dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, hydrological indices or hydrological signatures are useful to characterize the behavior of different components of river discharge, from low flows, annual runoff to floods  (Addor et al. , 2018; McMillan et al. , 2017) , and to assess the potential impact of climate change and human activities on river regimes  (Mahe et al. , 2013; Gnann et al. , 2020). They can be used for various purposes, including basin classifications, aquifer properties characterization, hydrological predictions in ungauged catchments  (Westerberg et al. , 2016)  and to investigate long term trends for different hydrological processes  (Do et al. , 2017; Nka et al. , 2015). We introduce here the African Dataset of Hydrometric Indices (ADHI) that aims at giving access to an ensemble of hydrometric indices computed from an unprecedented large ensemble of stations with daily discharge data  (Tramblay et al.
Data collection The database used in the present work is based on the collection of stations from the Global Runoff Data Center (GRDC) and the SIEREM database  (Boyer et al. , 2006; Dieulin et al. , 2019). The hydro-climatological data contained in SIEREM is the legacy from the former Laboratoire d'Hydrologie of the Office de la recherche scientifique et technique outre-mer (ORSTOM, now Institut de Recherche pour le Développement, IRD, France). It must be noted that in addition to the daily data, the SIEREM database also contains instantaneous rainfall and discharge for hundreds of experimental small catchments mostly established in the 1950s and 1960s. The criterion to include a station in the ADHI database is to have a minimum of 10 full years of daily discharge data between 1950 and 2018.
Most of the hydrological stations in French-speaking countries have been set up and managed for decades by the ORSTOM Institute  (Mahe and Olivry, 1999). At the time the data were processed, the SIEREM database included a total of 1046 series, with several of them being duplicates of the same monitoring station but for different time periods. There are a total of 101 stations with 2 times series, 42 stations with 3 time series, 24 stations with 4 time series and 7 stations with 5 time series. In most cases, one time series includes the longest record and that one was kept for the analysis in the present paper. For some stations, the different time series were differing substantially during the same period, due to different rating curves. A careful inspection of these series led to the elimination of erroneous or doubtful data.
Only for 17 stations the time series were concatenated, after making sure the rating curve(s) applied on the different time periods to compute river discharge were adequate, by comparing daily runoff on a common period. Additionally, to these 1046 series, 933 stations have been retrieved from the GRDC database. For 106 of these stations, there was a duplicate station in the SIEREM database with longer time series and the latter were selected. After this data quality processing step, 673 stations were kept for SIEREM and 800 for the GRDC database for a total of 1473 stations (Figure  1 ).
In addition, through visual inspection it was possible to identify stations where some gap filling methods have been applied (13 stations) or where the data are suspicious (28 stations). A flag has been added in the metadata to identify these stations. It is worth noting that, for the stations of the SIEREM database, most of the data were analyzed and criticized prior to the inclusion in the database by the former ORSTOM hydrology laboratory, with therefore a much reduced level of error in the archived data. In addition, to detect possible shifts in the data due to non-natural influences, such as an artificial drift in the monitoring devices, changing instrumentation, recalibration of the rating curve, or river regulation by dams or reservoirs, the Pettitt test  (Pettitt, 1979)  was applied to mean annual runoff series.
Catchment delineation and information about river regulation Station catchments areas have been delineated with the Hydroshed Digital Elevation Model (DEM) at 15 sec resolution using the TopoToolbox2 algorithm  (Schwanghart and Scherler, 2014). The map of the catchments is shown in Figure  3. Despite a careful check of the geographic coordinates of the stations, this type of automatic catchment delineation procedure is prone to some errors, in particular in regions with low elevation and flat terrain properties. For several hundred of catchments it is possible to compare the results of the automatic delineation procedure with the catchment boundaries available in the SIEREM database, which have been most often individually delineated and carefully checked from ground knowledge over the years  (Dieulin and Boyer, 2005).
