dataesr/scientific-paragraphs-categorization huggingface dataset:
EUSEDcollab contains measured time series data from monitored catchments across Europe, including measured discharge (Q), suspended sediment load (SSL), as well as additional precipitation and land-use information where available. Table. 1: Catchment overviews for the four monitored catchment sites simulated in this study. The catchments were selected for their relatively close proximity in Central Belgium and North-West France, meaning they are subject to similar climatic regimes as well as loess soil substrates. The metadata fields for each catchment correspond to the information available in EUSEDcollab  (Matthews et al. , 2023b). The 'Data type in EUSEDcollab' refers to the temporal structure of the time series data as it is made publicly available. 'Event -variable timestep' signifies event data with a variable timestep within each event  Eventaggregated  (Richet et al.
, 2020; Ouvry and et al. , 2018; Grangeon et al. , 2022; Pak and et al. , 2018; Grangeon et al. , 2020)  FDTL France 145 2011/11/01 : 2018/08/31 Eventaggregated  (Richet et al. , 2020; Ouvry and et al. , 2018; Grangeon et al. , 2022; Pak and et al. , 2018; Grangeon et al. , 2020)  In this study, we utilised four catchment datasets with measurements taken in ephemeral stream channels activated during rainfall-runoff events (Fig. 1  & 2). The selected catchments, all situated on the European loess belt  (Lehmkuhl et al. , 2021) , were as follows: Ganspoel, BE, Kinderveld, BE, Bourville (BRVL), FR and FDTL, FR, with a catchment drainage area of 117, 250, 1045 and 145 hectares (ha) respectively. The FDTL is a nested sub-catchment within the BRVL catchment, while the Ganspoel and Kinderveld are in close proximity to each other (Fig.
, 2007) , the ktc parameter pairs for TC2 and TC3 were rescaled within the W/S software by dividing by 10 4 and 10 6 respectively. Previous implementations of W/S over multiple time steps have noted issues with unrealistic cellular sediment deposition rates which exceed plausible patterns of landscape evolution  (Van Loo et al. , 2017). This is particularly true for TC formulae with exponents on the cellular drainage area and slope variables exceeding 1, which exaggerate errors in secondary attributes derived from the DEM model surface. The potential reasons propagate from fundamental scale issues with limited process representation, to instrumental data acquisition, to the various stages of algorithmic pre-processing  (Wechsler, 2007). A cellular limit of 5 mm per 15-days was therefore set within the model code, meaning that outstanding sediment mass is continuously routed and deposited in a more diffusive manner.
Model parameterisation Dynamic (15-day) and static input layers were processed multi-or single layers of input parameters respectively at a 25-m resolution. Rainfall timing and magnitude properties are the independent variable within the (R)USLE module  (Kinnell, 2010) , with established losses in accuracy when large-scale gridded meteorological forcings are used for its parameterisation  (Bezak et al. , 2022; Matthews et al. , 2022). We consequently rely on the catchment records of measured rainfall in EUSEDcollab for these data inputs (Table. 1). The input layers for all other model parameters were prepared using standard methods built upon data sources with at least an EU coverage (Fig. 3 ). An overview of all temporally static data layers is given in Table. (2) and a further explanation of the processing methods given in SI.
A noteworthy consideration is that this method accounts for only the canopy-cover component of the C-factor, which is the component receiving the overwhelming majority of research  (Alewell et al. , 2019). Possible deviation in the temporal patterns of the SLR due to prior-land-use, surface-cover (i. e. crop residues), surfaceroughness and soil-moisture is therefore an outstanding source of model parametrisation error. A key adaptation of the method was made in the Google Earth Engine workflow to replace the Sentinel-2 data with a harmonised version of the Landsat 5, 7 and 8 collections to cover more extensive time periods. To mitigate against changing field parcel structures through time, the 2018 IACS data layer was used and pixels were acquired from within a central polygon around the 2018 parcel centroid (SI.
