dataesr/scientific-paragraphs-categorization huggingface dataset:
Inland waters, estuaries and coastal vegetation DLEM-TAC 0. 5°×0. 5° 1980-2019 Yao et al. (2020), Tian et al. (2020) Mechanistic 0. 5°×0. 5° 2000 Lauerwald et al. (2019); Maavara Stochastic Model et al. (2019) Meta analysis-based watershed-level 2000 Hu et al. (2016) upscaling 18 regions 1975-2020 Rosentreter et al. (2023) Integrated ML & 0. 5°×0. 5° 2000 Marzadri et al. (2021) Physical model Atmospheric inversion models INVICAT 5. 625°×5. 625° 1997-2020 Wilson et al. (2014) PyVAR-CAMS 3. 75°×1. 875° 1997-2020 Thompson et al. (2014) MIROC4-ACTM ~2. 8°×2. 8° 1997-2019 Patra et al. (2018,2022) GEOS-Chem 5°×4° 1997-2019 Wells et al. (2018) Other models and datasets SRNM 1/12°×1/12° 1980-2020 Wang et al. (2020) Bookkeeping method 0. 25°×0. 25° 1980-2020 Tian et al. (2020), Keller and Reiners (1994) IMAGE-GNM Country-level 1980-2020 Bouwman et al.
All data were recorded within three hours of solar noon, and processed using the "ooacquire" and "photobiology" R packages  (Aphalo, 2015; Aphalo and Ylianttila, 2022). The following spectral photon ratios were calculated: UV-A:PAR, blue:red (B:R), blue:green (B:G), and red:far-red (R:FR) (UV-A: 315-400 nm; PAR: 400-700 nm; blue: 420-490 nm; green: 500-570 nm; red: 620-680 nm; far-red: 700-750 nm).
The studies utilise long-term and highly-quality observations of atmospheric composition on a global scale, combined with statistical approaches for identifying patterns in atmospheric composition change which can be traced back to modified emission behaviours in different parts of the world. Furthermore, the study by  Gkatzelis et al. (2021)  demonstrates the added value gained from the use of state-of-the-art tools of data-driven science for creating knowledge in atmospheric sciences. This study combines a traditional scientific review approach with the use of an openly accessible relational data base management system which holds the data from the publications included in the review. The approach allows the construction of a living review because new publications and data can easily be incorporated in the study and updated figures are produced directly from the database.
Particularly, the concept of scientific workflows has developed into a powerful tool over the past decade  (Atkinson et al. , 2017). Scientific workflows provide a systematic way of describing data analysis with its analytical activities, methods and data needed  (Cerezo et al. , 2013). They consist of a series of activities with input and output data and are directed to the description of scientific experiments and data analysis processes. Today, data-intensive workflows are capable to exploit rich and diverse data sources in distributed computing platforms and can be composed for re-using elements from other analytical processes in own applications; see also Chapter 5 in  Bouwer et al. (2022). will be open source managed on the Git repository on EGI infrastructure. The mature releases will also be published in Zenodo.
Science Demonstrators as Examples for Interoperability of Observation Infrastructures Atmospheric scientists entering the ENVRI-Hub though its portal on the European Open Science Cloud will reach graphical user interfaces for accessing data and services, such as map-based portals and lists of catalogued datasets, with semantic-driven search functionalities that enable them to search for appropriate datasets or services using controlled vocabularies of discipline- To give a brief description of data search and discovery (Figure  9 , left panel), the scientist browses the metadata catalogue with the help of the vocabulary, discovers data of potential interest, browses then the metadata catalogue for access information to the RI of interest with the help of the provided access documentation, and repeats this chain until all data of interest have been collected.
When ready, the results can be viewed in the tool and directly accessed through our Jupyter notebook service and Python library, to be combined with emission data for concentration calculations at the receptor(s) and comparison with observations retrieved from the RI data repositories. (3) The FAIR ENVRI Atmospheric Data Demonstrator aims to shorten time response in case of extreme events like fires, emissions form volcanic eruption and desert dust intrusion by providing scientific analyses as well as harmonized datasets and tools. It will offer the search for the ATMO-RIs data availability for the period and area of interest with a focus on ECVs, fetch the selected data including automatic previews of the datasets and provide statistical analysis on the downloaded time series. In addition, access to data from ATMO-RIs is offered as a bundle with automated compilation of provenance information, and a co-location service with satellite data is available.
