dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the most noticeable improvement is for FP30 and FP100, which before Table  2. Results of the bundle adjustment with self-calibration for the simulated dataset before and after the polynomial distancedepend correction correction showed the highest standard deviations and residual errors and where the image residuals RMSE has been reduced by approximatively the 93% post correction. Figure  5  reports the result of the accuracy assessment in Z (the weakest measurement direction) for the tie points estimated for all the simulated datasets. As with the internal assessment of bundle adjustment (Table  2 ), there is a clear improvement in the statistics, which in the most critical case reaches around 86% reduction in error in Z. Compared to the results obtained in  (Menna et al. , 2020) , we note that the method adopted here is effective in reducing image residuals and errors in the object space with both misaligned dome port and planar surfaces (flat port) at different distances from the entrance pupil.
ACKNOWLEDGEMENTS This study was partially realized within the  Moorea Island Digital Ecosystem Avatar (IDEA)  project, with the financial and scientific support of  Prof. Matthias Troyer  through the  Institute of Theoretical Physics, ETH Zurich , the  U. S. National Science Foundation  under Grant No. OCE 16-37396  (and earlier awards) as well as a generous gift from the  Gordon and Betty Moore Foundation. The  IDEA  project is executed under permits issued by the  French Polynesian Government  ( Délégation à la Recherche ) and the  Haut-Commissariat de la République en Polynésie Francaise (DTRT)  ( Protocole d'Accueil   2005-2018 ). The authors thank  Dr. A. Brooks  ( Marine Science Institute, University of California, Santa Barbara -UCSB, USA ),  Prof. A. Capra  ( DIEF Department, University of Modena and Reggio Emilia, Modena, Italy ) and the  UCSB Gump station team in Moorea  for their crucial support in the data acquisition.
a b s t r a c t Environmental data on organic products are needed to assess their environmental performance. The purpose of the ACV Bio project reported here was to generate environmental data as life cycle assessment (LCA) data for a sample of French organic production systems including cropping systems (annual crops, intercrops, forages), grassland, wine grapes, cow milk, calves, beef cattle, sheep, pigs, broilers and eggs. LCA was used to estimate environmental impacts of products from these systems. Recommended uses are to characterize part of the diversity of French organic farming systems and some of their environmental impacts, identify areas for improvement, perform eco-design and sensitivity analysis, and/or make system choices in a given context. However, these data do not represent average French organic products and should not be used as such.
The MEANS-InOut web application was used to generate life cycle inventories (LCI). Impact assessment was performed using SimaPro v9 software. The Environmental Footprint 2. 0 characterisation method was used to generate LCA data. These data were supplemented with three LCA indicators: cumulative energy demand, land competition (CML-IA non-baseline) and biodiversity loss. Three non-LCA indicators were also calculated for certain systems: diversity of crop families (for cropping systems), agro-ecological infrastructure (for sheep) and pesticide treatment frequency index (for grapes). In total, 173 products were modelled. LCA and non-LCA data are available in the Microsoft® Excel file at Data INRAE ( https://doi. org/10. 15454/TTR25S ). LCI data are available in the AGRIBALYSE database and can be accessed using SimaPro and openLCA software.
Value of the Data • Data can be used to identify ways to reduce the environmental impacts of organic plant and animal products. • Any life cycle or agricultural modeller may benefit from these data. • Data can be used to assess products from French organic farming and quantify several of their environmental impacts per kg of product and per ha of land occupied. • Data were produced for 173 crop and animal products. • Data have relevance for France and, more generally, for temperate climate regions. It is recommended, however, to adapt the LCIs to the region concerned. • Data are available as farmer practices in the MEANS-InOut web application (on demand), as LCI data in SimaPro v9. 0 and openLCA software, and as LCA data in the "Data INRAE" repository.
