dataesr/scientific-paragraphs-categorization huggingface dataset:
Externally sourced ancillary data to gap-fill GEA, UITP and WB We obtained external ancillary data denoted as D OTHERS to fill gaps in D GEA+ and to add new attributes related to emissions. Note that D OTHERS data were not used to replace any datathe names of these independent, updated or new attributes were tagged with an 'others' label and added alongside existing attributes (themselves tagged with their dataset of origin). City population and area from multiple sources were obtained for all cities in D emissions. Diesel and gasoline prices provided in D GEA+ for year 2009  21  were updated to 2014 values  22  for all the cities in D emissions. Average household size was added for 119 out of 139 cities in D emissions that did not have this attribute from D GEA+ , and 48 D GEA+ values were updated.
CDD and HDD climate indices Although D GEA+ had CDD and HDD indices for some cities, these indices were recomputed (D clim ) from a global harmonized gridded climate dataset  25   To summarize the coverage of the different data contained in D final , Figure  4  shows the number of cities per geographic group, per primary emission calculation methodology/emission reporting protocol, per years of emissions estimate, and per nominal GDP class where classes ranges are [0-100], (100-200], (200-300], (300-400], > 400 $BN. Figures 5 shows Scope-1 GHG emissions per capita in eight geographic regions, sorted in descending order according to Scope-1/capita for the ten highest per capita emission values. The new dataset D final covers emissions and ancillary data in a wide range of countries and 83 cities in China, allowing data to be analyzed for differences between cities worldwide (e.
D CDP dataset Scope-1 CO 2 emissions separated as described in Methods from total GHG emissions reported by cities in D CDP were estimated between 1990-2016, with 88% of the data being in the period 2010-2015. Some cities also reported change in their emissions between the most recent and previous reporting periods, explanations for this change, methodology details, and gases included, as well as ancillary data such as population, boundary definition, land area, mean annual temperature, mean altitude, and Gross Domestic Product (GDP). The following records were obtained from D CDP.
'Methodology details (CDP)' [string] Some cities provided comments relating to how they estimated their emissions within the specified protocol. 'Increase/Decrease from last year (CDP)' [string] Change in emissions based on previous reporting year. The majority (88 [47%]) of cities reduced their emissions with respect to the previous reporting year. Emissions increased for 36 (19%) cities, and stayed the same for 12 (6%) cities. Of the remaining cities, 24 (13%) cities were reporting their emissions for the first time in 2016 (thus had no previous comparison year), 7 (4%) cities reported "other" to indicate that their emission data was not directly comparable to previous estimates due to protocol differences, and 20 cities (11%) did not specify. 'Reason for increase/decrease in emissions (CDP)' [string] The interested reader may refer to the original D CDP2016 dataset to explore the reasons provided for changes in emissions.
D carbonn dataset Scope-1 GHG emissions from 73 cities selected in D carbonn (see Methods) were estimated for years in the period 1994-2017, with 78% of the data in 2010-2015. Emissions correspond to all activities occurring throughout the local government's entire geographic area (http://e-lib. iclei. org/wp-content/uploads/ 2015/12/cCR2015_5Year_Report. pdf). Cities also reported ancillary data such as city type, population, GDP and nominal GDP. The following records were obtained from D carbonn.
'Gasoline price (GEA+) [USD/liter]' [float] November 2008 gasoline price (national values)  21. In the final consolidated dataset, these values were not used; updated 2014 values  22  were used instead (see Methods Step 4). Fuel prices for two cities (Rio de Janeiro and Sao Paulo) in D UITP were anomalous compared with the corresponding D GEA and D WB values; cross-checking with the reference cited in the datasets revealed that these D UITP values were incorrect and they were therefore removed.
'Household size (GEA+) [people/household]' [float] Average number of persons per household, obtained from multiple sources, available for 48 of the 187 cities in D CDP. The following discrepancies/uncertainties were noted for household size in D GEA+ : Sydney: The 2011 household size reported in D GEA+ corresponds to the population of the Greater Sydney Area, whereas the corresponding city population in D emissions corresponds to the 'City of Sydney'. Stockholm: It could not be confirmed that household size in D GEA+ represents the corresponding city population in D emissions. Household size in D OTHERS was estimated by a manual calculation based on values from Statistics Sweden (http://www. statistikdatabasen. scb. se/pxweb/en/ssd/START__BE__-BE0101__BE0101S/HushallT03/?rxid=5b1086fd-d703-4c03-8791-3c76c740804f).