Acknowledgements The European research infrastructures ACTRIS,  IAGOS  and  ICOS  gratefully acknowledge the continuous support by the  European Strategy Forum on Research Infrastructures , ESFRI, and the long-term funding by the  European Union 's  Research and Innovation programmes  through the cluster projects  ENVRI  ( 2011-2014 ; Grant Agreement  283465 ),  ENVRIplus  ( 2015-2019 ; Grant Agreement  654182 ),  ENVRI-FAIR  ( 2019-2023 ; Grant Agreement  824068 ),  ATMO-ACCESS  (started 2021, Grant Agreement  101008004 ),  RI-URBANS  (started 2021, Grant Agreement  101036245 ),  ICOS Cities  (started 2021, Grant Agreement  101037319 ), and  EOSC Future  (started 2021, Grant Agreement  101017536 ). Furthermore, all RIs gratefully acknowledge the support by the  European Union 's  Research and Innovation programmes  during the design and implementation phases of the individual RIs, and the support of the operation by all involved national research ministries and funding agencies.
The last database update originates from 26 January 2023 (version 5), adding 64 new publications with 930 new measurements. Operational European Research Infrastructures with their capacities for long-term continuous provision of observational data and services about air quality and atmospheric composition are comprehensively prepared to assume their role in this important field of cross-disciplinary research, jointly with life sciences and social sciences. The currently ongoing European research projects entitled Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial AreaS (RI-URBANS) and ICOS Cities (ICOS-Cities) are driving the development and implementation of air quality and greenhouse gas monitoring systems that will allow European cities as well as health administrations and agencies to effectively mitigate the impact of poor air quality on human health and to support climate action.
e. , provenance information as part of rich metadata, and the discoverability of data via enhanced search capabilities though standardised keywords and vocabularies, significantly broadens the database accessible to scientists. Making use of open-source programming environments linked with harmonised standard operating procedures for instruments measuring ECVs in accordance with the GCOS defined requirements for data accuracy and precision, promotes the collaborative development of data quality assurance and control procedures across the ATMO-RIs, culminating in an increasingly consistent dataset of ECVs. In analytical frameworks made available by the ATMO-RIs as well as other ENVRI-RIs through the ENVRI-Hub, ECV-based scientific workflow templates will provide the best data handling, processing, analysis, and visualisation experience.
The Caribbean country of Haiti is highly exposed to hydroclimatic hazards. However, there is no usable database that is easily accessible to the scientific community for this area. To fill this gap, hydroclimatic data 15 were collected to create the first historical database in Haiti. This database, called "Simbi" (guardian of rivers, freshwater, and rain in Haitian mythology), includes 156 monthly rainfall series over the period   59  daily rainfall series over the period 1920-1940, 70 daily streamflow series, and 23 monthly temperature series, not necessarily continuous, over the period. It also provides simulated streamflow series over the period 1920-1940 using the GR2M and GR4J rainfall-runoff models for 24 catchments and 48 20 attributes covering a wide range of topographic, climatic, geological, land use, hydrogeological, and hydrological signature indices.
Introduction Hydroclimatic databases, generally composed of climatic (precipitation and air temperature) and hydrological (streamflow) time series at the catchment scale, are extremely useful  (Tramblay et al. , 2021). They are used for water resources planning and management as well as for monitoring and forecasting floods, droughts, and 30 changes in surface and groundwater resources  (Dewandel et al. , 2003 (Dewandel et al. , , 2004;; Alfieri et al. , 2020; Harrigan et al. , 2020). These databases are also used to evaluate the performance of "new" hydro-meteorological products based on Earth observation satellites, which are increasingly applied in poorly instrumented regions  (Beck et al. , 2019; Brocca et al. , 2019; Prakash, 2019; Bathelemy et al. , 2022). Furthermore, they are central to studies of climate change impact, for example, through the calibration and evaluation of hydrological models used to 35 quantify climate change impacts on water resources  (Abbaspour et al.
