dataesr/scientific-paragraphs-categorization huggingface dataset:
The orthophotos are displayed to help in mapping features. For more information, readers can access the following link: https://www. geoportail. gouv. fr/. -The database, which refers to the initial database that is modified during the campaign by the contributors. The data available for the French pilot are vector objects with respect to the test area. Two types of data are available: buildings and LULC. The API allows for accessibility to and modification of the data by using the Web Feature Service Transactional (WFS-T) protocol, and the API can be reached by other Geographic Information Systems (GIS) clients. - The third server manages the contributions from the users as well as the photographs taken by contributors in the field. This is described in more detail in Section 4. 2. The second tool developed is the PAYSAGES mobile application.
PAYSAGES Web Application The web application is an adaptation of the espace-collaboratif © (https://espacecollaboratif. ign. fr/) system proposed by IGN to engage with different local governments for data production. Figure  6  illustrates the functionalities of the PAYSAGES web application. In espace-collaboratif, a contributor can only edit some attributes in the database (i. e. , not the geometry directly) and create new reports (i. e. , photographs, documents, or schema may be attached). The contributions are managed by the data administrator, which is a staff member from IGN. The procedure to check the integration is launched before data publishing. In contrast, in the new PAYSAGES web application (see a screenshot in Figure  7 ), contributors can directly modify the data (i. e. , both the attributes and the geometry), and the updated data are directly available on the web through a Web Feature Service (WFS).
The user selects the principal and secondary uses of the building, selects the number of floors, and optionally takes a picture of the building. This information is then saved to the phone and uploaded later when they have a wifi or data connection. Figure  10c  shows the screen associated with a different task. The user has chosen a location where change may have occurred and is asked to verify if the LU is 'Agriculture'. If not, they are asked to choose the new LU followed by the new LC and to take up to four photographs (the latter two parts of the task are not shown in the figure). The app runs on both Android and iOS operating systems and is available for download from the Google PlayStore and the Apple AppStore, respectively. The app is available in both French and English; the language that appears is based on the phone settings, although translations were done manually to ensure the correct meanings of the terms used, particularly those related to LULC, which are often poorly translated when using automatic translation facilities.
PAYSAGES Wiki The wiki (https://paysages. ign. fr/fr. wiki) (implemented using the Mediawiki software, Version 1. 30. 0, https://www. mediawiki. org) is the social component of the PAYSAGES platform. Most people are familiar with Wikipedia; an advantage of this tool is that it provides a way for everyone to collaborate easily by writing unstructured information. To initiate the PAYSAGES wiki site, a presentation about the project, user guides, an Frequently Asked Questions FAQ, and important dates for the campaigns have already been published. One of the major challenges with LULC information is to understand the metadata. To this end, the wiki represents a good way to help contributors in understanding this information. First, as a result of infobox, important information about a particular LU or LC type is directly accessible from the wiki (Figure  11a ).
Anyone can add an image or advice, or initiate a discussion in a specific metadata page. Almost all elements of a metadata page are saved as semantic properties. Then, via the Semantic Mediawiki extension, the metadata page can be exported or published, allowing other systems, such as the mobile application, to access these pages. This last functionality is still under development. With Semantic Mediawiki, other extensions can be used to query external data, make searching and navigation easy, or display the results in tables or graphics. For example, a graphic could be published that summarizes the progress of visits or shows the points to visit on an interactive map, or the top 10 contributors could be displayed in a table (Figure  11b-d ). As of the PAYSAGES architecture, a database with field photographs and metadata will be created; however, this is still under development.
Various landscapes show different degrees of urbanization and contain features that correspond to the different tasks outlined in Section 3. 3. The extent of the area is approximately 10,000 km 2 (Figure  12 ). With Semantic Mediawiki, other extensions can be used to query external data, make searching and navigation easy, or display the results in tables or graphics. For example, a graphic could be published that summarizes the progress of visits or shows the points to visit on an interactive map, or the top 10 contributors could be displayed in a table (Figure  11b-d ). As part of the PAYSAGES architecture, a database with field photographs and metadata will be created; however, this is still under development. The wiki will be able to use this information and display it by querying and filtering with semantic metadata.
Launching the Campaigns The first campaigns running in 2018 have several parts, which we refer to here as 'thematic' campaigns. There are three opportunistic campaigns and five guided ones. The thematic campaigns were launched and communicated to potential contributors through different social media channels and face-to-face meetings with different stakeholders. The opportunistic campaigns are focused on the 2013 and 2016 LULC data as well as the ongoing update to the building database. The goals of these campaigns are: -To update the building data; To update the LULC data (LULC 2016) and correct any errors in the LULC data for 2013; and -To distinguish between residential, industrial, and service uses, which are three LU classes that are currently merged into a single class due to lack of information.
