dataesr/scientific-paragraphs-categorization huggingface dataset:
This is particularly relevant when solid compost is applied directly to farmland rather than using of HA/FA formulations that are commercially available or prepared by those with technical expertise. After determining the carbon content and the amount of acid groups, the comparison between the extracts was conducted by measuring the extent of the Cd 2+ binding, referred to as c ML. This value, expressed in mmol kg compost -1 was calculated by referencing the Cd 2+ binding by each extract to 1 kg of compost. The calculation was based in complexation data at pH 7. 0 and took into account the abundance of the extracts using Equations (  1 ) and (2) for the humic substances and water-soluble extracts, respectively: c ML mmol kg compost -1 = c ML mol L -1 c HS (kg HS L -1 ) × Y g HS kg compost -1 (1) c ML mmol kg compost -1 = c ML mol L -1 F d × 50 g compost L -1 × 10 6 (2) where c ML (mol L -1 ) and c HS (kg HS L -1 ) are the concentrations of metal complex and of organic matter (HA and FA), respectively, obtained from the electrochemical assay; Y is the yield of the HA/FA extractions (in g HS kg compost -1 ), F d is the dilution factor (F d = V DOM /V Total ) and 50 corresponds to the amount of compost per litre used to prepare the equilibrium solution (g compost L -1 ).
Acknowledgments: Thanks are due to Fundação para a Ciência e  Tecnologia (FCT)  and  FEDER (European Fund for Regional Development)-COMPETE-QRENEU  for financial support through the  research units Chemistry Research Centre of  ( UID/QUI/00686/2020 ) and  Centre of Molecular and Environmental Biology (CBMA)  ( UIDB/04050/2020 ) of  University of Minho. We would also like to acknowledge  R. López ,  M. Santiso , and  F. Casas , for their help with the characterization of the compost samples and extraction of humic substances. J. Antelo  and  S. Fiol  belong to the  Crossdisciplinary Research in Environmental Technologies (CRETUS Research Center ,  ED431E 2018/01 ), co-funded by  FEDER (EU).
2017). Crop diversification initiatives are highly variable because they are strongly context dependent and customized by specific combinations of actors, agronomic innovations, local markets, and value chain organization. We must better understand the influence of different actors, socioeconomic interactions, and cognitive processes if we wish to facilitate the transition toward crop diversification and, as a consequence, more sustainable agrifood systems  (Meynard et al. 2018; Weituschat et al. 2022). During the H2020 project DiverIMPACTS (http://www. diverimpacts. net/), an online survey was administered to assess crop diversification experiences (CDEs) across Europe. It collected data on the innovative systems being used, which helped to identify the main contributors and hurdles in CDEs  (Drexler et al.
A German farmer tried out sugar beet for biogas substrate. They were willing to take the risk when they had diverse agricultural activities or when they received financial support from other actors. Of the 23 CDEs, 12 were completely self-funded, and 5 were funded by combining the farmers' own resources with research money from local authorities (national level) and/or European institutions. Some farmers also considered the financial implications of crop diversification. The potential for better returns was an incentive for change. A desire to become more selfsufficient was another motivation. For example, a Belgian farmer looked for more lucrative crops than wheat and tried to grow peas and lupins that he gave as feed to his cows. He is closing the cycles on his farm and noted that "in other words, we are becoming more self-sufficient, this means that we will buy less from suppliers" (Farmer, Atlantic).
Acknowledgements We want to thank all actors interviewed and the researchers that carried out the qualitative interviews. We want to address a special thank to  Louise Legein  who was involved during the early stages of the study. Our warmest thanks go to  Antoine Messéan  for his unfailing commitment to coordinating the DiverIMPACTS project. We would like to thank the two anonymous reviewers and the editors whose questions, comments and suggestions helped improve and clarify this manuscript.
Funding This research was carried out in the framework of the project  DiverIMPACTS -"Diversification through Rotation, Intercropping, Multiple Cropping, Promoted with Actors and value chains towards Sustainability. " This project is supported by the  European Union 's  HORIZON 2020 research and innovation programme  under Grant Agreement no  727482  and by the  Swiss State Secretariat for Education, Research, and Innovation (SERI)  under contract number  17. 00092.