D IESE dataset Thirteen socio-economic indicators were obtained from D IESE : Economy, Environment, Governance, Human capital, International impact, Mobility and transportation, Public management, Social cohesion, Technology, Urban planning, the Cities in Motion index (CIMI), CIMI ranking, and CIMI performance. These indicators reflect the sustainability and standard of living of a city. The IESE indicators include both city and national level variables from several sources with objective measures (e. g. , GPD per capita) as well as subjective measure (e. g. , perception about traffic). The calculation of the synthetic indicators produces values that were standardized in order to note the differences amongst the various cities between positions more intuitively. In order to modify the scale of each city, the city with the highest score (first in the ranking) was assigned the number 100.
'Governance (IESE) [dimensionless]' [integer] The IESE governance index aims to reflect the effectiveness, quality and sound guidance of state intervention. This index takes into account five positively-contributing indicators: the strength of legal rights for borrowers and lenders, corruption perceptions index (0 = very corrupt to 100 = very transparent), number of functions of the city's innovation department, range of government Web services, and whether a city has an open data platform.
'HDD 15. 5 C (clim) [degrees C × days]' [integer] HDD calculated for a base temperature of 15. 5 °C from D clim at grid points closest to the lat/lon coordinates above, averaged over the same five-year period (2007-2011) as for the climate indices in D GEA+. HDD 15. 5 C is defined as the sum over one year of the difference between 15. 5 °C (base temperature) and the daily temperature, whenever the daily temperature is lower than the base temperature. We noted that our computed HDD 15. 5 C had poor correspondence (30-90%) with observationsbased HDD 15. 5 C in D GEA+ for 25 cities out of 56 cities with climate indices in D GEA+. Twelve cities are colder than predicted by the global model, and 5 of these cities are at high altitudes (>1000 m). The relatively course resolution of the ERA-interm dataset may not account for coastal and orographic effects  25  ; this could lead to the prediction of fewer colder days than for the same coordinates but at higher altitude.
UACs are selected based on the global human settlement (GHS) dataset (http://data. europa. eu/89h/jrcghsl-ghs_smod_pop_globe_r2016a). GHS is classified into high-and low-density and rural clusters, defined as: High-density clusters (HDCs) comprise "contiguous cells (4-cell connectivity, gap filling) with a density of at least 1,500 inhabitants/km 2 or a built-up density greater than 50%, and a minimum of 50,000 inhabitants per cluster"  30. Low-density clusters (LDCs) comprise "contiguous cells (4-connectivity, gap filling) with a density of at least 300 inhabitant/km 2 and minimum of 5,000 inhabitants per cluster"  30. Rural clusters include all cells outside high-and low-density clusters  30. From these areas, only the areas that intersect with one of the leftover city points are selected.
The results are the UACs for each city point. Importantly, some city points fell into the same UAC since these cities are close together and these settlements are contiguously connected. There are up to 9 city points in one cluster, as in e. g. the San Francisco bay area. In these cases, the standard buffer process was used nonetheless, including neighbouring urban areas, for methodological consistency. There are 343 city points but only 282 urban area clusters. Classification of built-up areas into low-or high-density areas. Built-up intensity changes for each UAC were calculated based on built-up area raster grids (GHG BUILT-UP_GRID) (http://data. europa. eu/89h/jrc-ghsl-ghs_built_ldsmt_globe_r2015b). Datasets were classified into 'low' and 'high' for values between 0-25% and >25% of the built-up area (BUA), respectively, for the years 1990, 2000 and 2014.
e. emitting activities included in the accounting for Scope-1. A large source of errors between city emission estimates is the use of distinct systems boundaries (e. g. counting or not cement manufacturing, industrial sites, small power plants, biofuels). Once these inconsistencies are corrected, the between-estimates uncertainty could be reduced. A detailed evaluation against independent estimates is arguably the best practical way to assess uncertainties when looking at a multiple-cities dataset. This is the approach we followed in this section. The VULCAN dataset from Gurney et al. 42  provides U. S. fossil fuel CO 2 emissions for the year 2002 on a 10 × 10 km grid at the level of fuel type, economic sub-sector, and county/state identification over the entire US landscape. VULCAN includes individual factories, power plants, roadways and neighborhoods.