From the catchment delineated, the mean, maximum and minimum altitude have been extracted and included in the metadata. Dams and reservoirs have also been extracted and added in the metadata of the stations. The Global Reservoir and Dam Database (GRanD) v1. 3  (Lehner et al. , 2011)  has also been considered to identify regulated basins. The number of dams included in each river basin has been extracted using the catchment boundaries. As shown in the metadata of GRanD, most of the dams in Africa basins have been constructed around the 1970s (Figure  4 ). The rivers could be considered regulated if at least one dam exists in the catchment area, otherwise the river is considered natural (Figure  1 ). However, the influence of dams and reservoirs on the flow regime are linked to the location of the regulation structure, the portion of the basin controlled, and the management strategies.
For instance, in a large basin with only one dam located on a small headwater catchment, its influence may not be distinguishable at the river outlet. On the other hand, a station located immediately downstream a dam outlet may have its flow regime strongly impacted by dam operations. It should be also noted that other regulation structures like small dams or water diversion channels that may not be included in the GRanD database could be present in the catchments considered natural  (Lehner et al. , 2011; Pekel et al. , 2016). This is particularly the case in semi-arid areas where earthenmade channels, often informal, draw their water supply from the river itself, by building small diverting structures  (Underhill, 1984; Kimmage, 1991). They can represent a large number of structures, but a variable amount of water withdrawal at the basin scale  (Barbier et al.
Indices computed on an annual basis These indices have been computed for each calendar year. This choice was motivated by the fact that hydrological years have different definitions across the different regions of Africa. For example, in North Africa and most of southern Africa the hydrological year starts in September, after the dry summer season, while in West Africa the hydrological year starts between January and March, depending on the latitude, with the annual maximum runoff occurring during the summer months. The use of calendar years has been also selected for consistency with other databases such as GSIM  (Do et al. , 2018; Gudmundsson et al. , 2018). These indexes have been computed for the years with less than 5% missing data: For example, for hydrogeology applications it would be interesting to compute the low stage specific discharge that is the ratio between the low-stage discharge and the area of the watershed.
This can be an indicator of aquifers' contribution to river discharge. The main issue is related to the definition of the low-stage discharge. From the indices proposed in the present database, it could be 5th percentiles of daily streamflow or the minimum of 7-days consecutive streamflow, per year. Similarly, the low-flow index could be computed from the ratio of the 90th and 50th percentiles of daily streamflow  (Smakhtin, 2001).
Conclusions and perspectives This new hydrological database brings together the largest number of African river flow measurement stations, in comparison with other previously published datasets. It results from a pooling of the GRDC and SIEREM databases, built from contributions of several agencies in African countries in charge of the management of hydrological measurement networks. This database will be regularly updated with data from SIEREM and GRDC. The dataset provides a series of indices that describes mean and extreme runoff, allowing the characterization of the hydrological regime and applications linked to the management of water resources and hydrological risks. Since most of the preprocessing steps have been automated, it would be possible to increase the number of stations considered or the length of the data series, if more data would become available.
The data from the SIEREM database is regularly updated from contributions of different organisms. In the future, individual contributions from researchers or institutes will be welcome to increase the spatio-temporal coverage of the data. The FRIEND programme (UNESCO/IHP) will also contribute to increase the number of stations through coordinated efforts at the regional level. More broadly, this ADHI database could contribute to a better knowledge on African hydrology. For instance, the impacts of dams on river discharge remains largely unquantified at the scale of Africa  (Biemans et al. , 2011). From these indices, various applications can be sought. For example, the percentiles of the daily streamflow could be useful to calibrate hydrological models using the flow duration curve  (McMillan et al.