Data Description In France, organic farming occupied 2. 3 million ha in 2019, which corresponded to 8. 5% of agricultural land and 10. 4% of farms. Organic farming is often perceived as a way to produce food that has lower environmental impacts than that from conventional farming. Life cycle assessment (LCA) is a methodology that estimates environmental impacts of a product, system or service along all or part of its life cycle. LCA has been used to estimate environmental impacts of organic farming  [1] [2] [3] [4] [5]  , but most of the studies that did so focused on a limited number of products and a few environmental impacts, such as climate change and energy use. Several LCA agricultural databases exist, such as the Agri-footprint and World Food databases, but they contain little or no data on organic farming systems and products.
AGRIBALYSE is a French database of life cycle inventories (LCIs) of agricultural products at the farm gate. It originally contained LCIs for 113 products, but only 13 of them came from organic farming. For the most important products from conventional farming, several variants of the same product were described (from different production regions and production systems), thus covering some of the diversity of production systems. For organic products, however, only one variant existed for each product, sometimes to represent a national average. Consequently, the diversity of production systems in organic farming was not apparent, even though it does exist, as in conventional farming. Thus, data on organic farming needed to be generated to assess its environmental impacts. To address this lack of data, the research project ACV Bio, funded by the French agency for ecological transition (ADEME), and the French Ministry for Ecological Transition, was launched.
Its main objective was to produce LCI and LCA data on a variety of plant and animal products from French organic farming at the farm gate. The LCI data were integrated in July 2020 into the AGRIBAL-YSE database and are now available in SimaPro (Pré Consultants, Amersfoort, Netherlands) and openLCA (GreenDelta, Berlin, Germany) software. Here, we present how we generated LCI and LCA data for a diversity of organic production systems and products, including cropping systems (annual crops, intercrops, forages), grassland, grapes, cow milk, calves, beef cattle, sheep, pigs, broilers and eggs. Data were produced using the Environmental Footprint 2. 0 (EF) characterization method  [6]  for two functional units: 1 kg of product and 1 ha of land occupied. Three additional LCA indicators were calculated: cumulative energy demand (CED)  [7]  , land competition (CML-IA non-baseline)  [8]  and biodiversity loss  [9] [10] [11].
Data generation LCI data for products of French organic farming were produced: annual crops, intercrops, forages, grassland, grapes, cow milk, calves, beef cattle, sheep, pigs, broilers and eggs ( Table  1  , see Table  S1  for the list of cropping systems). Each annual or perennial crop, forage and intercrop was modelled individually, and some of them were modelled in complete cropping systems (i. e. crop rotations). In total, 173 LCIs were modelled, some of which describe several cases of a given product (from different regions or farms). Hence, a variety of production systems were covered for most products The dataset produced contains 173 LCAs based on these LCIs of organic crop and animal products (Table  S2 ). LCA data for the EF, CED, land competition and biodiversity loss methods, as well as the three non-LCA indicators (DCF, AEI and PTFI), are available in the Microsoft® Excel file available at Data INRAE ( https://doi.
Life cycle inventories Farmer-practice data used to create LCIs came from multiple sources. For annual crops and cropping systems, data came from "typical cases" 1 created by ARVALIS in two research projects: OléAB and ProtéAB. For intercrops, data were based on expert knowledge from TERRES INOVIA, the Chambers of Agriculture of the Hauts de France and Pays de la Loire regions, and INRAE (UMR AGIR). Data for grapes came from real vineyards in the Pays de la Loire and Alsace regions. Each vineyard plot selected for this study represented one of the management types identified in each studied region using a typology. To address the perennial aspect of vineyards, data were collected for 2-4 productive years representative of the climate of the full life cycle, for the 3 first years of the vineyard (including planting), for vineyard destruction and the intercropping period, if relevant.
Data for forages and grassland came from typical cases created by ARVALIS (alfalfa, grassland) and from typical cases created by IDELE for sheep and dairy cow systems (grassland). Data for dairy cows came from typical cases created by IDELE and from one real farm. Data for sheep came from a typical case created by IDELE in the Agneaux Bio project. Data for pigs came from real farms. Finally, data for poultry came from technical data compiled by ITAVI. LCIs were calculated using the MEANS-InOut web application, which is a customised agricultural LCA tool that generates LCIs of agricultural production systems  [15]. It contains forms to guide data entry and includes a reference dataset for the main inputs of agri-food systems, analytical models to estimate direct pollutant emissions and resource use, and an export function that generates LCI files ready to be imported in LCA software to calculate impact indicators.