Data Availability As the qualitative interviews could contain sensitive information, the data of this research is not publicly available. Code Availability Three original R-packages have been used and/or developed in the framework of this study. All of these are publicly available on official repository and/or main DevOps platforms: • Duckles B, Sholler D, Draper J, Laderas T (2022). qcoder Lightweight Qualitative Coding. R package version 0. 1. 0, https:// github. com/ropenscilabs/qcoder. • Vanwindekens, F. (2019). cogmapr Cognitive Mapping Tools Based on Coding of Textual Sources. R-packages, Web Application and User Manual. Hosted on cran (https://CRAN. R-project. org/ package=cogmapr) and gitlab for the development version, https:// frdvnw. gitlab. io/cogmapr/. •  Vanwindekens, F. (2018).
Acid-induced gel stability The gel colloidal instability due to the spontaneously occurring syneresis was assessed in four independent fermented gels using a LUMiSizer (LUM GmbH, Berlin, Germany). The protein solutions were fermented for 4 h at 37 ± 1 • C in 10 mm polycarbonate LUM cells. The resistance of the final hydrogels to forced serum exudation was assessed by monitoring the light transmittance (λ = 865 nm) over the tube height at 2300g for 10 min with a 5 s interval between measurements at 25 • C. Data analysis was performed using the SEPView software (LUM GmbH, Berlin, Germany).
X-ray tomography. Micro-computed X-ray tomography (μCT) measurements were performed to visualise and quantify the 3D microstructure of the desiccated cryogels in duplicate. To this end, a laboratory X-ray cone-beam μCT system EasyTom 160 from RX Solutions (Chavanod, France) was used at 60 kV and 120 μA. 1440 projections over 360 • (step of 0. 25 • ) were recorded at 0. 5 frames s -1 by a 16-bit flat panel imager with a total pixel area of 1920 pixels × 1536 pixels. The source-to-object distance (SOD) and the source-to-detector distance (SDD) were set to approx. 2. 2 mm and 320 mm, respectively, enabling us to obtain a voxel size of approx. 0. 9 μm. Xact software (RX Solutions, Chavanod, France) was used with the projections acquired to reconstruct the volume into stacked slices after applying a sample movement correction (spot deviation module), a system misalignment correction (geometry correction module) and a ring filter (5 pixels).
The slices were subsequently analysed by means of the 3D image analysis software Avizo (Konrad-Zuse-Zentrum Berlin/FEI SAS-Thermo Fisher Scientific, Waltham, MA, United States). The following procedure was applied: median filter (3D mode, 6 neighbourhoods) to facilitate the segmentation, grey-level segmentation to isolate the pores and the matrix, quantification of the pore volume fraction and, thickness map analysis of the matrix (vessel thickness distribution).
Disintegration profile of the cryogels The disintegration profile of the cryogels was carried out using static light scattering (Mastersizer 3000, Malvern Instruments, Worcestershire, United Kingdom). The experiment was run by immersing a cryogel in the LV measuring cell filled with Milli-Q water under constant stirring at 3500 rpm for 15 min at 25 ± 1 • C. The particle size distribution was recorded with interval of 10 s during 3 s with blue and red lasers. The refractive index was set at 1. 45. The data was then fitted with a first order exponential model (Eq. (  4 )) as follows: y = D [4,3] ∞ + ( D [4,3] 0 -D [4,3] ∞ ) e -ln 2 t τ 1/2 (4) where D [4,3] 0 and D [4,3] ∞ denote the volume-weighted mean particle size (μm) at t = 0 and ∞ min, t the time (s) and τ 1/2 the time required to reach a 50% D [4,3] 0 decrease  (Quodbach & Kleinebudde, 2014).
Acknowledgements This work was supported by the  Fonds National de la Recherche Luxembourg  (Project  PROCEED :  CORE/2018/SR/12675439 ). The authors would like to acknowledge  Manon Hiolle (Ingredia, Arras, France)  for providing the  WPI. Carole Jeandel  and  Carole Perroud  are acknowledged for their technical support with DSC and DVS analyses.
