dataesr/scientific-paragraphs-categorization huggingface dataset:
(B) Transcription of sapS during growth in TSB. Cells of SA564 were cultured at 37 °C and 225 rpm in a culture to flask volume of 1:10, and after 3 h, 5 h, and 8 h of growth, cells were harvested, total RNAs isolated, and qRT-PCRs performed for sapS. Transcripts were quantified in reference to gyrase B mRNA. Data are presented as mean + SD of five biological replicates. (C,D) Survival of S. aureus strain derivatives in infected RAW 264. 7 macrophages. CFU rates recovered from infected cells lysates at the time points indicated. Data are presented as mean + SD (n = 5 biological replicates) (C) and survival rates expressed in relationship to the intracellular bacterial cell counts that were detected immediately after the gentamicin/lysostaphin treatment (T0), which was set to 100%. The data are displayed as box and whisker plots, displaying the median (horizontal Given that SapS (SACOL0303) includes a 31-aa long signal sequence at the N-terminus of its protein sequence  [28]  and that the protein was found repeatedly in S.
Cytoplasm (C), membrane (M), cell wall (W), and immunoprecipitated proteins (IP) from supernatants were migrated on SDS-PAGEs, transferred to PVDF membranes for Western-blot analyses using monoclonal mouse anti-Spot antibody as primary antibody (Chromotek, Germany) and an HRP-coupled donkey-anti-mouse antibody as secondary antibody (Jackson Immuno Research). Data are representative of three different experiments. (B) RAW 264. 7 macrophages (2. 5 × 10 5 cells/well) were infected with S. aureus at a MOI of 20, and non-phagocytosed bacteria were subsequently removed by gentamicin/lysostaphin treatment. Infected macrophages were lysed in 0. 1% Triton X-100 and centrifuged at 14,000× g. The macrophage lysates were immunoprecipitated with anti-Spot magnetic beads (Chromotek), while the pellets of the intracellular bacteria were resuspended in an equal volume of lysis buffer.
Data are representative of three different experiments. (B) RAW 264. 7 macrophages (2. 5 × 10 5 cells/well) were infected with S. aureus at a MOI of 20, and nonphagocytosed bacteria were subsequently removed by gentamicin/lysostaphin treatment. Infected macrophages were lysed in 0. 1% Triton X-100 and centrifuged at 14,000× g. The macrophage lysates were immunoprecipitated with anti-Spot magnetic beads (Chromotek), while the pellets of the intracellular bacteria were resuspended in an equal volume of lysis buffer. Immunoprecipitated proteins (IP) and bacterial pellets (BP) were separated on SDS-PAGE and observed with an anti-Spot antibody as described in (A). Data are representative of three different experiments (M kDa: molecular markers). Next, we determined how cells of the S. aureus strains SA564, SA564 ∆sapS, and SA564 ∆sapS::sapS survived inside RAW 264.
7), CD45R-PE-eFluor™ 610 (clone RA3-6B2), and NK1. 1-APC (clone PK136). All fluorochrome-conjugated antibodies were brought from eBioscience (Thermo Fisher Scientific). Flow cytometric analysis was performed using the BD FACSverse machine, and the data collected were analysed with the software Flowjo v10. 6. 2 (Becton Dickinson, Franklin Lakes, NJ, USA).
Blood samples were inoculated with fluorescently labelled bacteria at a MOI of 50 per PMN, and cultivated in the dark at 37 • C and 1000 rpm for 30 min. The cell suspensions were transferred into 5 mL round bottom polystyrene tubes (BD), the erythrocytes were lysed by the addition of FACS lysis solution (BD), and the lysed cell debris were eliminated by centrifugation at 450× g for 5 min. Flow cytometry was performed on the cell pellets after they were resuspended in PBS that included 2% foetal calf serum (PAA, Pasching, Germany) and 0. 05% sodium azide. The analysis was performed on a FACSCalibur (BD) device. The CellQuest Pro Software version 4. 02 (BD) was used to gate PMNs, and the mean fluorescence intensity (MFI) of each PMN was recorded. This value represents the amount of bacteria that were either adhered to or ingested by the leukocyte.
