New York City school level College Board SAT results for the graduating seniors of 2010. Records contain 2010 College-bound seniors mean SAT scores. Records with 5 or fewer students are suppressed (marked ‘s’). College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010. Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report. Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school. Data collected and processed by the College Board.

The most recent school level results for New York City on the SAT. Results are available at the school level for the graduating seniors of 2012. Records contain 2012 College-bound seniors mean SAT scores taken during SY 2012.

Analysis of ‘California SAT Reports’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/california-sat-report-1998-1999e on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Data were aggregated to produce school, district, county, and state-level reports.

Background

This document is meant to accompany the SAT Report. The California Department of Education (CDE) receives SAT data at the student level from the College Board. The CDE then aggregates the data by school, district, county, and state in order to produce the SAT Report. The SAT Report includes only California public schools with one or more students enrolled in grade twelve according to data submitted by local educational agencies (LEAs) and maintained in the California Basic Educational Data System (CBEDS). Scores for schools that had fewer than eleven students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.

Link to Glossary

What Is the SAT?

Description of the SAT test program developed by the College Board.The SAT Reasoning Test is a standardized test that assesses the critical reading, mathematics, and writing skills that students need to be successful in college. Each of the three sections that comprise the SAT Reasoning Test has a possible score of 800 points. Prior to 2005, the SAT test included only two sections, the verbal section (now referred to as the critical reading section) and the math section, each having possible scores of 800 points. SAT test results represent one factor considered by many colleges and universities in making admissions decisions.

The SAT is owned, published, and developed by the College Board, a non-profit organization in the United States. The California Department of Education does not have results that identify individual students. Please direct questions about individual scores, including requests for transcripts, to the College Board.

Questions: SAT, ACT, AP Test Results Team | SATACTAP@cde.ca.gov | 916-319-0869

Last Reviewed: Thursday, October 20, 2016

Record Layout for SAT Test Results 2015‰ÛÒ16

Provides field definitions for standardized test results from SAT Test.

Field #	Field Name	Type	Width	Description
1	CDS	Character	14	County/District/School code
2	RTYPE	Character	1	Record Type: C=County, D=District, S=School, X=State
3	SNAME	Character	50	School Name
4	DNAME	Character	50	District Name
5	CNAME	Character	15	County Name
6	ENROLL12	Character	7	Enrollment of Grade 12
7	NUMTSTTAKR	Character	7	Number of Test Takers
8	AVGSCRREAD	Character	3	Average of SAT Score for Critical Reading, * = Scores for schools that had fewer than 15 students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.
9	AVGSCRMATH	Character	3	Average of SAT Score for Math, * = Scores for schools that had fewer than 15 students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.
10	AVGSCRWRITE	Character	3	Average of SAT Score for Writing, * = Scores for schools that had fewer than 15 students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.
11	NUMGE1500	Character	6	Number of Test Takers Whose Total SAT Scores Are Greater or Equal to 1500, * = Scores for schools that had fewer than 15 students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.
12	PCTGE1500	Character	5	Percent of Test Takers Whose Total SAT Scores Are Greater or Equal to 1500, * = Scores for schools that had fewer than 15 students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.

Questions: SAT, ACT, AP Test Results Team | SATACTAP@cde.ca.gov | 916-319-0869

Last Reviewed: Friday, June 16, 2017

Record Layout for SAT Test Results for 2013‰ÛÒ14 and 2014‰ÛÒ15

Provides field definitions for standardized test results from SAT Test.

Field #	Field Name	Type	Width	Description
1	CDS	Character	14	County/District/School code
2	RTYPE	Character	1	Record Type: C=County, D=District, S=School, X=State
3	SNAME	Character	50	School Name
4	DNAME	Character	50	District Name
5	CNAME	Character	15	County Name
6	ENROLL12	Character	7	Enrollment of Grade 12
7	NUMTSTTAKR	Character	7	Number of Test Takers
8	AVGSCRREAD	Character	3	Average of SAT Score for Critical Reading, * = Scores for schools that had fewer than 11 students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.
9	AVGSCRMATH	Character	3	Average of SAT Score for Math, * = Scores for schools that had fewer than 11 students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.
10	AVGSCRWRITE	Character	3	Average of SAT Score for Writing, * = Scores for schools that had fewer than 11 students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.
11	NUMGE1500	Character	6	Number of Test Takers Whose Total SAT Scores Are Greater or Equal to 1500, * = Scores for schools that had fewer than 11 students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.
12	PCTGE1500	Character	5	Percent of Test Takers Whose Total SAT Scores Are Greater or Equal to 1500, * = Scores for schools that had fewer than 11 students taking the SAT are not shown on the SAT Report in order to preserve the anonymity of the students.