385 Table  4 : Per catchment model performances from introducing a temporally dynamic model structure into W/S using TC2 (eq. 3). The evaluations are made for the 15-day time series of simulated and observed suspended sediment load over the complete catchment measurement period. Note that the ktc pair for TC2 are rescaled within the W/S software by dividing by 10  The posterior monthly patterns of the optimised ktc parameters displayed seasonality which corrects error in the represented model processes and parameter inputs. For the Kinderveld, BRVL and FDTL catchments, the posterior interpretation of the ktc temporal curves showed significant reduction in the ktc parameters in summer months relative to the winter months (Fig. 7 ). These examples exhibit cases of loosely-coupled gross erosion and sediment delivery, the correlation coefficients between the 15-day mean predicted gross erosion and measured suspended sediment load were 0.
, 2013; Parsons et al. , 2006; Cerdan et al. , 2004). In three of the four modelled catchments, the predicted temporal signal of gross erosion from the RUSLE module did not strongly correlate with the measured SSL. In these cases a temporally static sediment delivery module strongly impeded the model performance (Fig. 5 ). Winter runoff events dominate the runoff and SSL budget in the BRVL and FDTL catchments  (Grangeon et al. , 2022) , representing cases in which the seasonal dynamics of predicted gross erosion and measured SSL were inverted. Contrastingly, in the Ganspoel catchment, the predicted dynamics of gross erosion explained a considerable amount of the variability in the sediment yield (R 2 = 0. 41), therefore resulting in a relatively higher model performance (NSE = 0. 28). The occurrence of one high correlation suggests that a globally low accuracy in the parameterised dynamic variables is not the case, although this cannot rule out changing parameter input accuracies or unconsidered processes impacting the gross erosion dynamics in different catchments  (Kinnell, 2015).
, 1982). In this study, the addition of a pixel-based CN runoff coefficient to the transport capacity equation (TC3) was made through a simplistic empirical model which matches the low parameter demand of the (R)USLE. The consequential decrease in model performance occurred for two likely reasons: 1) the introduction of a high cell-wise sensitivity within the sediment routing module (SI. 2), and 2) incorrect temporal profiles of predicted runoff which can cause their divergence from the RUSLE parameters through time (Fig. 10 ). Both reasonings link back to the potential cascading error when explicitly including overland flow, in that accurately describing its spatial and temporal magnitudes and threshold behavior introduces high model sensitivity, with incorrect predictions hindering the model performance  (Gómez et al.
Code availability The Python code to implement the model input layer pre-processing and multitemporal implementation of WaTEM/SEDEM is made available as a Figshare online repository (WaTEM/SEDEM_dynamic_EU, 2023). Note that all future software and documentation updates will be made through the Github repository (https://github. com/matfran/WaTEM_SEDEM_dynamic_EU. git). The R-factor processing code can be openly accessed online at: https://github. com/cn-ws/rfactor.
Acknowledgements The authors acknowledge the financial support provided for this research provided from the  Collaborative Doctoral Partnerships (CDP)  initiative of the  Joint Research Centre (JRC)  grant number  35332  and the  Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders  -application  S003017N ). The authors would like to thank  Jean-François Ouvry ,  Jean-Baptiste Richet ,  Lai Ting Pak  and the other contributors to the BRVL and  FDTL catchment monitoring campaigns in Normandy, France. For the monitoring efforts of the Kinderveld and Ganspoel  catchments, Belgium , the authors extend their gratitude to the efforts of  An Steegen ,  Gerard Govers , and other project contributors. We additionally thank  Petra Deproost  and  Johan Van de Wauw  of the  Departement Omgeving Flanders , as well as  Sacha Gobeyn  and  Daan Renders  from  FLUVES  for their contributions to the software developments of WaTEM/SEDEM.