Figure1. SapS has no impact on S. aureus SA564 growth in vitro but contributes to intramacrophage survival. (A) Growth kinetics of S. aureus strains SA564 (black symbols), SA564 ∆sapS (blue symbols), and SA564 ∆sapS::sapS (grey symbols) in TSB. Cells were cultured at 37 • C and 150 rpm in 96-well plates using a Spark 20M (Tecan, Trading AG, Männedorf, Switzerland) microplate reader. Data represent the mean A 600 readings ± SD at the time points indicated (n = 3; error bars are too small to be visible). (B) Transcription of sapS during growth in TSB. Cells of SA564 were cultured at 37 • C and 225 rpm in a culture to flask volume of 1:10, and after 3 h, 5 h, and 8 h of growth, cells were harvested, total RNAs isolated, and qRT-PCRs performed for sapS. Transcripts were quantified in reference to gyrase B mRNA.
5 Figure 1. 51 Figure1. SapS has no impact on S. aureus SA564 growth in vitro but contributes to intramacrophage survival. (A) Growth kinetics of S. aureus strains SA564 (black symbols), SA564 ∆sapS (blue symbols), and SA564 ∆sapS::sapS (grey symbols) in TSB. Cells were cultured at 37 • C and 150 rpm in 96-well plates using a Spark 20M (Tecan, Trading AG, Männedorf, Switzerland) microplate reader. Data represent the mean A 600 readings ± SD at the time points indicated (n = 3; error bars are too small to be visible). (B) Transcription of sapS during growth in TSB. Cells of SA564 were cultured at 37 • C and 225 rpm in a culture to flask volume of 1:10, and after 3 h, 5 h, and 8 h of growth, cells were harvested, total RNAs isolated, and qRT-PCRs performed for sapS. Transcripts were quantified in reference to gyrase B mRNA.
Dams and reservoirs are among the most widespread human-made infrastructures on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e. g. , GlObal geOreferenced Database of Dams, GOODD) or detailed attributes for a limited dam quantity or region (e. g. , GRanD (Global Reservoir and Dam database) and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD), maintained by the International Commission on Large Dams (ICOLD), documents nearly 60 000 dams with an extensive suite of attributes. Unfortunately, the WRD records provide no geographic coordinates, limiting the benefits of their attributes for spatially explicit applications.
To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dams And Reservoirs (GeoDAR) dataset, created by utilizing the Google Maps geocoding application programming interface (API) and multi-source inventories. We release GeoDAR in two successive versions (v1. 0 and v1. 1) at https://doi. org/10. 5281/zenodo. 6163413 (Wang et al. , 2022). GeoDAR v1. 0 holds 22 560 dam points georeferenced from the WRD, whereas v1. 1 consists of (a) 24 783 dam points after a harmonization between GeoDAR v1. 0 and GRanD v1. 3 and (b) 21 515 reservoir polygons retrieved from high-resolution water masks based on a one-to-one relationship between dams and reservoirs. Due to geocoding challenges, GeoDAR spatially resolved ∼ 40 % of the records in the WRD, which, however, comprise over 90 % of the total reservoir area, catchment area, and reservoir storage capacity.
, 2009; Vörösmarty et al. , 2003; Wang et al. , 2017). Despite such environmental and societal significance, our spatial inventory of global dams and reservoirs, even for the large ones (such as those with a surface area > 1 km 2 ), has been insufficient. We still lack a thorough and authoritative dataset that documents both geographic coordinates (latitude and longitude) and standard attributes (e. g. , purpose, reservoir storage capacity, and hydropower capacity) of the existing large dams. One of the most comprehensive datasets, the World Register of Dams (WRD), is regularly updated by the International Commission on Large Dams (ICOLD; https://www. icold-cigb. org, last access: 13 March 2019), a non-governmental organization dedicated to the global sharing of professional dam or reservoir information.
This dilemma may be partially resolved by using georeferenced regional registers such as the United States National Inventory of Dams (US NID; https://nid. sec. usace. army. mil, last access: 20 March 2021). Nevertheless, such regional registers are not always publicly available, especially in developing nations, where dam construction is still booming  (Zarfl et al. , 2015). Other global dam and reservoir datasets that are georeferenced, however, often lack essential attributes. An example is the recently published GlObal geOreferenced Database of Dams (GOODD V1)  (Mulligan et al. , 2020) , which contains 38 667 dam points digitized from Google Earth imagery and their associated catchments delineated from digital elevation models (DEMs). Despite this dam quantity, GOODD provides no other attribute information.