Data Collection The species data were obtained by requesting lists of alien species for each of the 13 SANBI gardens from the curators and garden estate managers. Several key sources were used for plant species identification, including field guides by Bromilow  [34, 35]  and Henderson  [36] , the Invasive Species South African Database  [37]  and herbarium specimen collections. Due to limited evidence, some species were identified only up to the genus level. Species names were verified using the national resource: "The Status of Biological Invasions and their Management in South Africa"  [24, 26]. Following the criterion described in Mokotjomela et al. [33] , plant species were classified by life form as follows: herbs, graminoids, succulents, woody for the shrubs and trees and ferns using the Botanical Database of Southern Africa (BODATSA)  [38]  and the Integrated Taxonomic Information System (Table  A1 , [39]).
Pyšek et al. [40]  showed that some species' traits, especially life form, stature and pollination syndrome, may provide a method of predicting impact, regardless of the habitat and geographical region invaded. In addition, the region of origin is critical for understanding and managing the pathway of introduction for alien species (see also  [41] ). The data collected for each species included the gardens and their provincial locations where the species was recorded in South Africa, the reason for introduction as a proxy for the pathway pattern, and the continental origin of each species. Since the native range of the species is important for climate matching with the recipient environment during invasion risk assessment  [42] , we made an assumption that the species were introduced directly to South Africa from their native continent.
Inasmuch as the exact country of origin of each species was identified (after  [12, 33] ), for this study we used the continental and/or broad region to classify the recorded species as specified in the Centre for Agriculture and Bioscience International (CABI) and Global Invasive Species Database. Species were also categorised following the NEM:BA-A&IS Regulations, 2021. There are four categories-1a, 1b, 2, and 3-depending on what is permitted and the overall management goal  [43, 44] , and the "Not listed species" (denoted by NL; 33; Tables  2  and A1 ). Unlisted species: Alien species that are not listed in the NEM:BA-A&IS Regulations but have been reported as present in natural or semi-natural ecosystems in South Africa or on offshore islands Depending on the risk assessment outcome of each alien species in South Africa and their differential occurrence and impacts in different provinces (Kumschick et al.
Species Classification: Continental Origin, Life Forms, and NEM:BA-A&IS Categories To compare the numbers of alien species in different classification categories (i. e. , life form, NEM:BA-A&IS Regulations' categories, species' continental origin and reason for plant species introduction (pathway)), species count data were analysed using a Generalised Linear Models (GLM), with a Poisson error distribution and log link in SPSS software, version 20. The counts of alien plant species were generated from total records obtained from different gardens, and they were specified as the dependent variable. Different species classification categories were treated as predictor variables.
Concluding Remarks In this study, we have presented a list of alien and invasive plant species occurring in SANBI gardens located in different parts of South Africa, which may guide the safeguarding of biodiversity conservation gardens from the threats posed by biological invasions  [2]. Indeed, compiling species lists and regular monitoring of high-risk sites, including the botanical gardens, can strengthen their management if public awareness campaigns are conducted timeously and the escape of various alien species from different points of introduction is reduced. We also recognise the complexity of the relationship between human socio-economic needs and alien species, and thus advocate for increased awareness of negative impacts as a potential strategy for mitigation. The findings in this study constitute progress toward reducing the reported uncertainty of the existing alien species data sets that restrict planning management of biological invasions in South Africa  [63].
Information on the towns' architecture derived from  Carti (2017)  for Nice and  Casta et al. (1996)  for Bastia. The mapping was conducted using the software  QGIS 3. 16 (GGIS. org, 2021)  with the OpenStreet map background. To appreciate whether a segregation exists between the two species distribution within each town, we used a 100x100m grid and counted within each square the number of sites occupied respectively by Common Swifts, Pallid Swifts, and mixed sites. Comparative information on the presence and demography of the two swifts in both regions is presented in Table  1. The statistical analyses were carried out using the free software BioStaTGV (http://biostatgv. sentiweb. fr/) and the figures of the correspondence analysis (CA) carried out with RStudio  (RStudio Team, 2020).
