taxonomic groups may be due to actual absence of real data (i. e. , lack of studies on non-fish marine organisms) or time-lagged entering of research publications in SeaLifeBase or that source of information has not been considered (for instance, grey literature or local journals).
Contrary to LWR that are easier to collect and compute, the sample collection and laboratory work required to determine the diet and fecundity of specimens are costly and timeconsuming and require technological equipment and advanced expertise  (Dimarchopoulou et al. 2017). In contrast, natural mortality can be easily calculated using existing datasets based on the many known empirical equations that are available (constant across ages/sizes:  Pauly 1980 , Then et al. 2015 ; size/age-based: Chen & Watanabe 1989,  Gislason et al. 2010 ) without any extra cost or sampling that would be required if other methods were selected (tagging:  Krause et al. 2020; lengthbased and age structured models: Lorenzen 2022). Therefore, it is strongly recommended, at least for decapod crustaceans and cephalopods, to report maximum age in every study in which growth parameters are determined and, if possible, to calculate and report natural mortality.
2007) , filling gaps in survey datasets  (Coro et al. 2022) , the effect of COVID-19 on fish stocks  (Scarcella et al. 2022 ) and fisheries in general, especially in the western part of the sea  (Lotze et al. 2011). There are even some historical data available for large marine animals  (Lotze & Worm 2009). Non-indigenous species have also been extensively studied in terms of their effect on the food web dynamics  (Libralato et al. 2010 (Libralato et al. , 2015)). It appears that the data collected from scientific surveys on non-fish marine organisms either remain unpublished, or do not include the biological characteristics covered in this review. Furthermore, they have potentially not yet been included in SeaLifeBase.
With such a wealth of biotic and abiotic information on the ecosystems of the Baltic Sea ecosystems, with long-term datasets of many marine groups available since the 1950s and some expeditions dating back to 1850s  (Ojaveer et al. 2010) , it is surprising that the basic biological characteristics for many marine organisms supporting ecosystem models and assessments have not been published. The possibility that this information is published but has not yet been scrutinised by SeaLifeBase is also a potential explanation especially in case of local or not yet digitised journals.
Demographic and Health Survey Service Provision Assessment Survey Overview We used data from the DRC Demographic and Health Survey (DHS) Service Provision Assessment (SPA) collected from October 2017 to April 2018 to describe facility readiness for our first aim and to describe PAC and medication abortion service provision before the decriminalization of abortion in 2018 for our second aim. The SPA-funded by the United States Agency for International Development, the United States President's Malaria Initiative, and the Global Fund-is designed to collect information regarding health service availability and delivery within country-wide health systems using five core components: the Inventory, Health Worker Interview, Newborn Resuscitation Simulation, Observation Protocols, and Client Exit Interview questionnaires.
Additionally, it is important to note that the WHO recommends the use of vacuum aspiration for surgical abortion or medication abortion for pregnancies up to 14 weeks and for treatment of postabortion complications; it suggests that these methods replace dilation and curettage (D&C) due to safety issues  [16]. However, the SPA Inventory Survey asks about a functioning "vacuum aspiration kit or D&C kit", and these two methods could not be separated in the data; hence, we included the D&C kit in our signal functions despite it no longer being a recommended termination or PAC method. Each signal function indicator was operationalized as a binary variable to denote availability. Equipment needed to have been observed and recorded as functioning while commodities needed to be observed and with valid expiration date at the time of data collection.
Performance monitoring for Action Service Delivery Point and female surveys Survey Overview We used data from the Performance Monitoring for Action (PMA) initiative to describe PAC and medication abortion service provision post-decriminalization of abortion in 2018 for our second aim and to examine accessibility of services in Kinshasa and Kongo Central for our third aim. PMA conducts female, household, and service delivery point (SDP) surveys in the DRC using resident female interviewers who collect information in their communities on reproductive health services and women's reproductive health history and practices using smartphones. Full details of the PMA initiative, including study and sampling design, questionnaires, and data, are available online  [20]. We used cross-sectional female and SDP data from the Kinshasa and Kongo Central Phase 3 surveys collected in December 2021-April 2022.