, 2009; Chokkavarapu and Mandla, 2019; Teutschbein and Seibert, 2012). In recent years, hydroclimatic databases called "CAMELS" (catchment attributes and meteorology for largesample studies) have been created in several countries: United States  (Addor et al. , 2017) , Chile  (Alvarez-Garreton et al. , 2018) , Brazil  (Chagas et al. , 2020) , Great Britain  (Coxon et al. , 2020) , Australia  (Fowler et al. , 40 2021) , Central Europe  (Klingler et al. , 2021) , and Switzerland  (Höge et al. , 2023). The CAMELS databases use large datasets (precipitation, streamflow, air temperature, etc. ) from multiple sources (in situ, reanalysis, remote sensing, etc. ) over several hundreds of catchments. They also include multiple catchment attributes covering a range of topographic, climatic, hydrological, geological, and land use variables, etc.
While the CAMELS databases provide time series, other databases provide indices and hydroclimatic signatures of catchments, such as the African Database of Hydrometric Indices (ADHI;  Tramblay et al. , 2021). The main objective of these works is to make hydroclimatic data freely and easily available to the scientific community. Unfortunately, there are significant differences between countries in terms of the quality and quantity of hydroclimatic reference databases, as well as regarding access to these data. Some countries do not have such reference databases. This is the case of Haiti, whose territory is, nevertheless, highly exposed to natural 50 disasters  (Khouakhi et al. , 2017; Burgess et al. , 2018) , and which will, moreover, tend to increase due to climate change  (Peterson et al.
, 2002). At the same time, Haiti is facing the consequences of massive deforestation and anarchic urbanization in recent decades  (Hedges et al. , 2018; Tarter et al. , 2018; Mompremier et al. , 2022) , resulting in increased vulnerability to hydroclimatic hazards. Currently, there is no freely and easily accessible hydroclimatic database in Haiti. The only existing data are the 156 monthly rainfall 55 series  compiled by  Moron et al. , (2015) , the 70 daily streamflow series  from the BVH project (Bassins versants haïtien in French, i. e. , Haitian catchments;  Gaucherel et al. 2018) , paper archives in several institutions, and data collected as part of different projects, often available for very short periods. This BVH project includes the characterization of Haitian catchments using streamflow data  (Gaucherel et al.
, 2016)  and rainfall data  (Moron et al. , 2015). The BVH has also investigated existing 60 relationships between catchment shape, relief, and river sinuosity  (Gaucherel et al. , 2017 , Bonhomme et al. , 2013). These two databases (rainfall and streamflow data) are not known to the public, have never been cross-referenced to form a hydroclimatic database for the analysis of Haitian catchments, and remain largely underused to date. The main goal of this study is to cross-reference and analyze these different databases in order to propose 65 the first hydroclimatic database for several Haitian catchments. Nevertheless, most of the available historical Haitian streamflow series have gaps and are therefore limited in their application. One way to obtain continuous streamflow series is to use a hydrological model, fed by climatic time series, to reconstruct the missing values  (Brigode et al.
, 2016; Smith et al. , 2019). This method has been used for several decades for various types of catchments covering different climatic regions  (Caillouet et al. , 2017; Crooks and Kay, 2015 ; 70  Jones and Lister, 1998). Thus, monthly (GR2M,  Mouelhi et al. , 2006)  and daily (GR4J,  Perrin et al. , 2003)  lumped rainfall-runoff models were used to reconstruct continuous streamflow series in Haiti at both time steps. The goal of our work is therefore threefold: Observed hydroclimatic data, simulated streamflow series, and calculated catchment attributes make up the Simbi database, the first continuous and freely available hydrological database in Haiti. Simbi is a guardian of rivers, freshwater, and rain in Haitian mythology (https://en. wikipedia. org/wiki/Simbi). 85 2 Data used.