Acknowledgements In Corsica, we are grateful to the people and organizations who facilitated our access to the buildings where swifts were nesting, or entrusted chicks that had fallen from the nest or were injured, in particular  Chantal Belgodere ,  Stéphane Japet ,  Cécile Jolin ,  Arnaud Lebret , and  Elisabeth Stodd. Véronique Vancoillie  coordinated our project with the town of Bastia. To all of them, we address our warmest thanks.
Note we add superscript indices for clarity where appropriate. For example, "h t t+1 " represents the prior estimate of the state variable h at time t + 1 given forcing data up to time t (i. e. , forecast without any conditioning), whereas "h t+1 " means the best estimate of the state variable h at time t + 1 (i. e. , which will arise from all information available up to time t + 1). at a point scale and on a daily basis, the main biophysical processes and relationships governing soil water balance, phenological transition controls, light interception, biomass accumulation (carbon assimilation), and biomass partitioning between different organs (leaf, shoot, fruit and root) as a function of phenological stage. The reader is referred to  Walker et al. (2005 Walker et al. ( , 2020a, b, c) , b, c)  for a detailed description of Vine-LOGIC and its supporting datasets.
Climate/weather data Daily weather data are obtained from a nearby weather station (located approximately 100 m from the vineyard) (Loxton Research Centre Station 024,024; Bureau of Meteorology). Data spanning the current (2020-2021) and preceding growing season (2019-2020) are used for direct assimilation (Section 3. 4. 1). Data spanning the preceding 10-year period  (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018)  are also used for the purposes of characterizing plausible future climate/weather conditions (Section 3. 4. 1).
LAI data LAI data were obtained at between weekly and fortnightly time intervals (giving rise to a total of 33 observation times) using the nondestructive VitiCanopy system  (De Bei et al. , 2016). This involved photographing one section of canopy per panel or approximately every 6 m (each image represents up to 1. 81 m of linear cordon), with images then being processed through the VitiCanopy system to determine a plant area index (PAI) value for each image, which was then averaged to represent the vineyard scale. Photographs were taken along vine rows between stems (trunks) to avoid interference, using a smartphone camera tilted at 30 • to capture the whole canopy with the sky as background rather than the row behind it (Fig. 4 ). Processed images reflect PAI as the entire plant area is captured including the cordon or permanent wood, which becomes occluded by leaves, at which time the value represents LAI.
Direct updating of climate/weather data Daily weather data corresponding to the 2019-2020 and 2020-2021 growing seasons are directly updated in the model (Section 3. 2. 1). This involves, for each day, replacing the 'background' climate/weather data, representing prior climate/weather uncertainty, with observed data from Loxton Research Centre weather station. We assume zero variance about these climate/weather observations. Climate/weather data used as 'background' conditions represent samples of historical records collected at the same weather station spanning the period 2009-2019.
The reader is referred to Section 5. 2. 2 for more details. The need for a DA software tool that is non-intrusive (i. e. , can interface flexibly with any model, without the need for 'statically linking') to continuously map insights to end-of-season QoI is not expected to serve as a significant barrier to effective crop model DA. This reflects the availability of the recently developed software tool PESTPP-DA  (Alzariee et al. , 2021). PESTPP-DA-and indeed all the software tools within the PEST++ suite  (White et al. , 2020) -has potential to facilitate model-based agricultural decision support beyond that demonstrated herein, particularly given its non-intrusiveness, public availability and open-source development environment. It is therefore anticipated that PEST++ can play an important role in accelerating adoption of model-based decision support analyses in agriculture, especially given recent and ongoing software developments to lower the barrier to entry (e.
Acknowledgements This research was supported by funding from  Wine Australia , with co-funding from  Riverland Wine. Wine Australia invests  in and manages research, development and extension on behalf of Australia's grape growers and winemakers and the  Australian Government. Riverland Wine  is the regional representative body for the wine industry in the  Riverland (South Australia). We acknowledge  Wayne Kiely  ( The University of Adelaide ) for his assistance in the collection of field data pertaining to the case study, and  Everard Edwards  ( CSIRO ) for helpful discussions regarding canopy data interpretation. We thank  Richard Niswonger  ( US Geological Survey ) and two anonymous reviewers for their constructive comments. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U.