To compare with our Scope-1 values for US cities, we considered two scenarios: 1) summed VULCAN emissions from all the sectors of aircraft, cement, commercial, industrial, non-road, on-road, residential, and electricity production and 2) we removed from the sum of all-sectors the emissions from electricity production. Emissions at the location of 64 US cities in D final (63 from D CDP and 1 from D carbonn ) were collocated with the gridded VULCAN data, assuming that each city is approximately a circle of area corresponding to the reported administrative area in our dataset. Scope-1 emissions and Scope-1 emission density (emissions/area) in D final were regressed against the two corresponding VULCAN scenarios including and excluding emissions from electricity production. The correlation coefficient (R 2 ) when electricity production emissions were included was 0.
5. Validation of Scope-1 CO 2 emissions per capita against Markolf et al. The second dataset against which we compared Scope-1 CO 2 emissions in D final for US cities is described in Markolf et al. 43. The authors compiled publicly available national datasets for estimating emissions in the 100 most populated metropolitan areas in the US in 2014. Twenty-eight cities in Markolf et al. overlapped with D final. Markolf et al. documented separately CO 2 emissions from on-road transportation, electricity generation, industrial processes, residential buildings, commercial buildings and waste. Then, the emissions were divided by population because the metropolitan areas in Markolf et al. generally encompass a wider territory than in D final. The ratio between per capita Scope-1 emissions of Markolf et al.
We note that some of the inventory data used by Markolf et al. may be common with D CDP and D carbonn , thus Markolf et al. cannot be proven to be strictly independent from our data. The third dataset against which we compared D final Scope-1 CO 2 emissions was developed for all municipalities in Japan and described in Nakamichi et al. 44  The authors computed direct CO 2 emissions using a bottom-up (built-up) approach based on emission factors and other statistics attributed to four civilian categories: residential, commercial, industrial (including electricity production) and transportation, as well as the total integrated direct emissions. The year of analysis was 2005 for all categories. Six cities in Nakamichi et al. overlapped with D final. The ratio between Scope-1 emissions of Nakamichi et al.
sampled for the grid-cell containing each city. D clim contains HDD for a base temperature (T b ) of 15. 5 °C and CDD for a base temperature (T b ) of 23 °C. The dataset containing D emissions merged with all ancillary datasets (D GEA+ , D INRIX , D TomTom , D IESE , D clim , D UEX , and D OTHERS ) is denoted D final and has dimensions of 343 rows (cites) × 179 columns (first column after city name and definitions = Scope-1 GHG emissions, others = Scope-2 emissions, ancillary data, and quality flags). The data is publicly available on PANGAEA (Data Citation 2).
(GEA+) [degrees C × days]' [integer]HDD values for a base temperature of 15. 5 °C obtained from the weather station specified in the 'weather_station_id' attribute of D GEA+. HDD values across D GEA , D UITP and D WB were the same for all cities except Sao Paulo in D UITP , which disagreed with the values in the other two datasets. The D UITP values were considered anomalous and were removed. No HDD data were available for one city (Brasília) in D GEA+. 'CDD 23 C (GEA+) [degrees C × days]' [integer]CDD values for a base temperature of 23 °C obtained from the weather station specified in the 'weather_station_id' attribute of D GEA+. CDD values across D GEA , D UITP and D WB were the same for all cities except Sao Paulo in D UITP , which disagreed with the values in the other two datasets. The D UITP values were considered anomalous and were removed.
Changes in population density (population intensification/sprawl). Population densities are extracted (zonal statistics) within the classified built-up areas, high and low, for the 1990 baseline. Changes are captured from 1990-2000 and from 2000-2015. Note that the population raster dataset (GPWv4) (http://data. europa. eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a) depicts the 2015 population, whereas the latest built-up area layer is from 2014. The 18 attributes obtained from D UEX for 282 urban clusters corresponding to the 343 cities in D final are described below:'Urban area name (UEX)' [string]Name of the urban area cluster. 'Low BUA -year (UEX) [km2]' [integer]Low Built-up Area (BUA) for year = 1990, 2000, and 2014; three attributes in total. 'High BUA -year (UEX) [km2]' [integer]High BUA for year = 1990, 2000, and 2014; three attributes in total.