, 2017)  and to constrain model outputs  (Tumbo and Hughes, 2015; Ndzabandzaba and Hughes, 2017). Flow duration curves are also useful for catchment classification according to their rainfall-runoff response  (Cheng et al. , 2012). In the recent years, global runoff simulations have been provided by the Global Flow Awareness System, with land surface or global hydrological model driven by reanalysis data  (Alfieri et al. , 2020; Harrigan et al. , 2020). Yet, due to the small number of stations representing African basins in the currently available databases preventing a robust calibration of the models, the hydrological simulations have a poor performance  (Harrigan et al. , 2020). More generally, this new ADHI database could open perspectives to apply hydrological models in African basins, in particular combined with recent remote sensing data products  (Brocca et al.
of 7-days consecutive streamflow, per year, and corresponding date 3. Annual maximum runoff, and the corresponding date This database includes a wide range of catchment sizes and rivers with different hydrological regimes, as shown on figure 5 displaying mean daily runoff against https://doi. org/10. 5194/essd-2020started: 19 November 2020 c Author(s) 2020. CC BY 4. 0 License. catchment areas. From the supplied indices, other useful indicators could be derived.
Acknowledgements River runoff has been obtained from The  Global Runoff Data Centre, 56068 Koblenz, Germany  and included in the database with their authorization. We would like to thank the  GRDC  for granting access to their data. A large part of the data processed in the present study comes from the SIEREM database, and the authors wish to express their gratitude to all the persons who contributed to this database over the years. The database is available from the online repository: https://doi. org/10. 23708/LXGXQ9Additional indices could be computed upon reasonable request to the corresponding author. This work is dedicated to the memory of  Claudine Dieulin  who passed away in January 2020 during the course of this project.
The pipeline is a combination of structure based (docking and pharmacophore models) and ligand based (pharmacophore models) methods. The models have been constructed using the Environmental Protection Agency (EPA) data encompassing a large number of structurally diverse compounds. A validation step was then achieved using two external databases: the NR-DBIND (Nuclear Receptors DataBase Including Negative Data) and the EADB (Estrogenic Activity DataBase). Different combination protocols were explored. Results showed that the combination of models performed better than each model taken individually. The consensus protocol that reached values of 0. 81 and 0. 54 for sensitivity and specificity, respectively, was the best suited for our toxicological study. Insights and recommendations were drawn to alleviate the screening quality of other projects focusing on ERα binding predictions.
Database Preparation After filtering and cleaning, the Environmental Protection Agency (EPA) database is a collection of 2442 chemical compounds experimentally tested for ERα binding comprising 2219 non-binding (NB) compounds and 223 binding (B) compounds (see Material and Methods section). The distribution of the physiochemical and constitutional descriptors of B and NB compounds is represented in Figure  1. Two external validation sets were used, i. e. , the NR-DBIND (Nuclear Receptors DataBase Including Negative Data) ERα set that comprises 732 compounds, divided into 554 B compounds and 178 NB compounds, and the EADB (Estrogenic Activity DataBase) set comprising 131 B compounds and 101 NB compounds for a total of 232 molecules. Distributions of the 15 constitutional, physiochemical, and molecular descriptors for each dataset are presented in Supplementary Figures  S1  and S2.
Databases Comparison Pairwise similarities were calculated using the Tanimoto coefficient (Tc) between each pair of topological fingerprints for: 1. ) the EPA database and the NR-DBIND and 2. ) the EPA and the EADB (see Figure  S3A ). The analysis of similarity values shows that the Tc are globally very low with a mean of 0. 181 for the pairing with NR-DBIND and 0. 174 for the pairing with EADB. Only 2% and 0. 6% of the total calculated Tc for EADB and NR-DBIND, respectively (as shown in Figure  S3B ), are higher than 0. 5. Finally, the chemical space of the three databases was mapped using a SALI (Structure Activity Landscape Index) map for the whole databases (Figure  2 ). The map illustrates that all three databases share the same chemical space.