Another inventory, the Global River Obstruction Database (GROD)  (Whittemore et al. , 2020; Yang et al. , 2022) , located more than 30 500 flow obstructions along rivers wider than 30 m as mapped in the Global River Width from Landsat (GRWL) database  (Allen and Pavelsky, 2018). The current attributes are mainly limited to obstruction types such as locks, weirs, and multiple types of dams. In addition, GRWL was tailored for the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission, which was designed to observe river reaches wider than 50-100 m  (Biancamaria et al. , 2016). While these rivers are sufficiently captured by GRWL, the obstruction infrastructure identified along the river mask in GRWL excludes many large dams on rivers narrower than 30 m. In the US, for instance, there are about 9020 NID-registered large dams according to ICOLD criteria, but only ∼ 9 % of them intersect with GRWL (calculated with variable distance tolerance being the maximum river widths (attribute "width_max") of the GRWL lines).
Among the few global dam or reservoir datasets that provide both georeferenced locations and essential attributes are the United Nations Food and Agricultural Organization (FAO) AQUASTAT  (Li et al. , 2011)  and the Global Reservoir and Dam database (GRanD)  (Lehner et al. , 2011). GRanD was constructed by harmonizing AQUASTAT and a wide range of regional gazetteers and inventories. Its latest version, v1. 3, contains 7320 dams as well as their reservoir boundaries and over 50 attributes, with a cumulative storage capacity of 6881 km 3. Since its publication, GRanD has been applied extensively by a variety of studies, although its focus is on the world's largest dams (e. g. , > 0. 1 km 3 ), and its quantity (7320 dams) is a fraction of the ∼ 59 000 dams documented in the WRD. A spatially resolved inclusion of additional large dams, such as those in compliance with the ICOLD definition, has been increasingly desired by the hydrology community and encouraged by growing collaborations from multiple disciplines such as biogeochemistry, ecology, energy planning, and infrastructure management  (Belletti et al.
, 2020; Boulange et al. , 2021; Grill et al. , 2019; Lin et al. , 2019; Wada et al. , 2017). Here, we present the initial versions of the Georeferenced global Dams And Reservoirs dataset, or GeoDAR. We built GeoDAR by leveraging multi-source dam and reservoir inventories and the Google Maps geocoding application programming interface (API). Our goal is to tackle the limitations of existing datasets by offering a dam inventory that is both spatially resolved and has an extended ability to access important attributes. As summarized in Table  1 , GeoDAR includes two successive versions. GeoDAR v1. 0 is essentially a georeferenced subset of the ICOLD WRD. It contains 22 560 dam points, each indexed by an identifier (ID) that is associated with a unique WRD record, allowing for potential retrieval of all its 40+ proprietary attributes from ICOLD.
(2017). These processes resulted in GeoDAR v1. 0, a total of 22 560 georeferenced WRD dam points with an accumulative storage capacity of 6441 km 3 (accounting for more than 80 % of that in the ICOLD WRD). The Venn diagram in Fig. 2a  provides an overview of the logical relations among the georeferencing sources and methods for GeoDAR v1. 0. To further improve our spatial inventory of the world's largest dams, we performed a harmonization between the  cess is illustrated by the Venn diagram in Fig. 2b. Finally, the reservoir polygons for each of the georeferenced dams were retrieved as thoroughly as possible from three global water body datasets: GRanD v1. 3 reservoirs  (Lehner et al. , 2011) , HydroLAKES v1. 0  (Messager et al. , 2016) , and the Landsat-based UCLA Circa 2015 Lake Inventory  (Sheng et al.
Besides spatial coordinates, each of these registers also provides attributes for their documented dams, which were required by the geo-matching process. While other dam inventories could be available, our geo-matching effort for GeoDAR v1. 0 was focused on these collected ones. However, we referred to additional registers or inventories from China, India, and Japan (Table  2 ) for the validation of our WRD geocoding (see "Validation"). For these additional regional registers, it was either inconvenient to bulkdownload the dam records, or we were legally restricted from releasing their dam coordinates. Therefore, we only used these registers for the purpose of validation. The procedure of geo-matching is illustrated in Fig. 3. Given each regional register, our goal was to find its matching records from the subset of the ICOLD WRD for the same region, by cross-checking value similarities for several key attributes between the two datasets.