Datasets and their corresponding method steps. Datasets D*, their description and the Method Section steps where they are handled/created. Separate CO 2 emissions from non-CO 2 GHGs Since emissions of different greenhouse gases (GHGs) are included under Scope-1 emissions, we separated the fraction of emissions from fossil fuel and cement CO 2 sources from other GHG contributions in D CDP. In total, 44 cities (25%) reported only emissions from fossil fuel and cement CO 2 , 6 cities did not specify the GHGs covered, 4 reported CO 2 and CH 4 , and 133 included all GHGs, that is, CO 2 , CH 4 , N 2 O, HFCs, PFCs, SF 6 , and/or NF 3 (see Table.
19see Fig. 1): GEA19(225 cities, henceforth D GEA ), UITP (https://trid. trb. org/view/708144) (87 cities, henceforth D UITP ), and WB 20 data (26 cities, henceforth D WB ). These datasets contain energy-use, other socio-economic and climate-related attributes. We integrated the GEA, UITP and WB datasets that comprise 273 cities into D GEA+ , but this dataset only matched emissions from 57 cities from D CDP , 25 from D carbonn and 35 from D PKU. In D GEA+ , duplicate identical values of the same attribute in D GEA , D UITP and D WB were merged into one common column tagged with label 'GEA+'. If the values were different, they were investigated further to resolve discrepancies (e. g. data entry errors) or left as is if the values were obtained from different sources; any such discrepancies are noted in the Data Records.
Acknowledgements We acknowledge the contribution of  Olivier Boucher  ( Laboratoire de Météorologie Dynamique, UPMC/ CNRS, Paris, France ) who calculated the climate indices over the global grid. We thank  Takahiro Yoshida  ( National Institute for Environmental Studies, Tsukuba, Japan ) for providing ancillary data for eight cities in Japan. The project was enabled by, and C. N. and A. P. were funded by, a donation of the  Climate Initiative  from the  BNP Paribas foundation  (https://group. bnpparibas/en/group/bnp-paribas-foundation/environment). This effort is part the  Global Carbon  Project (www. globalcarbonproject. org) work on cities and carbon.
Code availability The Jupyter notebooks (IPython version 5. 1. 0) used in this section are publicly archived with the final dataset on PANGAEA (Data Citation 2), along with the necessary input files (D GEA+ , D INRIX , D TomTom , D IESE , D clim , D UEX , and D OTHERS ). The notebooks import the following modules: pandas (version 0. 22. 0), numpy (version 1. 14. 0), matplotlib (version 2. 1. 2), and csv (version 1. 0).
'HDD 15. 5 C 155 (GEA+) [degrees C × days]' [integer]HDD values for a base temperature of 15. 5 °C obtained from the weather station specified in the 'weather_station_id' attribute of D GEA+. HDD values across D GEA , D UITP and D WB were the same for all cities except Sao Paulo in D UITP , which disagreed with the values in the other two datasets. The D UITP values were considered anomalous and were removed. No HDD data were available for one city (Brasília) in D GEA+. 'CDD 23 C (GEA+) [degrees C × days]' [integer]CDD values for a base temperature of 23 °C obtained from the weather station specified in the 'weather_station_id' attribute of D GEA+. CDD values across D GEA , D UITP and D WB were the same for all cities except Sao Paulo in D UITP , which disagreed with the values in the other two datasets.
Step 1 D CH4 Previously created dataset containing values for estimating CO2 from CO2-eq. Step 2 D CDP Estimation of CO 2 emissions from CO 2-eq Step 2 D carbonn Scope-1 emissions from carbonn Step 3 D PKU Scope-1 emissions from the Chinese dataset Step 3 D GEA , D UITP , D WB = > D GEA+ Published ancillary data from GEA, UITP, and WB, respectively Step 4 D OTHERS Other ancillary data obtained from external sources Step 4 D INRIX Previously-prepared INRIX traffic-related data Step 4 D TomTom Previously-prepared TomTom traffic-related data Step 4 D IESE Previously-prepared IESE socio-economic indicators Step 4 D UEX Calculated urban expansion data Step 4 D clim Previously-calculated climate indices Step 4 D final Final dataset Figure 2. Flow chart of the steps in Methods to produce final dataset D final.