Farmer-practice data for each type of production are available on demand in the MEANS-InOut application (contact Julie Auberger, julie. auberger@inrae. fr). Databases used for background processes were AGRIBALYSE 3. 0. 1 and ecoinvent 3. 5. LCIs of animal feeds were created by averaging the crop LCIs and considering processing of crop products when necessary (e. g. to produce soya bean meal). Crop residue dry matter quantities and nitrogen contents were calculated based on the CITEPA method  [16]  , which supplies values that are more accurate for the French situation than the data based on the IPCC 2006 guidelines. For soya bean residue N content the value proposed by CITEPA (2. 69%) was very high compared to that of other crop residues. Comparison with measured data confirmed that this value was unrealistic, so instead we used the value proposed by IPCC (0.
CH 4 emissions from enteric fermentation were modelled using INRA Feeding System for Ruminants 2018 Tier 3  [14]  for ruminants and IPCC 2006 Tier 1 [32] for pigs. CO 2 emissions from fuel combustion or active substances of pesticides were NO 3 emissions for grassland and alfalfa were modelled using the DEAC model  [29]. DEAC considers nitrogen from fertilization and from animal excreta, as well as soil and climate data. For grassland, predicted NO 3 emissions were higher than those of grassland in the AGRIBALYSE database. Thus, we adjusted these estimates as a function of the ratio of the total amount of nitrogen applied to the NO 3 emissions from AGRIBALYSE for grassland grazed by conventional cattle (Table  S3 ).
Impact assessment 2. 4. 1. LCA impact categories Impact assessment was performed using SimaPro v9 software. We calculated values for 9 of the 16 impacts of the EF method v2. 0  [12]  (see Table  S4  for the detailed list and units of all impact indicators). We only calculated impacts with levels of recommendation I (recommended and satisfactory): Climate change, Ozone depletion, Respiratory inorganics, and II (recommended but in need of some improvements): Ionising radiation (human health), Photochemical ozone formation (human health), Acidification terrestrial and freshwater, Eutrophication terrestrial, Eutrophication freshwater, Eutrophication marine, according to Fazio et al. 2018  [12]. The climate change impact category was adjusted by setting the characterisation factor "CO 2 in air" to 0, because we assumed that CO 2 absorbed by plants does not count as carbon sequestration, since it returns to the air within a short period.
The "CO 2 from land transformation" characterisation factor was also set to 0 since we excluded CO 2 emitted from land transformation. Other impact categories were used to supplement the dataset. Cumulative Energy Demand v1. 11 was used to estimate energy use in MJ throughout the life cycle  [13]. Land competition from CML-IA non-baseline v3. 04 was used to calculate land occupation in m ²year  [14]. Finally, we used three biodiversity indicators: Knudsen et al. [15]  for temperate annual crops and grassland, Mueller, de Baan and Koellner  [16]  for tropical annual crops (for soya beans in animal feed), and Koellner and Scholz  [17]  for grapes. These three indicators are based on species richness of vascular plants and provide characterization factors that differentiate the biodiversity impact of conventional and organic farming ( Table  4  ).
Acknowledgments The authors thank the following institutions and persons: ADEME and the  Ministry for Ecological Transition (MTES) , both of which funded the ACV  Bio  project. The members of  ACV Bio's Steering committee: ADEME, Agence Bio (Agency  for the development and promotion of organic agriculture),  CTIFL  ( Interprofessional Technical Center for Fruits and Vegetables ), FNAB (National federation of organic agriculture),  INRA  ( National institute  for agronomic research),  ITAB (Institute for organic agriculture and food) Ministry of agriculture and food, MTES, Synabio (National union of organic food companies), Synalaf (National union of poultry labels ). Antoine Roinsard  and  Natacha Sautereau of ITAB  for their contributions in the ACV  Bio  project to methodology, data collection, system description and data analysis.