Accessibility measures We linked the SDP and female samples using geospatial data to examine the percentage of women who have access to facilities providing abortion services. Global Positioning System points were taken at the time of each SDP and female interview, and each woman's distance to any sampled facility and to a facility that provided PAC, misoprostol, or the combipack was calculated in kilometers using Euclidean distance, a method applied in a similar study exploring accessibility of services  [9]. Four access variables were developed, assigning either 0 or 1 to each woman based on whether she had access-defined as being within a three km distance-to any facility or a facility providing PAC, misoprostol, or the combipack. Facility type was recorded differently between the two data sources.
The descriptive analyses were weighted to account for the probability of selection and non-response. Our second aim was to examine PAC and medication abortion provision before and after the decriminalization of abortion in the DRC in 2018. We used the SPA data to describe the proportion of facilities providing PAC, misoprostol, and the combipack before decriminalization and the SDP data for after decriminalization. We applied post-stratification weights to the SDP data to reflect the  representative distribution of facilities in the SPA within each province. Provision of the combipack was only calculated using the SDP data, because information about mifepristone was not collected in the SPA. Proportions were presented by facility type and managing authority for each province. Our third aim was to examine accessibility in each province using the linked PMA data.
To minimize this limitation in the health system, investments in solutions focusing on ensuring a well-functioning supply chain are needed. The shortage of misoprostol stock may also be related to its relatively recent introduction to the country's essential medicines list in 2012 -only five years prior to the SPA data collection-combined with supply chain challenges. Recent introduction may also explain the low levels of combipack provision, given that mifepristone was not registered in the DRC until 2020. Because both medications were only recently introduced and given the changes in abortion legality in the country, health providers may not have had adequate training or be willing to provide these services  [15]. The low provision of medication abortion is especially pronounced in rural areas than the capitol province, and our accessibility results pointed to a greater need among poor women and those of lower education attainment who were least likely to live near a facility offering these services.
Additionally, the SPA Inventory did not collect any information on mifepristone stock at facilities despite it being added to the WHO essential medicines list in 2005 after WHO guidelines recommended the use of the combination regimen (mifepristone and misoprostol) for medication abortion  [25]. Mifepristone was registered in the DRC more than two years after the 2017-2018 DHS SPA and so was not included in the survey, therefore we were only able to look at the availability of misoprostol and not the combipack in the SPA. The SPA survey also collected information on the number of health providers at different occupational levels yet did not include any data on specific abortion care training for these positions. This restricted our ability to assess staff readiness based only on the presence of occupational categories at facilities and not on the capacity of staff to perform the necessary abortion procedures.
Lastly, the SPA dataset only enabled us to examine signal functions and facility readiness right before the codification of the Maputo Protocol in 2018. Ideally, we would have used the PMA data for a more recent status of these measures, but the data were not collected in the SDP survey. Additional limitations were present in the PMA data. First, our analysis did not include pharmacies, because they were not asked abortion-service-related questions. We may have eliminated a major source of medication abortion with their exclusion, as findings from the Kinshasa Abortion Study confirmed that women who relied on medication abortion often obtained them from pharmacies  [24]. Second, we analyzed access to PAC or medication abortion using proximity to a facility, defined as less than or equal to three kilometers, but our narrow definition of access excludes other factors that may influence women's access to services, such as costs, transportation availability, and knowledge of services provided.
The EPICOVIDEHA electronic case report form is available at www. clinicalsurveys. net (EFS Summer 2021, TIVIAN GmbH, Cologne, Germany). The anonymized electronic clinical report form is the single access for each participating institution and is structured in different thematic pages, as follows: (1) identification, (2) demographics, (3) underlying diseases, (4) HM, (5) COVID-19, and (6) outcome. Cases could be registered according to the following inclusion criteria: (1) age equal to or greater than 18 years, (2) history of active HM at any stage within the last 5 years before COVID-19, and (3) report of SARS-CoV-2 positive test documented by real-time reverse transcriptase polymerase chain reaction (RT-PCR) diagnostic panels. Qualitative demographic and clinical data were described by frequencies and percentages, while medians and interquartile ranges (IQR) were used to summarize quantitative variables.