95 A modified version of the monthly rainfall database compiled by  Moron et al. (2015)  was used in this study. The modifications to the original database are described in detail in Appendix A. The original data produced by  Moron et al. (2015)  included 156 monthly rainfall series available from 1905 to 2005 and were derived from three different sources: 1. The CNIGS (National Center for Geospatial Information) database with 162 monthly rainfall series; 100 2. The database managed by the international company Chemonics (https://chemonics. com/, accessed 06/02/2023) with 109 monthly rainfall series; 3. The CNSA (National Coordination for Food Security) database with 14 monthly rainfall series. These three databases were merged by  Moron et al. (2015)  by removing and/or correcting duplicates.
Daily rainfall Several daily rainfall series were collected by the observatory of the Petit-Séminaire Collège St-Martial (PSCSM) in Port-au-Prince during the 20 th century. These data were available in paper archives of the Haitian Spiritual Library (BHS for Bibliothèque Haïtienne des Spiritains) in Port-au-Prince. Overall, 59 of these series 110 with data for the period 1920-1940 have been digitized for the Simbi database.
Calculation of catchment attributes Similar to the CAMELS databases  (Addor et al. , 2017 , Alvarez-Garreton et al. , 2018 , Chagas et al. , 2020 , Coxon et al. , 2020 , Fowler et al. , 2021 , Klingler et al. , 2021) , several attributes were calculated for each catchment. Thus, 48 attributes grouped into six classes (topography, geology, land use, land acquisition type, climate index, and hydrological signature) were calculated (see Table  C2  in Appendix C). These attributes 275 were selected according to the data available in Haiti and their relevance to Haitian catchments. The catchment contours defined in section 3. 1. 2 and the SRTM digital terrain model with a resolution of 90 m  (Reuter et al. , 2007)  were used to calculate the topographic attributes. The location of the catchment outlets was defined by the relocation process described in section 3.
Land use has changed significantly in recent decades, mainly due to massive deforestation and uncontrolled urbanization  (Churches et al. , 2014; Hedges et al. , 2018; Salomon et al. , 2022). Although the 290 past  and the current land (2000-2020) cover is not what it was in 1995 and 1998, we have nevertheless chosen to present two land cover attributes calculated over these periods. For each catchment, the geological, land use, and aquifer type layers of the shapefile are cut out to enable the determination of the various corresponding attributes. Although there is no clear correlation between the digitized and reanalyses temperatures, the KGE values (KGE in the evaluation for the two subperiods) obtained with the three air temperature databases are very similar for most of the catchments (bottom left figure).
This shows that the GR2M model, through its two parameters and especially the X2 parameter (bottom right figure), has the ability to absorb the potential biases associated with the air temperature data. 310 The three temperature databases could be used a priori for rainfall-runoff modeling, as the model parameters absorb the associated biases. However, since the reanalysis databases do not represent temperature well at the catchment scale, they will not be used in the remainder of this study. Therefore, the digitized temperatures will be used to build the Simbi database.
GR2M performance analysis Reference rainfall and rainfall from all possible combinations of raingauges were calculated at the catchment scale as described in section 3. 2. 1. Table  C1  in Appendix C presents the number of raingauges used to calculate reference rainfall, the number of combinations, and the most relevant raingauges for rainfall-runoff modeling for each of the catchments. 325 Figure  3  shows the summary of the KGE scores in the evaluation and its three components obtained with NOAA 20CR rainfall, reference rainfall, and relevant raingauge combination. The lowest KGE scores are obtained with NOAA 20CR rainfall, highlighting the limitations of this rainfall database for rainfall-runoff modeling in Haiti and the need to use observed data rather than reanalyses. There is also a clear improvement in KGE values when using the relevant raingauges compared to the reference raingauges.
Graphical summary sheets of the Simbi database 475 The main results are summarized in sheets that highlight the key characteristics of the catchments studied. These summary sheets have been inspired by those prepared by the catchment hydrology research group at INRAE  (Brigode et al. , 2020). An example is shown in Figure  15  describing the main characteristics of the Cavaillon catchment, which was studied several times after Hurricane Matthew  (Mathieu, 2023; Joseph, 2019; Joseph et al. , 2018). This catchment has an area of 320 km², half of which is at an elevation above 250 m, 480 with a slope greater than 10°, and overlies a karst aquifer.