-practice data are available on demand. © 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons. org/licenses/by/4. 0/ ) Specifications Table Subject Environmental Science Specific subject area Life cycle assessment (LCA) data of French organic agricultural products Type of data Table How data were acquired ecological infrastructure and the pesticide treatment frequency index Data source location Institutions: ARVALIS, ESA-Angers, IDELE, IFIP, INRAE, ITAB, ITAVI, TERRES INOVIA City/Town/Region: regional scale (i. e. French administrative region) Country: France Data accessibility Repository name: Data INRAE Direct URL to data: https://doi. org/10. 15454/TTR25S Instructions for accessing these data: Click on "Access Dataset" then "Download ZIP".
Description of data collectionThe dataset contains LCA data of 173 organic agricultural products. LCA results were expressed per kg of product and per ha of land occupied on-farm and off-farm using the Environmental Footprint 2. 0 characterization method. Three other LCA indicators were calculated: cumulative energy demand, land competition (CML-IA non-baseline) and biodiversity loss. For certain systems, three other non-LCA indicators were calculated: diversity of crop families, agro-.
We rolled each piece of nori around small pebbles, placed each one into an ice cube tray segment, and added some warm agar-water. After the agar cooled and hardened, the top of the nori was loose, and the bottom of the nori was condensed around the pebbles and held in place by on days 1, 3, 5, and 7. A predation cue was introduced on day 7. Data was recorded (bites per minute and overall food consumption) on days 3, 5, and 7. There were two types of controls: (1) the tanks to which we did not apply any predation risk cues and (  2 ) the difference in bite rate and food consumption between two consecutive days of the experiment where there was no predation cue ("baseline") compared to the change in bite rate when we introduced the predation risk cue ("predation"). the agar. In this way, the initial e orts of the fish to graze were more productive than any continuing e ort, which resembles their natural feeding environment.
The food trays were comprised of ten equal pieces of seaweed, one in each ice cube segments. At each interval, we further quantified the portion of seaweed remaining in each ice cube segment on a scale of 0-3. This allowed us to estimate the overall proportion of seaweed remaining in each tank at each measurement time on a scale of 0-30 (10 ice cubes per tank ⇥ 3 portions per ice cube). We assigned a value of 0/1 to each algae portion at each observation time (30 per tank, where 0 indicated a portion that was eaten, and 1 indicated a portion of food that remained). As we did not retain data on the fate of each individual ice cube segment, this was done by dividing the total number of algae portions remaining on each tray by the total original number of algae portions under the assumption that grazing was even across ice cube segments.
ACKNOWLEDGMENTS We would like to thank  Dar Golomb ,  Erez Yeruham , and  Shahar Malamud  for their help in setting up the mesocosm and catching the fish. We would also like to thank all the members of the  Belmaker Lab  for their generous help, and particularly to  Sarah Ohayon ,  Tal Gavriel , and  Nitzan Yitzchak  for help feeding the fish;  Renanel Pickholtz  for help with the experimental design, and  Itai Granot  for help with the analysis.
The proposed AOP-helpFinder webserver uses better performing version of the tool which reduces the need for manual curation of the obtained results. As an example, the server was successfully applied to explore relationships of a set of endocrine disruptors with metabolic-related events. The AOP-helpFinder webserver assists in a rapid evaluation of existing knowledge stored in the PubMed database, a global resource of scientific information, to build AOPs and Adverse Outcome Networks supporting the chemical risk assessment.
, 2016). With advances in technologies, huge amounts of data have become available, compiled in well-structured toxicological databases (e. g. CTD, CompTox), in AOP-oriented webservers (AOP-wiki, sAOP, AOP4EUpest) and scientific publications  (Williams et al. , 2017). Innovative data mining tools are needed to identify sparse but complementary data such as Abstract Sifter allowing to have a view of the toxicological information landscape for a set of entities as chemicals  (Baker et al. , 2017)  or ComptoxAI (https://comptox. ai/ index. html). Artificial intelligence (AI) technology, that uses natural language processing (NLP), is an interesting way to facilitate the identification of links between relevant information that can be used to build novel AOPs  (Song et al. , 2020) , and identify knowledge gaps and research needs  (Zgheib et al.