The guided campaigns are focused on the 2016 LULC data and are divided into: -Collecting new information on some pre-selected buildings; -Identifying the current state of quarries; -Distinguishing between residential, touristic, and agricultural LU; -Updating the LU and LC classes at locations that are 'under construction' in the initial database; -Validating points automatically detected by the change detection service; and -Making reports at any location concerning building and LULC data collected in the field.
Conclusions This paper has outlined a strategy and workflow for updating and correcting LULC information on a three-year cycle and building data on a continuous update cycle for the national mapping agency, IGN France. The technology, which was developed as part of the LandSense project, is comprised of web, mobile, and wiki applications, targeted at different types of users to be employed in different types of campaigns. The web application will mostly be used by experts who work for IGN or local government partners who are also users of the data. The mobile application is intended to reach a wider audience including citizens and students, so the tasks are well-defined and the locations of where information is needed are pre-determined. Finally, the wiki application is intended for use by both citizens and experts.
As general feedback received to date, the contributors have noted that the application is user-friendly and very easy to use. From a technical perspective, for the second campaign that will take place in 2019, we intend to implement the link between the PAYSAGES system and the LandSense platform, as well as to develop the functionality that allows the mobile application to access metadata from the wiki application and to take into account user feedback. From a data quality perspective, the collected data will be analyzed in order to assess their qualities such as positional accuracy, thematic accuracy, topological consistency, etc. , which will eventually use the LandSense quality assurance service when it becomes available. Moreover, both the Sentinel-based change detection service and collaborative mapping can generate errors.
These errors can accumulate from the initial data collection and propagate to the final product. Thus, it is essential to identify these errors, to model them and their propagation, and to quantify them. The errors can be positional as well as thematic (e. g. , misclassification of LULC classes). Different strategies can be applied. On the one hand, quality indicators such as topological and thematic consistency computed on the same dataset (before and after changes) can be used to highlight positional and thematic errors. For example, overlapping between polygons, sliver polygons, or polygons with holes can be detected. Thematic inconsistency between LULC classes (e. g. , a feature having the land cover class shrubs, and the land use class equal to residential) can be detected. On the other hand, different snapshots of LULC data (e.
Indeed, some changes are valuable at the features scale but they are not meaningful from an LULC class point of view. For example, a contribution reporting two new buildings in an urban area is valuable, but it is not meaningful for LULC because the new features do not change the class in the LULC dataset. Thus, our goal is to develop new methods to determine if the change is meaningful for the authoritative databases under consideration (e. g. , the change has already been inserted in the database, the change does not concern features in the authoritative data due to the data specifications, etc. ). These types of situations will be identified and allow us to make decisions regarding whether an update needs to be done in the authoritative database.
The addition of new building information: Primary use, secondary use, number of floors. Note that primary and secondary uses are drawn from the same list of values. -LULC validation or quality validation: This task is linked to the change detection and quality assurance services provided by the LandSense project. LULC polygons are detected as having changed in the previous year and this change has to be validated as a change (and hence values must be changed in the database) or determined as having not changed (and hence no changes are made to the initial database). -Status of quarries: This task focuses on the quarry LU polygons that are in the initial database, where contributors are asked to indicate the status of the quarry as active, closed, or abandoned. -Updating construction LU classes: This is a transitory class.
- The addition of new building information: Primary use, secondary use, number of floors. Note that primary and secondary uses are drawn from the same list of values. -LULC validation or quality validation: This task is linked to the change detection and quality assurance services provided by the LandSense project. LULC polygons are detected as having changed in the previous year and this change has to be validated as a change (and hence values must be changed in the database) or determined as having not changed (and hence no changes are made to the initial database). -Status of quarries: This task focuses on the quarry LU polygons that are in the initial database, where contributors are asked to indicate the status of the quarry as active, closed, or abandoned. -Updating construction LU classes: This is a transitory class.
Data analysis Results of the questionnaires were analysed statistically using R, SPSS, and Latent Gold software. For each feature evaluated, respondent's scores of all scenes were averaged and used to calculate statistical characteristics for a given group of respondents. In case that average scores did not follow a normal distribution, statistical differences in the distributions were tested by the non-parametric Wilcoxon-Mann-Whitney signed rank and the Kruskal-Wallis tests. Correlation between scores given to the analysed aspects (aesthetics, naturalness, danger and improvement) was tested with the Spearman correlation test. Research questions regarding possible clusters were tested with a latent class cluster analysis (LCCA;  Vermunt and Magidson, 2002; Vermunt, 2003; Díez-Herrero et al.