Data availability The Simbi database is available for download at: https://doi. org/10. 23708/02POK6  (Bathelemy et al. , 2023). The SIMBI_README. txt file contains a description of the database and the organization of the various files and folders. The 00_OBSERVED_DATA folder contains the observed data (daily streamflow, daily and 495 monthly rainfall, and monthly air temperatures). The 01_CATCHMENT folder contains the contours and the outlets of the 24 catchments. The 02_SIMULATED_STREAMFLOW folder contains the simulated monthly and daily streamflow series. The 03_ATTRIBUTE folder contains all 48 catchment attributes. The 04_MAP folder contains catchment graphical summary sheets summarizing all available data and the main results obtained for each catchment. 500.
Conclusion and perspectives To the best of our knowledge, the hydrological database presented in this article represents the first open access work of an exhaustively documented hydro-meteorological dataset for Haiti. This database, called "Simbi," contains station observations and catchment-scale data. The station observations contain: The data at the catchments scale contain: 1. Simulated monthly streamflow series for 24 catchments and simulated daily streamflow series for 510 21 catchments using three sets of parameters (three simulated streamflow series per catchment) from the GR2M monthly and GR4J daily rainfall-runoff models, and 2. A set of indices that describe a wide range of low, moderate, and extreme rainfall and streamflow characteristics to characterize the hydrological regime and water resources management applications.
515 The processed database highlights the spatial variability of Haiti's hydrological conditions. The central part of Haiti is associated with relatively low streamflow and high drought coefficients. Conversely, the north and south of Haiti are associated with relatively high streamflow. In fact, large floods are more frequent in these areas  (Terrier et al. , 2017). The simulated monthly streamflows perform well in representing average streamflow and their spatial variability. However, the model is less effective at the daily time step (KGE score 520 in the evaluation is below 0. 5 for most watersheds). This results in poor representation of the frequency or number of consecutive days with high and low streamflow. This may be due to a combination of the quality of the data used and the calcareous geological formations that can create non-conservative catchments that are difficult to model.
Moreover, the estimation of relevant raingauge combinations and the simulation are dependent on the model used. A different model may produce different results. Thus, these results should be 525 used with caution. Our database can be considered a starting point for any hydroclimatic work in Haiti, since it gathers, in addition to the simulated data, all the hydroclimatic data available in Haiti over several decades. It could help to better understand the Haitian climatology in the twentieth century, and to study the evolution of the climate in Haiti for better adaptation to climate change. The database could also contribute to better knowledge of the 530 hydrology of the catchments and help assess the impact of massive deforestation and anarchic urbanization on the hydrological response of the catchments.
Acknowledgments The authors thank the  Bibliothèque Haïtienne des Spiritains (BHS)  for providing the paper archive of daily 545 rainfall data and the BVH project for providing the daily streamflow series. Special thanks to the students  Eddy-Terson François ,  Douninio Jeanite ,  Appollon Jean Philippe , and  John Claury Ménélas  of the  Université d'Etat d'Haïti  who contributed to digitizing the daily rainfall data and to the students  Kathleen Gerarduzzi ,  Camille Morillon , and  Alexandre Antony  of the  Université Côte d'Azur  who digitized the monthly air temperature data. Olivier Delaigue 's work inspired the design of our files, and we extend our thanks to him. 550 Thanks to  Isabella Athanassiou  for editing the English version of the manuscript. The authors also thank the  CLIMEXHA  project ( Anticipating Extreme CLIMATE events over HAITI for a sustainable development ), which contributed financially to the digitization of the daily rainfall data.
i) producing climatic (air temperature and rainfall) series at the catchment scale by spatially and 75 temporally aggregating available series, ii) creating a continuous Haitian hydroclimatic database for the 1920-1940 period, using the catchment climatic series and the rainfall-runoff models, iii) characterizing the hydrological behavior of Haitian catchments based on 48 hydrological indices and signatures covering six classes of catchment attributes (topographic, geological, 80 hydrogeological, land use, climate indices, and hydrological signatures).