Contributions of the authors. Ehouman Serge Koffi wrote the manuscript with the data collected and compiled with Hydraccess software by Ehouman Serge Koffi and Maurice Guilliod. Dabissi Djibril Noufé, Bamory Kamagaté, Lanciné Droh Goné are the initiators of the "Lagune Aghien" project. Luc Séguis and Jean-Louis Perrin are our research partners in the Aghien Lagoon project. Anzoumanan Kamagaté and Amidou Dao helped to read and correct the paper.
Acknowledgements Our thanks go to the " Debt Reduction and Development Contract " (C2D) between France  and  Côte d'Ivoire  which funded the research activities of " Aghien lagoon " project through the partnership  PReSeD-CI. This partnership was a very good collaboration between researchers from  Université NANGUI ABROGOUA (UNA) , in particular those from the " Laboratoire de Géosciences et Environnement " (LGE) and researchers from the  French Institute for Research and Development (IRD).
Draix-Bléone critical zone observatory was created in 1983 to study erosion processes in a mountainous badland region of the French Southern Alps. Six catchments of varying size (0. 001 to 22 km 2 ) and vegetation cover are equipped to measure water and sediment fluxes, both as bedload and suspended load. This paper presents the core dataset of the observatory, including rainfall and meteorology, high-frequency discharge and suspended sediment concentration and eventscale bedload volumes. The longest records span almost 40 years. Measurement and data processing methods are presented, as well as data quality assessment procedures and examples of results. All the data presented in this paper is available on the open repository https://doi. org/10. 17180/obs. draix (Draix-Bleone Observatory, 2015) and a 5-year snapshot is available for review at https://doi.
CC BY 4. 0 License. Fig. 2 ). At a regional scale, it has been estimated that these Terres Noires provide 40 % of the total sediment flux of the Durance river, whereas they only represent 1. 5 % of its catchment  (Copard et al, 2018). The climate is characteristic of a mountainous and Mediterranean region with two rain periods in spring and autumn, winter frost and intense summer storms that may give rise to flash floods. Mean annual rainfall for the observatory is 919 mm and mean annual temperature is 9. 8°C. More details on the rainfall and meteorological dataset are given in Sect. 3. 1. While most of the badland area is denuded, some hillslopes were reforested at the end of the XIX th century following erosion control policies (Restauration des Terrains de Montagne, RTM) in the French Alps.
Data The present paper focuses on the core data set of the observatory, which includes rainfall and meteorological data, hydrological data, and sediment flux data for both bedload and suspended load. In the following, we detail how this data is collected and processed before reaching the database. We present the type of instruments that are used, the acquisition protocol, the data filtering or gap filling procedures when applicable, the data quality assessment. Data quality is defined following a code that is defined in Table  2.
Measurement Rainfall has been measured at 9 locations, currently 8, indicated in Fig. 3 , using tipping bucket rainfall recorders that allows recording each 0. 1 or 0. 2 mm of rain (cone surface of 1000 or 2000 cm 2 ). Table  3  lists all the raingauges, the type of instrument and logger, the coordinates and elevations, and the period of data acquisition. All the raingauges except three are equipped with an insulated chamber, and a heating system was installed only for the Laval raingauge in 2007. Data is stored in a logger system that records tipping times. All the raingauges except two are also equipped with a container that collects water under the raingauge. Every two weeks, data is retrieved from the logger, the raingauge is cleaned if needed (leaves, needles) and the volume stored in the container is measured to check that there is no bias or drift in the instrument.
Data processing The raw data series is processed as follows: First, when the data is retrieved from the logger, we check for consistency between cumulated recorded rainfall and water volume stored in the contained below the raingauge. If these two values differ by more than 2 % and less than 10 %, the raingauge is recalibrated, and the data for the antecedent period is corrected such that the cumulated rain over the period corresponds to the real water volume that was collected. Secondly, data from all the rain gauges are compared. Due to the small spatial extent of the site, most rainfall events reach all the raingauges. Snow events are detectable in the raw data set by the difference in the cumulated rainfall recorded by heated and non-heated raingauges. Cumulated rainfall recorded by non-heated raingauges increases slowly over the days that follow the precipitation event as snow melts, especially during daytime, whereas cumulated rainfall recorded by the heated raingauge increases more quickly over the duration of the precipitation event.