[31] [32] [33]  This study enrolled many patients involved by the first SARS-CoV-2 waves and thus a very low percentage of patients had received at least one vaccine dose. The present report also suggests to devote intensified preventive measures to patients with MPN receiving immunosuppressive agents. There are limitations of our study that must be acknowledged. First, MF patients are likely to be slightly overrepresented in our series, but we could not attribute this skewness to the higher severity of COVID-19 in MF patients. Second, as we included patients from different countries and centers, and diagnosed in different waves of the pandemic, a certain heterogeneity in our data is to be expected. In addition, only a small number of patients in this series were vaccinated: this is due to the fact that most data were collected during early waves, where vaccinations were not broadly available.
Acknowledgements  Toni Valkovic ˇ ,  Christian Bjørn Poulsen ,  Klára Piukovics ,  Monica Piedimonte ,  Irati Ormazabal-Vélez ,  Hans-Beier Ommen ,  Joseph Meletiadis ,  Maria Stamouli ,  Marina Machado ,  Austin Kulasekararaj ,  Carolina Garcia-Vidal ,  Nicola Fracchiolla ,  María Fernández-Galán ,  Noemí Fernández ,  Rita Fazi ,  Alessandro Busca ,  Marcio Nucci ,  Milan Navrátil ,  Carolina Miranda-Castillo ,  Nick De Jonge ,  Michelina Dargenio ,  Annarosa Cuccaro ,  Barbora Weinbergerová ,  Jens Van Praet ,  Zlate Stojanoski ,  Uluhan Sili ,  Guldane Cengiz Seval ,  Ikhwan Rinaldi ,  László Imre Pinczés ,  Jan Novák ,  Jorge Loureiro-Amigo ,  Lisset Lorenzo De La Peña ,  Tobias Lahmer ,  Jorge Labrador ,  Nina Khanna ,  María-Josefa Jiménez-Lorenzo ,  Anna Guidetti ,  Tomás-José González-López ,  Iker Falces-Romero ,  Maria Ilaria Del Principe ,  Cristina De Ramón ,  François Danion ,  Nicola Coppola ,  Maria Calbacho ,  Nathan Bahr ,  Natasha Ali ,  Osman Ilhan ,  Ramón García-Sanz ,  Josep Maria Ribera-Santasusana ,  Agostino Tafuri ,  Nicole García-Poutón ,  Sofia Zompi ,  Mariarita Sciumè.
Anaerobic digestion is developing in various countries worldwide to produce renewable energy. In addition, the resulting digestates provide readily available nutrients when applied to cropping systems as fertilizers. The introduction of a biogas plant on a farm can induce land cover changes, in relation to the production of feedstock for the biogas plant and/or to the modification of the farming system. The aim of this study was therefore to characterize and quantify the land cover changes in farms associated with biogas plants in France. We combined two national spatialized databases: the Land Parcel Identification System (yearly French land cover at the parcel scale with farm identifier per parcel) and the SINOE database (biogas plant location and year of start-up). We showed that, on average, the changes were limited, with an increase in maize areas (+3.
| MATERIALS AND METHODS 2. 1 | Spatialized database used 2. 1. 1 | French LPIS data LPIS data are used to manage European agriculture subsidies  (Levavasseur et al. , 2016). In France, LPIS with farm identifiers is available yearly from 2007, but the data format changed in 2015. From 2007 to 2014, the reference unit was the block (one or several agricultural parcels cultivated by a single farmer), and the land cover was described in a nomenclature of 28 groups of crops (e. g. , "maize", which included silage and grain maize, or "barley", which included spring and winter barley). An anonymous farm identifier was given to each block, but the farm identifier changed each year. Since 2015, the reference unit has been the agricultural parcel, and the land cover is described in a nomenclature of more than 300 crops.