On one hand, the compared attributes must be mutually available in both datasets. On the other hand, the attributes should cover various themes so that in combination, they are able to disambiguate records that represent different dams but may coincide in certain attributes. Taking both requirements into account, the key attributes used include the dam and reservoir names, multiple levels of administrative or political divisions for the dam, and the dam's completion year. The river on which the dam was constructed was also considered for all regions except Cambodia as the register does not contain such an attribute. For each of the key attributes, we considered values in the WRD and the regional register agreeing with each other if the similarity score between the value sequences exceeded ∼ 85 % (meaning that there are more than eight pairs of identical elements, with consideration of their orders, between two 10character sequences).
In princi-ple, high confidence would require a unanimous agreement on all key attributes. However, this ideal scenario was often unnecessary and sometimes impossible. One of the reasons is that the key attributes do not always have valid values. In the WRD, for instance, the values of "nearest town" for nearly all (> 99 %) US dams are missing. While this attribute is available for many other dams, the nearest town/city is not necessarily the division administrating or containing the dam. Another reason is that our collected multi-source datasets were not collated by a universal standard. As a result, inherent discrepancies of the attribute definitions and/or values may exist among the datasets. One example is the dam's "completion year", which could be ambiguous between the year when the dam construction was concluded and the year when the dam operation was initiated or commissioned.
This led to a conservative success rate in our automated geo-matching. More technical details about QA are given in our Python scripts at https://github. com/surf-hydro/ georeferencing-ICOLD-dams-and-reservoirs (last access: 13 March 2021). Following the automated QA, we performed a manual QC to reassure the accuracy of the geo-matching results. We went through each geo-matched WRD record to examine whether its attributes (e. g. , dam or reservoir name, administrative locations, river name, construction year, and storage capacity) indeed agreed with those of the regional source. If an evident discrepancy was identified, the "match" was removed or corrected in the final product. Although we made every endeavor to be as rigorous as possible, remnant matching errors may still exist due to the challenges of incompleteness and possible errors in the attribute information (refer to Sect.
In addition, some of the outputs, regardless of agreement on the address components, are not dams or reservoirs. We therefore included another baseline filter which aimed to remove such errors by analyzing the feature type information in the geocoding output (see scripts in "Code availability"). Although the QA process was designed to be automated, we still manually enforced hundreds of the initial outputs, many of which had returned feature names in native languages, to pass the baseline filters. As a result, our QA process yielded more than 16 000 geocoded WRD records, each with the optimal spatial coordinates and the corresponding QA rank. To complement the QA process, we then conducted a rigorous QC to correct and/or remove the remaining geocoding errors. We considered a geocoding error to be a location where (a) no dam or reservoir could be visibly verified from Google Earth or Esri images, or (b) the WRD attribute information is inconsistent with the feature or division labels on Google Maps.
In such cases, we usually first attempted to re-geocode the dam manually (such as by directly using the Google Maps interface) before deleting this error. It is important to clarify that the georeferenced coordinates, although referred to as "dam points" in our data product, do not always fall on the dam bodies. While the geo-matched coordinates from regional registers are usually on or close to the dams, the geocoded coordinates, depending on the address input (Table  S2  in the Supplement) and the available Google Maps information, could be located on the associated reservoir. Note that the latter case was not considered an error, but for improved locations, we manually adjusted some of the georeferenced coordinates more towards the dams. Due to China's GPS shift problem (e. g. , misalignment between the street maps and satellite imagery on Google Maps), the geocoded points across mainland China often exhibit system-atic offsets of roughly 500 m or more from their actual dam or reservoir features.
Supplementation with other global inventories The outputs from both geo-matching and geocoding, a total of 22 528 georeferenced ICOLD WRD records (Fig. 2a ), were further supplemented or harmonized by two global dam or reservoir inventories to improve our inclusion of the world's largest dams. We considered this process necessary for two reasons. First, our georeferencing process, particularly geocoding via Google Maps API, did not warrant an exhaustive inclusion of the largest dams. This is particularly evident for regions where the address and label information in Google Maps is either lacking or difficult to pass the automated QA due to language ambiguity or naming discrepancies. Second, through cross-referencing we noted that the attribute values of reservoir storage capacity (as well as reservoir surface area) provided in the ICOLD WRD are occasionally erroneous (also noted by  Mulligan et al.