, 2021). Several tools use text mining (TM), an AI method to transform unstructured into structured text. For example, Limtox provides a biomedical search for adverse hepatobiliary reactions  (Cañada et al. , 2017). Recently, the AOP-helpFinder tool, based on TM and graph theory was proposed to identify stressor-KE relationships by examining large collections of scientific abstracts, and was applied to bisphenol A substituents and pesticides  (Carvaillo et al. , 2019; Jornod et al. , 2020; Rugard et al. , 2020). Here, we present the AOP-helpFinder webserver, which uses an updated version of the tool, to provide an easy but effective resource for identifying and compiling existing knowledge from the scientific literature. The main optimized features are (i) the capability to choose to search in full abstracts or without considering the introductory parts, (ii) the possibility to perform a refined search using machine learning and (iii) an automatic update of the PubMed database before each search.
The AOP-helpFinder tool To increase the performance of the previously developed version, several methods were tested using a set on ED and biological events related to metabolism, and two were kept through the process (see Supplementary Material, https://github. com/jornod/aop-helpFinder):(i) 'reduced search': searches are performed in the full abstracts or without considering the introductory part, which appears to be covered usually by the first 20% of the abstracts. This option allows avoiding too many false positives, as the introduction often reflects a working hypothesis instead of the conclusions of the publication and (ii) 'refinement filter': after the preprocessing that uses a stemming process  (Carvaillo et al. , 2019) , the tool can refine the searches by combining a deletion of sentences containing context words with a lemmatization process.
Lemmatization is a machine learning method for text normalization used in NLP that considers the context and converts the word to its meaningful base form. This option is very useful when terms have common stems (e. g. tests, testis h test) leading to incorrect meanings and spelling errors. Further, an automatic daily update of the PubMed database was newly implemented using the NCBI API to screen the full existing knowledge. The current version of the AI tool mined the PubMed database, that is a global source for scientific literature. Nevertheless, the developed method screens text-based knowledge, and therefore the AOP-helpFinder server could be improved for mining multiple sources (databases, literature), including studies reporting negative findings, to accelerate information gathering when data are limited and present in diverse sources  (Carvaillo et al.
Case study on endocrine disruptors and metabolism The AOP-helpFinder webserver was used for a case study aiming at automatically identifying existing relationships and knowledge gaps between 10 ED (Supplementary Table  S1 ) and 294 biological events related to metabolism (Supplementary Table  S2 ). The webserver was launched using 'reduced search' (omitting searches in the first 20% of the abstracts) and 'refinement filter'. Among the 83 970 abstracts retrieved in the PubMed database as of May 10, 2021 related to at least one ED (Supplementary Table  S1 ), a total of 4622 were retained (comentioning ED and event). Among the 294 events, 108 were identified as comentioned with at least one ED (Supplementary Table  S2 ). Figure  1  illustrates the large disparity of knowledge for the 10 selected ED in the area of metabolism (see Supplementary Fig.
The optical extensometer involves a high-resolution charge-coupled device (CCD) camera (Redlake Megaplus II (Tuscon, AZ, USA), 1920 × 1080 contiguous and square pixels, coded in 256 grey levels), set in front of the specimen, which records images during the test. The optical axis of the camera remains perpendicular to the in-plane surface of the specimen during the test (Figure  2 ). The images acquisition is commanded by the LabVIEW ® software (National Instruments, Austin, TX, USA) which allows the simultaneous acquisition of the images and the data from the testing machine (such as load and crosshead displacement). According to the test speed used, images are recorded every 0. 5 s. The scale factor is fixed to 42 µm per pixel. The in-plane Green Lagrange strains E x , E y were deduced from digital image correlation (DIC) following a method well described in previous works  [31].