The anonymous farm identifier remains unchanged every year. Both before and after 2014, the land cover data exhaustively described only the main crops, not the cover crops. For simplification purposes, we included fodder crops (except silage maize) in the temporary grasslands (i. e. , grasslands of less than 5 years) because the distinction was not clear in French LPIS data. 2. 1. 2 | The SINOE database on biogas plants We used the SINOE database  (Ademe, 2022) , built by Ademe, the French national agency for the ecological transition, which represented the biogas plants in activity in France. The database was downloaded in July 2022 and contained 1309 biogas plants with their location (latitude and longitude). The available information indicates whether the biogas plants are managed by one or more farms (without providing any details that allow the direct identification of the farm), by agro-industries, waste water treatment plants or others.
In our study, we focused on the 895 biogas plants managed by farms and for which it was possible to establish by geospatial methods the relationship between the location of the biogas plant and at least one farm (see Section 2. 2). The location of each biogas plant was checked manually with the help of aerial photography and was corrected if needed. The start-up year of each biogas plant was also obtained from the SINOE database. The number of biogas plants strongly increased first in 2012 and then in 2018 (Figure  S1 ). This corresponded to the implementation of various regulations favoring the development of anaerobic digestion, first with cogeneration and then with gas injection  (Berthe et al. , 2022).
| Identification of a farm associated with each biogas plant To identify a farm associated with a biogas plant, we used the farm identifier associated with each parcel of the LPIS data (or with a block for LPIS before 2015). We looked to which farm a specific parcel associated with the biogas plant belonged. This parcel could be (Figure  1 ): 1. The parcel on which the biogas plant was located in the LPIS data a. in the year of the biogas plant start-up, or the following years (and therefore declared as nonagricultural areas in LPIS), b. in the years before biogas plant start-up in the case that the parcel no longer existed in the LPIS after biogas plant start-up. 2. The closest parcel in the LPIS data in the year of biogas plant start-up if no parcel was ever present at the biogas plant location.
We retrieved the land cover of each farm associated with a biogas plant from LPIS data for both periods, before and after the start-up. To do so and for each farm, we intersected the LPIS data of the years for which we wanted to retrieve land cover with the LPIS data of the year the farm was identified. The farm that intersected more than 50% of the farm of the LPIS data the year of farm identification was considered the same farm. If less than 50% of the farm areas were in common between the 2 years, we considered that we could not identify the farm throughout the considered period, and the farm and the biogas plant were removed from our study. Once the farm identified each year of the considered periods, we retrieved its land cover. Finally, we computed the mean land cover of each farm (in % of the total farm area) for the period before and after the start-up of the biogas plant (average over 3 years), as well as Shannon's diversity index.
ANOVA was also used to test if the changes in crop area proportions after and before biogas plant start-up were significantly different between the farms associated with biogas plants and their control surrounding areas. The effect of farm size (total farm area, computed from LPIS) and biogas plant size (estimated with the yearly maximal amount of feedstock authorized documented in the SINOE database) on the land cover changes were tested with a Kendall rank correlation coefficient (linear model assumptions violated). The effects of NUTS2 unit and of the type of biogas utilization pathway (gas injection or combined heat and power, documented in SINOE) on the land cover changes were tested with a Kruskal-Wallis test.
| Farms associated with biogas plants Over the 895 biogas plants associated with one or several farms, we removed four plants because their exact location could not be validated (uncertainty in latitude and/or longitude). We removed three duplicated plants. We removed four plants because the start-up year could not be found in the SINOE database. We removed 469 plants because they started too early (before 2011) or too late (after 2018) with regard to available LPIS data. We removed seven plants because the identification of the associated farm over years could not be ensured (major changes in farm areas over years). We removed five plants because of issues in the LPIS data. We removed 11 plants because the closest farm did not respect the threshold of 70% of farm area in the surrounding 200 m (see third identification method, Section 2.