(2017)  to double-assure and improve their spatial accuracies. This supplementation is illustrated by the Venn diagram in Fig. 2a. We next compared the storage capacities of each of the 133 dams in  Wada et al. (2017)  with those in the WRD and identified 21 of them exhibiting substantial discrepancies between the two datasets (including 3 dams without capacity values in the WRD). We then collated their storage capacities with other documents (e. g. , regional inventories, GRanD, and Wikipedia) and concluded that WRD may supersede  Wada et al. (2017)  in the accuracy of storage capacity for 5 of the 21 dams. Except these five dams, the original WRD capacities were replaced by those in  Wada et al. (2017). More detailed data collation and verification for  Wada et al. (2017)  are given in Table  S4  in the Supplement (full spreadsheet available at https://doi.
org/10. 5281/zenodo. 6163413; Wang et al. , 2022). The entire supplementation process, including adding new dams, updating existing dam coordinates, and correcting reservoir storage capacities, increased the total storage capacity of our georeferenced dams by 15 %, and 70 % of the capacity increase comes from the 32 added large dams. For improved clarity, it is worth reiterating that all dams supplemented by  Wada et al. (2017)  were also documented in the ICOLD WRD. The combined results of geomatching and geocoding, after the supplementation from  Wada et al. (2017) , define GeoDAR v1. 0, which contains 22 560 georeferenced records in the ICOLD WRD.
Harmonization with GRanD: forming GeoDAR v1. 1 While GeoDAR v1. 0 largely exceeds GRanD in dam count, a visual comparison of their spatial distributions revealed that the latter is often complementary to (instead of completely duplicated by) the former in many regions of the world. This motivated us to perform a systematic harmonization between the two datasets. The merged version, which we entitled Geo-DAR v1. 1, combines the merits of GRanD in documenting the world's largest dams and GeoDAR v1. 0 in providing extensive spatial details of smaller but more widespread dams. We assumed that GRanD, by having collated multiple data sources, is superior to GeoDAR v1. 0 in the accuracies of both spatial locations and attribute values (particularly reservoir storage capacity) of the world's largest dams.
While this may be true for most cases, we identified at least 88 dams in GRanD with possible location errors. With the help of several references such as regional registers (Table  2 ), the recently published Dataset of Georeferenced Dams in South America (DDSA)  (Paredes-Beltran et al. , 2021) , Google Maps, and other literature and openaccess documents, we were able to correct the locations of 76 of these dams and absorbed the corrected coordinates to the harmonization. The other 12 GRanD dams, including 3 duplicates with other dams and 9 we were unable to correct the locations for, were excluded from the harmonization. What was also excluded are another five dams in GranD that were subsumed or replaced by newer dams. For user convenience, we released these ∼ 90 GranD dams together with https://doi.
org/10. 5194/essd-14-1869-2022 Earth Syst. Sci. Data, 14, 1869-1899, 2022 the identified issues and suggested coordinates (if possible) in Table  S5  in the Supplement (full spreadsheet available at https://doi. org/10. 5281/zenodo. 6163413; Wang et al. , 2022). Using the adjusted GRanD data (7303 points), the harmonization (Fig. 5 ) aimed at (a) improving spatial coordinates of the dam points in GeoDAR v1. 0, (b) adding WRD dams that are not georeferenced in GeoDAR v1. 0 but are included by GRanD, (c) reducing storage capacity errors in the georeferenced WRD, and (d) absorbing the remaining GRanD dams that are not documented in the WRD. Detailed processing for each of the objectives is given below. First, when a dam in GeoDAR v1. 0 also exists in GRanD, the spatial coordinates of the former were replaced by those of the latter.
0. A distance tolerance of ∼ 5 km was applied to assist the spatial association and account for possible offsets in Geo-DAR v1. 0. As part of the QC, the attribute values of each pair (one from GRanD and the other from the WRD) were manually compared to determine whether they are indeed the same dam. This step identified another 400 or so overlapping dams between the two datasets. In total, we found that Geo-DAR v1. 0 overlaps 5080 out of the 7303 dams in GRanD, and their spatial coordinates were updated to be consistent with those in GRanD. Second, for the remaining 2223 dams in GRanD that do not overlap GeoDAR v1. 0, we assumed that at least part of them could be matched to the WRD records not georeferenced in GeoDAR v1. 0. Therefore, we performed another round of attribute association between the remaining subsets of GRanD and the WRD.