The P(active) and enrichment factor (EF) deducted for these TH yielded better results for smina-dkoes than PLANTS. Regardless, trends are the same for both: the higher the sensitivity, the lower are the specificity, the P(active), and the EF. The behavior is the same for single structure and ensemble docking. In the light of the docking results, we decided to select for the rest of the study the smina-dkoes scoring function and the single structure docking approach (using the 1QKU PDB structure) and to select two potential scoring TH (-6 and -7). Table  3  presents the performance of the selected protocols on the EPA database and the external validation sets (EADB and NR-DBIND) in terms of specificity, sensitivity, and binders retrieval rate. Since the compounds of the active training set belong to different chemical series, their alignment to derive a single LB pharmacophore is not feasible.
To overcome this issue, all the compounds were clustered to obtain subsets of similar compounds for which pharmacophores can be generated. Distance between each cluster was fixed to 0. 4 to ensure balanced groups and to minimize the number of singletons. In total, 14 clusters were obtained containing a minimum of 3 and a maximum of 69 compounds per cluster. 6 molecules could not be fitted in any cluster and were not used to generate the pharmacophores models. The maximum number of pharmacophores generated per cluster was set to 10. Each pharmacophore was used to screen the training subset of the EPA database. Based on individual hit retrieval performances, the best pharmacophore of each cluster was optimized according to the procedure described in the methods section. In the case where the optimization protocol failed, i.
e. , the optimized pharmacophore was not associated with a high rate of B/NB, the other pharmacophores generated for this cluster were considered in the descent order of their individual performances until one pharmacophore could be successfully optimized. If none out of the 10 generated pharmacophores or the corresponding optimized were associated with a high rate of B/NB, no pharmacophore was conserved for this cluster. In total, 11 unique (non-redundant) LB pharmacophores were obtained. Their performances in terms of selectivity and sensitivity are described in Table  4. These 11 LB pharmacophores were combined and used to screen the training subset of the EPA database. High specificity and relatively low sensitivity values were obtained with 30% of the total of binders retrieved against only 2.
SBLB Pharmacophore Models Results for both SB and LB selective pharmacophores were combined into a set of SBLB pharmacophores for ERα binding compounds. Redundant pharmacophores were removed to obtain a total of 26 unique SBLB pharmacophores. Performance in terms of sensitivity and specificity of this ensemble of pharmacophores is shown in Table  4. The set of SBLB pharmacophores is able to retrieve almost 40% of B against only 3% of NB. The 26 SBLB pharmacophores were also used to screen the two external validation sets, i. e. , the EADB and the NR-DBIND ERα sets, and the results are shown in Table  4. For EADB, similarly to the results associated with the EPA database, high specificity and low sensitivity values were obtained. The opposite is observed with the NR-DBIND ERα set, for which the sensitivity value is higher than the specificity.
Consensus Protocol Using the consensus protocol, each molecule predicted as active using the docking or the pharmacophores models will be identified as an active compound in the consensus protocol results. The remaining compounds will be predicted as inactive. Performances obtained using this protocol for the EPA database and the validation datasets, i. e. , the EADB and the NR-DBIND ERα set are depicted in Table  5. Two docking TH defined using the PC were studied. For TH = -7, a sensitivity of 0. 56 and a specificity of 0. 76 are obtained for the EPA database. Conversely, for each validation set, the consensus protocol yields higher sensitivity (0. 832 and 0. 495, respectively) against lower specificities (0. 495 and 0. 029). When TH = -6 is chosen, the corresponding sensitivities are high: 0.
81, 0. 937, and 1 corresponding to the EPA database, the EADB, and the NR-DBIND, respectively. Recorded specificities are very low: 0. 51, 0. 158, and 0. 005 for the EPA, the EADB, and the NR-DBIND. The higher positive predictive value (PPV) for the EPA database is reached by applying the TH = -7, with a PPV value around 19%. The same trend is observed with the EADB external validation set, whereas quite similar PPV are obtained for both threshold using the NR-DBIND set. The PPV obtained with the external validation sets using both TH = -6 and TH = -7 were largely superior to those obtained with the EPA database. For equal specificity values between both TH, the TH = -6 yields better sensitivities for the EPA database as well as for the validation datasets. This is why our choice of docking TH is set at -6 for the consensus protocol.