Questions: SAT, ACT, AP Test Results Team | SATACTAP@cde.ca.gov | 916-319-0869

Last Reviewed: Tuesday, March 14, 2017

Source: http://www.cde.ca.gov/ds/sp/ai/

This dataset was created by Education and contains around 2000 samples along with Unnamed: 2, Unnamed: 10, technical information and other features such as: - Unnamed: 6 - Unnamed: 9 - and more.

How to use this dataset

• Analyze Unnamed: 12 in relation to Unnamed: 3
• Study the influence of Unnamed: 7 on Unnamed: 4
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Education

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--- Original source retains full ownership of the source dataset ---

Integrating spatially explicit biogeophysical and remotely sensed data into regression-tree models enables the spatial extrapolation of training data over large geographic spaces, enhancing a more complete understanding of broad-scale ecosystem processes. This data release presents maps of estimates of annual gross primary production (GPP) and annual ecosystem respiration (RE) that were derived from weekly summaries of gross photosynthesis (Pg) and ecosytem respiration (Re). To conduct this study we used carbon data from flux towers that are scattered strategically across the conterminous United States (CONUS). We also calculate and present a map of average annual net ecosystem production (NEP). We present and analyze carbon flux dynamics in the CONUS to better understand how drought affects GPP, RE, and NEP. Our study experienced correlation coefficients (r) greater than or equal to 0.94 between training and estimated data for both GPP and RE. We conclude that this modeling method effectively measures carbon dynamics through time and allows the exploration of impacts of meteorological anomalies and vegetation types on carbon dynamics.

The most recent school level results for New York City on the SAT. Results are available at the school level for the graduating seniors of 2012. Records contain 2012 College-bound seniors mean SAT scores taken during SY 2012.

United States Avg Hourly Earnings: IF: Wired & Wireless Telecom Carriers, Exc Sat data was reported at 44.080 USD in Mar 2025. This records a decrease from the previous number of 44.170 USD for Feb 2025. United States Avg Hourly Earnings: IF: Wired & Wireless Telecom Carriers, Exc Sat data is updated monthly, averaging 31.360 USD from Mar 2006 (Median) to Mar 2025, with 229 observations. The data reached an all-time high of 45.760 USD in Dec 2024 and a record low of 25.990 USD in Mar 2006. United States Avg Hourly Earnings: IF: Wired & Wireless Telecom Carriers, Exc Sat data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings.

Aiming for near-real-time data-quality monitoring of the Level-2 satellite sea surface salinity (SSS) products, the satellite oceanography team of NOAA National Centers for Environmental Information (NCEI) has generated quick-look 1.0-degree Level-3 experimental products from the NASA Aquarius/Satélite de Aplicaciones CientÃficas (SAC)-D satellites level-2 swath data. The datasets are comprised of two respective Level-3 monthly and 7-day mean datasets, one from the standard level-2 SSS data created by the NASA Goddard Space Flight Center's Aquarius Data Processing System (ADPS) and another from the one created by the Jet Propulsion Laboratory (JPL) based on the Combined Active-Passive (CAP) algorithm. The Level-2 swath data is interpolated onto a 1.0-degree grid using equal-weighted box (“bin†) averaging.

New York City school level College Board SAT results for the graduating seniors of 2010. Records contain 2010 College-bound seniors mean SAT scores. Records with 5 or fewer students are suppressed (marked ‘s’). College-bound seniors are those students that complete the SAT Questionnaire when they register for the SAT and identify that they will graduate from high school in a specific year. For example, the 2010 college-bound seniors are those students that self-reported they would graduate in 2010. Students are not required to complete the SAT Questionnaire in order to register for the SAT. Students who do not indicate which year they will graduate from high school will not be included in any college-bound senior report. Students are linked to schools by identifying which school they attend when registering for a College Board exam. A student is only included in a school’s report if he/she self-reports being enrolled at that school. Data collected and processed by the College Board.

This is a selection of the MODIS 8 day mean level 3 mapped 8 day mean ocean products at the two highest resolutions available. It spans from July 2002 onwards and will be augmented by newer data from time to time. The source of the data is NASA at the above reference. This data has been reformatted into 3 dimensional, longitude by latitude by time netcdf files for use by researchers in CSIRO. Initially the following products have been selected: chlorophyll, extinction coefficient (K490) and sea surface temperature. The NASA data are also available in various resolutions. The highest two resolution levels have been chosen, with nominal pixel size of 4 and 9 kilometres. A description of the processing carried out by NASA is at http://oceancolor.gsfc.nasa.gov/DOCS/modis_processing_overview.pdf

United States - Consumer Price Index for All Urban Consumers: Cable and Satellite Television Service in U.S. City Average was 595.20800 Index Dec 1983=100 in May of 2025, according to the United States Federal Reserve. Historically, United States - Consumer Price Index for All Urban Consumers: Cable and Satellite Television Service in U.S. City Average reached a record high of 599.39200 in February of 2025 and a record low of 180.80000 in January of 1992. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Consumer Price Index for All Urban Consumers: Cable and Satellite Television Service in U.S. City Average - last updated from the United States Federal Reserve on July of 2025.