Introduction Agroecology, is a discipline and a movement  (Wezel et al. , 2009). As a discipline is can be defined as the ecological science of food production  (Francis et al. , 2003). Agroecology is also a movement and a set of principles of action aiming to drive the transformation of food system toward sustainability  (Wezel et al. , 2009). Among its principles of action is the choice to build concerted solutions, promoting diversity and synergy among ecosystem components, based on local observations and knowledge  (FAO, 2018). Agroecological diversification and local solutions therefore harbors high value in terms of participatory research on agroecosystems components and their inter-actions. Yet putting together this knowledge and the diversity of local solutions, and testing agroecological hypotheses regarding synergies and diversity requires organizing a common agroecological dataset  (FAO, 2019).
The system count with a large number of icons that allow to represent agroecological information in its different dimensions of practices, crops (89 crops are graphically represented), farm characterization for certification, and crop pest and disease observations for research. Each variable is associated with help pages (links, images, text alimented by administrator) to accompany users data collection. 6. Activate or deactivates variables and categories so that users can or cannot collect data for these variables (Figure  2h ). 7. GenerateTAGs ("Etiquetas") (Figure  2i ) and add these tags in thevariable edition panel. TAGs are selection of variables that can be published as web pages by users for traceability purposes (research, or market differentiation). 8. Generate posts in the BLOG web pages for the system that can be used as help pages for users of the system.
Here we present the EUropean SEDiments collaboration (EUSEDcollab) database, a multi-source platform containing over 1600 catchment years of water discharge and sediment yield time series measurements suitable for soil erosion, sediment delivery and runoff studies. The dataset originates from collaborative efforts between a network of researchers and practitioners across the community with the goal of increasing data accessibility and usability. The data collection and harmonisation campaign was undertaken in multiple phases: (1) a call of interest for participation was made to the research community, issued by the Joint Research Centre (JRC) as part of the erosion working group within the EU Soil Observatory (EUSO), (2) interested collaborators were given (meta-)data templates to compile and share time series data to a centralised data repository, and (3) following data acquisition, a harmonisation and quality checking effort was undertaken to create a standardised database from the multiple data contributors.
Following this process, we provide the first data release Table  1. The standardised metadata template issued to the collaborating data producers of EUSEDcollab in the data collection campaign. Each time series of water discharge and sediment yield has an accompanying metadata file to allow the filtering based on method or catchment attributes and provide the user with relevant contextual information (e. g. method descriptors and published work). Metadata identifiers were open or categorical for the data producer, or otherwise assigned during the database harmonisation process. The '% populated' column refers to the % completeness of each metadata field for the entire collected database. For Boolean variables, the % populated column gives the database % with an accompanying count of the cases with a true value (i.
e. containing precipitation or sediment rating curve data). (EUSEDcollab. v1) of a continuing collaboration and data collation campaign through the EUSO, with the broad objective of converging scientific knowledge, people and data for research and policy-related objectives in Europe  48. Table  3. An overview of database entries with a daily timestep and their respective assigned IDs. The 'Literature references' column gives the corresponding studies on the catchment undertaken before the data submission phase. Catchment.
Methods Data collection: scope. The initial scope of EUSEDcollab on conception was to identify and unite high value research data in predominantly agricultural landscapes across Europe. Binary conditions were not set during the data collation phase, rather holistic criteria were made to be reflected in the compiled database, such as: (1) a significant contribution of rill and inter-rill erosion to the total sediment yield among the other relevant erosion processes (i. e. landslides, gullying and river bank erosion), and (2) a small to medium spatial scale (<1000 km 2 ) in which the signal of hillslope sediment delivery is reflected in the sediment yield dynamics. Following this, an inclusionary approach is taken to maximise the number of catchment datasets in the repository, allowing a user to later subset the data repository based on their needs.
temporal extent) and the methods used to measure and quantify the water discharge and sediment yield. Land cover information is included as a metadata field since it gives the opportunity for data contributors to add and qualify primary descriptive catchment properties with more localised detail than is possible with auxiliary large-scale landcover datasets. At minimum, each catchment entry contains a Q and SSL timeseries with a metadata file providing the geographic coordinates of the monitoring station location. However, for the majority of catchment entries the population of each metadata field within EUSEDcollab is relatively high (Table  1 ). Where possible, we also include: (1) precipitation time series data and rain gauge location information, (2) accompanying literature references from relevant publications for each dataset, and (3) a readme file to give expert-based contextual information to the end-user and qualify any necessary considerations within the time series data.