Hierarchical Protocol We first evaluated the impact of using hierarchical screening with the pharmacophore models prior to or after the molecular docking models on the performance in enrichment. Since both protocols displayed similar performances in terms of sensitivity and specificity, we relied on computational times to select the protocol. We thus decide to first screen using the pharmacophore models and then using the optimal docking protocol previously defined. On a desktop computer with 8x Intel(R) Xeon(R) CPU L5520 @ 2. 27 GHz it takes ~75 min to dock the 2442 molecules against one ERα structure versus ~5 min to screen the same number of compounds on the 26 SBLB pharmacophore models. Results depicted in Table  5  are those obtained using this hierarchical screening, i. e. , the entire database is screened using the pharmacophore models and the compounds thereby identified as hits are used as the screening database for the docking method.
The docking outcomes are then analyzed using the 2 docking scores TH previously identified and corresponding to different sensitivity values. For both TH values, the same trend is observed, i. e. , high specificities (0. 99 and 0. 98) and low sensitivities (0. 25 and 0. 32). Table  4  also presents sensitivity, specificity, and PPV obtained using the hierarchical protocol on the validation sets. The performance associated with the EADB is very similar to those obtained with the EPA database whereas the hierarchical protocol applied on the NR-DBIND ERα set lead to high values of sensitivity and specificity for both thresholds. Based on the hierarchical protocol outcomes, in particular the sensitivity values, on both the EPA database and the external validation sets, we selected the TH = -6 as the threshold to be used for docking scores using the hierarchical protocol.
Compounds and Database Preparation The comparison of the distribution of the 15 constitutional descriptors for the three databases, i. e. , EPA, EADB, and NR-DBIND, was performed in order to ensure that the difference in activity was not solely explained by the difference in physiochemical properties. In order to assess the prediction performance of our models, we used external validation sets. Pairwise comparison of topological fingerprints between the EPA database and each external validation sets verifies the structural dissimilarity between those sets and thus the possible use of the EADB and NR-DBIND ERα sets as external validation sets. Moreover, the SALI map confirms that the three databases belong to the same chemical space, which was recommended for pharmacophore models validation  [27].
Docking For the docking approach, both single structure and ensemble docking were explored. Three software with free academic licenses, accounting for 6 scoring functions, were used, i. e. , smina (smina-ad4, smina-dkoes, smina-vina, smina-vinardo), Surflex-dock, and PLANTS. Although different magnitudes of AUC were obtained, most of them agreed on the elected structure yielding the best single structure docking results: 4 out of the 6 docking methods associated the best outcomes with the 1a52 structure. However, the highest AUC were obtained with different structures, 1qku and 1x7e for smina-dkoes and PLANTS, respectively. Interestingly, the 1a52 structure presents an artifactual position of the helix that is extending away from the body of the ligand binding domain. The resulting conformation is more similar to an antagonist-bound ERα structure than an agonist-bound one  [4].
This observation leads to discard 1a52 despite its selection by most of the software and reinforces the choice of the 1qku structure and smina-dkoes as the optimal single structure docking protocol. It is to note that 1qku is co-crystallized with the native ligand 17β-estradiol. Furthermore, it was shown that smina_dkoes was very proficient at sampling low RMSD poses compared to Vina  [28]. No major performance improvement as evaluated by the AUC values was brought by ensemble docking over the single structure strategy. This was true using either only agonist-bound structures ensembles or combinations of agonist and antagonist-bound structures. When considering only agonist compounds as positives and the remaining compounds (antagonists and experimental non binders) as negatives, both agonist-bound and antagonist-bound structures were associated with similar AUC values (results not shown).