Acknowledgments Authors thank all the interview participants, and collaborators who distributed the online survey among colleagues and students. Special thanks go to  Elisabet Peñuela ,  Gonzalo Peño ,  Ignacio Ibáñez ,  Diego López , and  Sergio Santolaria  ( University of Zaragoza, Spain ),  Dany Caetano  ( Universidad Federal de Santa Catarina, Brasil ), and  M. Angeles Perucha  ( Geological Survey of Spain ) for their collaboration in the field surveys. This work has been partially supported by the projects  MAS Dendro-Avenidas  ( CGL2010-19274 ) and  MARCoNI  ( CGL2013-42728-R ) and  Dendrolab. ch ( University of Bern, Switzerland ) and the  WoodFlow  project funded by the  Swiss Federal Office for the Environment (FOEN). Comments from three anonymous reviewers significantly improved the manuscript.
(CAZymes) and binding modules (CBMs), tailored to specifically hydrolyse and recognize the unique sugar-units of these glycans  2. CAZymes and the associated carbohydrate binding modules (CBMs) are classified into sequence-based families in the CAZy database (www. cazy. org/)  3. It is noteworthy that marine enzymes and modules often represent novel families or are present as additional sub-families within those of the glycoside hydrolase CAZy classification  2. Indeed, the current status quo is that genomic and metagenomic data from marine environments is most often annotated with biochemical knowledge derived almost entirely from bacteria and fungi that decompose terrestrial plants. However, marine primary producers and land plants share only a limited number of polysaccharides, for example starch, cellulose and certain mixed linkage glucans  [4] [5] [6].
The repeatability of the DB-CoMPP technique was tested by comparing data derived from three independent protein productions. To increase the robustness in signal assignment, the extracts are usually printed with repetitive spot frequencies on CoMPP arrays  21, 22  and the same strategy was applied for the DB-CoMPP technique (16-spot sub-array per sample). If a DB-CoMPP regime similar to that described in Fig. 1  is used, up to 96 proteins can be produced and screened on a 100 extracts-based array, and analysed in two weeks.
galactanivorans  19. The mean values for the binding of the bacterial lysates to the marine glycoarrays are shown as heatmaps, where spot signals are correlated to colour intensity. Some of the negative controls produced weak signals which was defined as unspecific binding (Supplementary Fig. S1 ). These values were subtracted from the initial dataset (Supplementary Fig. S2 ) to give the final heatmap shown in Fig. 3  and from which positive protein-glycan interactions and signal intensities, as an indication of relative avidities, can be inferred. The highest mean signal value in the entire data set was set to 100 and all other signals adjusted accordingly The bacterial lysates gave positive signals with intensities evenly distributed within the 0-100 interval, and this is visually comparable to results obtained with standard CoMPP  21, 23.
Acknowledgements A. S. S. was supported by the  Danish Strategic Research Council  under the  Sustainable Enzyme Technologies for Future Bioenergy (SET4Future) program. Visits of A. S. S. to  Station Biologique de Roscoff (France)  were supported by short term grants provided by the  European Commission  through under the  FP7 'capacities' specific program  ASSEMBLE. C. H. acknowledges funding from the  French National Research Agency  with regard to the investment expenditure program Idealg (http://www. idealg. ueb. eu/,grant agreement no. ANR-10-BTBR-04 ). We sincerely thank all the staff from the  Marine Service at the Station Biologique de Roscoff  for the sampling of local marine species.
Ammonia (NH 3 ) emission from animal manure contributes to air pollution and ecosystem degradation, and the loss of reactive nitrogen (N) from agricultural systems. Estimates of NH 3 emission are necessary for national inventories and nutrient management, and NH 3 emission from field-applied manure has been measured in many studies over the past few decades. In this work, we facilitate the use of these data by collecting and organizing them in the ALFAM2 database. In this paper we describe the development of the database and summarise its contents, quantify effects of application methods and other variables on emission using a data subset, and discuss challenges for data analysis and model development. The database contains measurements of emission, manure and soil properties, weather, application technique, and other variables for 1895 plots from 22 research institutes in 12 countries.
Data on five manure types (cattle, pig, mink, poultry, mixed, as well as sludge and "other") applied to three types of crops (grass, small grains, maize, as well as stubble and bare soil) are included. Application methods represented in the database include broadcast, trailing hose, trailing shoe (narrow band application), and open slot injection. Cattle manure application to grassland was the most common combination, and analysis of this subset (with dry matter (DM) limited to < 15%) was carried out using mixed-and fixedeffects models in order to quantify effects of management and environment on ammonia emission, and to highlight challenges for use of the database. Measured emission in this subset ranged from < 1% to 130% of.
g. ,  Søgaard et al. , 2002; Rotz et al. , 2014; Langevin et al. , 2015). Standardisation of experimental results and collection in a database can increase the utility and accessibility of experimental data, which can then be used for multiple purposes, including development or evaluation of both emission factors and models. The ALFAM project on NH 3 emission from field-applied manure demonstrated this: the resulting database and the related ALFAM model  (Søgaard et al. , 2002)  have been widely used (215 citations as of 17 August 2017 according to Google Scholar). But many emission measurement experiments have been carried out since the publication of the ALFAM database, and measurement techniques have been refined  (Sintermann et al. , 2012). Preservation of these emission measurements, which have been used to determine emission factors, along with supporting data in an accessible (numeric) form, is essential.