United States Avg Hourly Earnings: IF: Wired & Wireless Telecom, Except Satellite data was reported at 43.780 USD in Mar 2025. This records a decrease from the previous number of 43.840 USD for Feb 2025. United States Avg Hourly Earnings: IF: Wired & Wireless Telecom, Except Satellite data is updated monthly, averaging 31.140 USD from Mar 2006 (Median) to Mar 2025, with 229 observations. The data reached an all-time high of 45.370 USD in Dec 2024 and a record low of 26.130 USD in Mar 2006. United States Avg Hourly Earnings: IF: Wired & Wireless Telecom, Except Satellite data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings.

The data sets of monthly mean surface air temperature (SAT) contain data from land stations. Several databases are consolidated. Historically, European countries have maintained good station coverage, providing the longest records of air temperature and pressure (see thumbnail). Meteorological observations over land regions of Russia and Alaska also have reasonable station coverage and length. A few long time records are also available from the Northwest Territories of Canada. All the land station data have been assessed for homogeneity using interstation comparison (see discussion of homogeneity assessment techniques in Jones et al. (1999)). Monthly data have also been assessed for errors by identifying peaks exceeding 3 standard deviations and then checking them with nearby station records. The WMO name of the station is used for the name of the file containing SAT station data. The file stn_list_sat contains information about each station such as the WMO number and name of the station, its latitude, longitude and elevation and years for which data are available. Data compiled by Igor Polyakov.

chlorophyll a concentration (in g.m-3)

United States AHE: PW: IF: TC: WW: Wired & Wireless Carriers excl Satellite (WC) data was reported at 32.650 USD in Nov 2024. This records a decrease from the previous number of 32.740 USD for Oct 2024. United States AHE: PW: IF: TC: WW: Wired & Wireless Carriers excl Satellite (WC) data is updated monthly, averaging 24.490 USD from Jan 1990 (Median) to Nov 2024, with 419 observations. The data reached an all-time high of 33.130 USD in Sep 2024 and a record low of 13.650 USD in Jan 1990. United States AHE: PW: IF: TC: WW: Wired & Wireless Carriers excl Satellite (WC) data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G075: Current Employment Statistics: Average Hourly Earnings: Production Workers.

mineral Suspended Particulate Matter (g.m-3)

United States Avg Weekly Earnings: IF: Wireless Telecom Carriers, Except Satellite data was reported at 1,593.990 USD in Mar 2025. This records an increase from the previous number of 1,562.830 USD for Feb 2025. United States Avg Weekly Earnings: IF: Wireless Telecom Carriers, Except Satellite data is updated monthly, averaging 1,104.380 USD from Mar 2006 (Median) to Mar 2025, with 229 observations. The data reached an all-time high of 1,621.050 USD in Oct 2024 and a record low of 844.160 USD in Jul 2009. United States Avg Weekly Earnings: IF: Wireless Telecom Carriers, Except Satellite data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Weekly Earnings.

United States Avg Hourly Earnings: IF: Wireless Telecom Carriers, Except Satellite data was reported at 40.050 USD in Mar 2025. This records an increase from the previous number of 39.970 USD for Feb 2025. United States Avg Hourly Earnings: IF: Wireless Telecom Carriers, Except Satellite data is updated monthly, averaging 30.510 USD from Mar 2006 (Median) to Mar 2025, with 229 observations. The data reached an all-time high of 40.730 USD in Oct 2024 and a record low of 23.410 USD in Dec 2009. United States Avg Hourly Earnings: IF: Wireless Telecom Carriers, Except Satellite data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings.