Data Records The EUSEDcollab repository contains 245 catchments with time series of Q and SSL (Tables  2 3 4 ). We include a further seven catchment records with full Q time series and intermittent SSC measurements for a user to define their own extrapolation method, since no prior extrapolation was completed in these cases. These records are not considered in the subsequent summary but are included in the data release with accompanying metadata files. The combined dataset covers over 1600 catchment years of water discharge and suspended sediment load records. Based on time-structure, this repository is divided into 22 daily data records, 212 monthly records, 1 event record with a fixed timestep, 2 event records with variable timesteps, and 8 event records with event aggregations (Fig. 1 ).
technical Validation Technical validation of each original record is done in a decentralised manor by the data producer. The multi-source nature of EUSEDcollab means that measurements of Q and SSL measurements were acquired with varying apparatus set-ups, temporal structures and post-processing methods (Tables  2 3 4 ). Acknowledging varying degrees of data heterogeneity requires end-users to make a judgement on the inter-comparability of catchment records for a particular use-case, based on differing measuring extents, sampling resolutions and uncertainty sources. As a data integration and harmonisation exercise, we aimed to facilitate this user-side assessment by providing necessary metadata properties, namely: (1) water discharge method descriptors, (2) sediment flux measurement and quantification methods, and (3) quality control properties describing the frequency of monitoring station checks, (4) literature references, and (5) dataset contact information (Table  1 ).
Data evaluation: quality and completeness assessment. To give a centralised assessment of the completeness and consistency of each submitted time series record, a ready-to-use evaluation was made of missing data inputs (Fig. 5 ). For example, missing inputs could be due to temporary technical issues, incomplete measurements or periodic discontinuation. Depending on the use-case, missing data may limit the applicability of a catchment dataset to a certain task and therefore may be useful for a user to know a priori. The compiled time series entries in EUSEDcollab contain continuous measurements (e. g. with a daily or monthly timestep) in perennial streams or episodic measurements (e. g. time-aggregated or time-distributed events) in discontinuous streams. Based on these structural data characteristics, adapted evaluation routines were used to summarise data presence/absence through time (Fig.
Bottom: summary-level empirical relationships found within the database entries, showing a) the relationship between catchment area (km 2 ) and specific sediment yield (t km 2 yr -1 ), and (b) the relationship between mean annual discharge (m 3 yr -1 ) and the mean annual sediment yield (t yr -1 ) for all high temporal resolution datasets (excluding monthly data). The error bars show the variation of the annual sediment yield values around the mean annual average.
Usage Notes Data opportunities. EUSEDcollab is the first database of its kind in Europe, intended as a resource for a non-exhaustive range of applications relating to runoff, soil loss by water erosion and sediment delivery research at singular or multiple sites. These opportunities can include a range of research domains seeking to understand the system dynamics of catchment-scale runoff, erosion and sediment fluxes (Figs. 4, 6 ). These may include modelled and analytical developments in frequency-intensity relationships  26, 27, 57, 58  , spatial and temporal scale-effects  25, [59] [60] [61]  , or internal (e. g. topography, geology, soil characteristics), external (e. g. meteorological conditions) and anthropogenic (e. g. land use and land cover) drivers of sediment variability  62. By uniting data from across a European scientific network, we aim to: (1) release an open-access data resource hosted on the European Soil Data Centre (ESDAC) with the goal of continued database growth in a standardised manor, (2) mitigate data loss from discontinued research projects, (3) build a repository upon which a broad range of analytical and modelling methods can be built to advance scientific knowledge, and (4) allow cross-domain intercomparisons to assess the generalisation of empirical relationships and model prediction systems.