1C ; Table  S1 ). These data were generated from quadruplicate assays followed by mass spectrometry. We concluded that 130 T. gondii proteins could potentially bind to CHOK-1 Golgi-enriched membranes, whereas only 25 T. gondii proteins could bind to CHOK-1 ER-enriched membranes. Thirty-two T. gondii proteins that commonly bound to both Golgi-enriched membranes and ER-enriched membrane were not present in RH lysate alone (Fig. 1C ; Table  S1 ); accordingly, these latter proteins were not investigated further. To identify proteins of special interest, we applied the following criteria: (1) selected proteins must have been identified in RH lysate and in CHOK-1 Golgi-enriched membrane (Golgi)-binding assays at least twice in four independent experiments; (2) they must have been identified at most twice in CHOK-1-enriched ER (ER)binding assays; and (3) proteins that were identified twice in both CHOK-1 Golgi-enriched membrane (Golgi)-and CHOK-1enriched ER (ER)-binding assays were excluded.
Ultrastructural microscopy revealed that intracellular ΔGRA3 mutants contained large vesicles and exhibited abnormal morphology (Fig. 3D ) relative to intracellular parental parasites (Fig. 3D ), which had normal PV tubular networks (dashed square, magnified square). The data suggest that TgGRA3 may be required for formation of normal tubules at the PVM. (A) Z-stack confocal images of HFF cells infected with the RHΔKU80 parental strain for 30 h. The host Golgi marker giantin (gray) and TgGRA3 (green) were visualized using specific antibodies, and nuclei were stained with DAPI. The white dashed square corresponds to a magnified region. The gamma value was changed to 0. 75 in the magnified image to better visualize the TgGRA3-labeled structures. Scale bars: 10 µm (2 µm for magnified regions).
For MS/MS, an isolation window of 2 m/z was used. The ten most intense peaks (TopN) with charge state between 2 and 4 were selected for fragmentation in the higher-energy collisional induced dissociation (HCD) cell, with normalized collision energy of 27. The tandem mass spectra were acquired over the m/z 200-2000 range in the Orbitrap mass analyzer with resolution 35,000 at m/z 200 and an AGC of 5×10 4. The ion intensity selection threshold was 1. 7×10 4 , and the maximum injection time was 150 ms. Dynamic exclusion was set to 20 s (15 s for a long gradient). The total run time was 60 min for a short gradient and 120 min for a long gradient. All systems were fully controlled by Thermo Xcalibur 3. 0 (Thermo Fisher Scientific). For protein identification, all data files (*. raw) collected during nanoLC-MS/MS analyses were processed with a specific workflow designed in Proteome Discoverer 1.
4 (Thermo Fisher Scientific) as described  (Sangaré et al. , 2016). Searches with a mass tolerance of 10 ppm for precursor ions and 0. 02 Da for fragment ions were performed against two different databases corresponding to the sample: T. gondii protein sequences were downloaded from www. toxodb. org on December 11, 2014 (18,954 entries), and Cricetulus griseus protein sequences (34,841 entries) were downloaded from Universal Protein Resource (UniProt) on September 29, 2014. The target-decoy database search allowed the estimation of the false-positive identification rate of the present study  (Elias and Gygi, 2007). The final catalogue of proteins has an estimated false-positive rate <1%.
Bioinformatics and modeling All histograms were plotted with GraphPad version 6 (GraphPad Prism, San Diego, USA). Surface three-dimensional reconstructions, volume measurement and quantification of co-localization were performed with Imaris version 8, Imaris XT, Bitplane Inc. (USA). Confocal images were processed using Carl Zeiss Zen software. AutoQuant X3 Version 3. 0. 5. software was used to perform deconvolution of imaging data. Helical wheel plots were generated using software available online (http://kael. net/helical. htm). Venn diagrams were created using software available online (http://bioinformatics. psb. ugent. be/ webtools/Venn/). Coiled-coil predictions were generated by COILS  (Lupas et al. , 1991). Theoretical modeling of GRA3 protein structure was performed by HHpred and SWISS-MODEL.
Acknowledgments We thank  Dr Suzanne Pfeffer  ( University of Stanford, USA ) for the VSVG-ts045-YFP plasmid construct and the GCC185 and VSVG antibodies; and  Etienne Dewailly  ( University of Lille, France ) for electron microscopy. We also thank  Drs Ryan Nottingham  ( Stanford University, USA ) and  Michael R. Ziemba  ( Binghamton University, USA ) for critical reading of this manuscript. We are grateful to the  Bio Imaging Center (Lille Nord France)  for the use of their confocal microscopes.