Given the current interest in NH 3 emission from both regulatory and research perspectives, expansion and improvement of the ALFAM database is needed. The primary objective of this article is to present the ALFAM2 database, which consists of data from the original ALFAM database and new data. The ALFAM2 database contains about twice as many observations as ALFAM, and provides more information by adding new variables related to emission. In this article, we: 1) describe and summarise the new ALFAM2 database; 2) use statistical models to attempt to quantify effects of application methods and other factors on emission, 3) discuss challenges for data analysis and model development based on the database, and 4) recommend ways to standardize and improve the quality of ammonia emission measurements.
Data collection The database described in this work is a combination of the original ALFAM database  (Søgaard et al. , 2002)  and newer data. New data were entered into a spreadsheet template by each research group that collected them, or transferred to the template from other files provided by the group. Research groups in Europe, Canada, and the US known to us to have made NH 3 emission measurements were invited to submit data. (New contributions are also welcome, and readers are invited to contact the corresponding author for information on data submission. ) The following types of variables were included: identification (project, publication, experiment, field, plot code, treatment), location (latitude, longitude, topography, field name), soil (texture, moisture, pH, temperature, tillage), weather (air temperature, wind speed, precipitation, relative humidity, solar radiation), manure (source, bedding, dry matter, total ammoniacal nitrogen (TAN), pH, treatments), application (time, method, rate, incorporation), crop (type, height, coverage), and emission (interval time, measurement method, plot size, background NH 3 (g) concentration upwind of the emitting surface, average emission rate).
Two types of data were collected: plot-level (concerning a single physical plot from which emission was measured) and measurement interval-level (concerning a single time interval with one estimate of NH 3 emission rate, for a single physical plot). Plot-level data included plot identification, location, soil, manure, application, and crop data. Measurement interval-level data also included weather and emission for each interval, which varied in length among experiments and within individual plots. Weather data included average air temperature, wind speed, and precipitation for each interval. Emission data were reported as average NH 3 flux (typical unit, kg ha -1 h -1 ) for each measurement interval. Application methods and emission measurement methods  (McGinn and Janzen, 1998)  were selected from a list of possibilities or defined by the submitter.
Data processing, calculations, and database organization All data processing and analysis was done using R version 3. 3. 1 (R Core Team 2017). The general steps were: 1 Data from the original ALFAM study were read from a spreadsheet file, and column names and factor levels were added. 2 Minor changes were made to ALFAM data (described directly below). 3 Data from each individual ALFAM2 research institute were read from one or more spreadsheet files, standardised (measurement units, spelling of categorical variable levels, date and time format), and merged with the original ALFAM data. 4 Cumulative emission and other new variables were calculated. 5 Plot-level data were taken from the first observation for each plot (e. g. , slurry type) or else calculated (e. g. , weather averages and cumulative emission).
6 Data were saved in two files: plot-level and interval-level. Only minor changes were made to the original ALFAM data. A note was added to all observations from a single institute explaining that measurements are now thought to be biased (see Section 3. 3). For three institutes the broad measurement method category "micrometeorological" was replaced with more specific levels based on knowledge of the authors (integrated horizontal flux (IHF) for institutes 105 and 106, and ZINST for institute 107). Date format was corrected for several plots, and corrections were made to about 30 interval start times for institutes 104, 105, and 107. Finally, the wind speed measurement height for institute 105 was corrected from 0. 25 cm to 25 cm. Two files were produced in order to meet expected needs of users.
To find values of cumulative emission after a specific time (e. g. , 48 h) or the entire measurement event duration, the smaller plot-level file (File S1) will be more convenient. Each observation contains data on a single plot. In a small number of cases, emission was measured from a single plot simultaneously by more than one method, and therefore multiple observations exist for some plots in the plot-level file. Each observation contains a unique plot/measurement method code ("pmid" variable in the files, for plot-measurement method identification code), as well as a plot id ("pid" variable in the files). For data on emission rates over time, the interval-level file (File S2) is required. In this file, multiple observations are included for each plot. Two related text files describe the variables and headers  (Files S3 and S4).