Data limitations. Data users are advised to consider the applicability of each utilised dataset for their application. These considerations range from the spatial scale (drainage area) of the catchment in its context-dependent environmental setting, to the temporal detail and measurement-richness underlying the dataset. The data quality evaluation gives additional relevant information on the time series completeness in order for initial evaluations to be made (Fig. 5 ). The EUSEDcollab. v1 repository has a significant spatial bias in its coverage due to a large number of data additions from small to medium sized catchments from a national monitoring campaign in Denmark  56. These data have evidenced usage in erosion modelling  36  but may not meet the requirements of certain high temporal resolution research applications due to infrequent underlying suspended sediment sampling.
We envisage that continued catchment data inputs from national monitoring campaigns fitting the motivations of EUSEDcollab will improve the overall spatial coverage and reduce this spatial bias. Data platform and continued community contributions. The EUSEDcollab repository is openly accessible via the European Soil Data Centre 63 (ESDAC) platform (https://esdac. jrc. ec. europa. eu/content/ EUSEDcollab) and Figshare  64. All files are provided in. csv format in their relevant folders and are identifiable based on the assigned ID listed in the overview file (Catchment_ID_assignment. csv). In the case of database-wide applications, users are requested to cite this article as the reference for the entire repository. In cases of individual catchment applications, users should refer to the reference studies for each catchment provided in the metadata and summarised in Tables  2 3 4.
EUSEDcollab. v1 is intended as the first version of a continued effort to gather and platform data through collaborative efforts from across the community. Future data collection efforts will seek to extend the size and scope of the repository through including a wider diversity of catchment types (e. g. pristine forests, badlands etc. ) across a wider range of elevation settings. Further contributions can be made to the database by downloading and completing the data and meta-data template files available in the ESDAC data portal (https://esdac. jrc. ec. europa. eu/content/EUSEDcollab). Data Fig. 5  An overview of the data quality control procedure to include an evaluation of missing data entries within each time series record. A modified evaluation is made according to the time series structure of each data record.
The output of the quality control procedure provides an accompanying JSON file for each data entry within EUSEDcollab. submissions can be included in future data releases by contacting the listed data manager through the contact details listed in the ESDAC data portal. Fig. 6  Example syntheses of time series data from the Kinderveld catchment, BE (250 ha) and the Nučice catchment, CZ (53 ha) in the EUSEDcollab repository. Note that the data is not area-normalised and the data from the Kinderveld catchment (a) is presented in tonnes per aggregated event, while the Nučice catchment (b) is made available and presented in tonnes per day. Additionally, it is important to consider the following contextual factors: (i) The Nučice measurements include periods with baseflow carrying sediments, whereas in the Kinderveld, only runoff events are included.
acknowledgements All data included in EUSEDcollab is all open-access to the user-community. The authors gratefully acknowledge network of funding agencies, monitoring station operators and data managers required to maintain the sediment monitoring programs that underwrite the existence of this data compilation. The first author acknowledges the financial support provided for this research provided from the  Collaborative Doctoral Partnerships (CDP) initiative of the Joint Research Centre (JRC)  grant number  35332  and the  Fonds Wetenschappelijk Onderzoek  (Research Foundation Flanders -application S003017N). Further acknowledgements corresponding to individual datasets are included in the readme files associated with each catchment.
The typical dust attenuation curve for the REQUIEM-2D galaxies is constrained to be steeper than Calzetti's law in the UV and generally consistent with A V < 1. Combined together and accounting for the different physical radial distances and formation timescales, we find that the REQUIEM-2D galaxies that formed earlier in the universe exhibit slow and uniform growth in their inner core, whereas the galaxies that formed later have rapid inner growth in their inner core with younger ages than the outskirts. These results challenge the currently accepted paradigm of how massive quiescent galaxies form, where the earliest galaxies are thought to form most rapidly. Significantly larger samples close to the epoch of formation with similar data quality and higher spectral resolution are required to validate this finding.