This dataset provides gridded daily and monthly mean global estimates of sea level anomaly based on satellite altimetry measurements. The rise in global mean sea level in recent decades has been one of the most important and well-known consequences of climate warming, putting a large fraction of the world population and economic infrastructure at greater risk of flooding. However, changes in the global average sea level mask regional variations that can be one order of magnitude larger. Therefore, it is essential to measure changes in sea level over the world’s oceans as accurately as possible. Sea level anomaly is defined as the height of water over the mean sea surface in a given time and region. In this dataset sea level anomalies are computed with respect to a twenty-year mean reference period (1993-2012) using up-to-date altimeter standards. In the past, the altimeter sea level datasets were distributed on the CNES AVISO altimetry portal until their production was taken over by the Copernicus Marine Environment Monitoring Service (CMEMS) and the Copernicus Climate Change Service (C3S) in 2015 and 2016 respectively. The sea level dataset provided here by C3S is climate-oriented, that is, dedicated to the monitoring of the long-term evolution of sea level and the analysis of the ocean/climate indicators, both requiring a homogeneous and stable sea level record. To achieve this, a steady two-satellite merged constellation is used at all time steps in the production system: one satellite serves as reference and ensures the long-term stability of the data record; the other satellite (which varies across the record) is used to improve accuracy, sample mesoscale processes and provide coverage at high latitudes. The C3S sea level dataset is used to produce Ocean Monitoring Indicators (e.g. global and regional mean sea level evolution), available in the CMEMS catalogue. The CMEMS sea level dataset has a more operational focus as it is dedicated to the retrieval of mesoscale signals in the context of ocean modeling and analysis of the ocean circulation on a global or regional scale. Such applications require the most accurate sea level estimates at each time step with the best spatial sampling of the ocean with all satellites available, with less emphasis on long-term stability and homogeneity. This dataset is updated three times a year with a delay of about 5 months relative to present time. This delay is mainly due to the timeliness of the input data, the centred processing temporal window and the validation process. However, these processing and validation steps are essential to enhance the stability and accuracy of the sea level products and make them suitable for climate applications. This dataset includes estimates of sea level anomaly and absolute dynamic topography together with the corresponding geostrophic velocities, which provide an approximation of the ocean surface currents. More details about these variables, the sea level retrieval algorithms, additional filters, optimisation procedures, and the error estimation can be found in the documentation.

Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, Geophysical Research Letters, https://doi.org/10.1002/2018GL077914

The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: https://doi.org/10.5281/zenodo.1219784

Abstract

Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.

Description of repository datasets:

1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:

• "ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758
• "UP_CELLS" : number of upstream cells (pixels)
• "AREA" : upstream drainage area (km²)
• "DISCHARGE" : discharge (m³/s)
• "WIDTH" : mean bankfull river width (m)
• "WIDTH5" : 5th percentile confidence interval bankfull river width (m)
• "WIDTH95" : 95th percentile confidence interval bankfull river width (m)
• "DEPTH" : mean bankfull river depth (m)
• "DEPTH5" : 5th percentile bankfull river depth (m)
• "DEPTH95" : 95th percentile confidence bankfull river depth (m)
• "LENGTH_KM" : segment length (km)
• "ORIG_FID" : original ID of segment
• "ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg
• "POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)
• "POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)
• "SLOPE" : average slope of segment (m/m)
• "CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/
• "CITY_POP_M" : population of joined city (max N inhabitants)
• "DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html
• "DAM_AREA_S" : surface area of joined dam (m²)
• "DAM_CAP_MC" : volumetric capacity of joined dam (m³)
• "CELER_MPS" : modeled river flow wave celerity (m/s)
• "PROPTIME_D" : travel time of flow wave along segment (days)
• "hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins
• "GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0
• "FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution
• "SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.
• "UPSTR_DIST" : upstream distance to the basin outlet (km)
• "UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)
• "CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)
• "DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)
• "MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)
• "MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)
• "MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)
• "MC_LENGTH" : mean of Monte Carlo simulated river length (m)
• "MC_SLOPE" : mean of Monte Carlo simulated river slope (-)
• "MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)
• "MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))
• "CONTINENT" : integer indicating the HydroSHEDS region of shapefile

2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:

• Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles
• Col2: Downstream UID
• Col3: Number of upstream UIDs
• Col4 – Col12: Upstream UIDs

3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:

• FID : unique identifier of each polygon
• CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)
• CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)
• COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)

4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table.

5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:

• GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv
• GAUGE_SITE: USGS gauge site number of joined gauge
• GAUGE_HUC8: which hydrological unit code the gauge is located in
• OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R.
• OBS_CEL_MPS: gauge-based celerity estimate (m/s).

6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.

7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.

8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:

• tabdTT_b.csv : basin outlet travel times for all rivers
• tabdTT_b_swot.csv : basin outlet travel times for SWOT
• tabdTT_c.csv : next downstream city travel times for all rivers
• tabdTT_c_swot.csv : next downstream city travel times for SWOT
• tabdTT_d.csv : next downstream dam travel times for all rivers
• tabdTT_d_swot.csv : next downstream dam travel times for SWOT

Monthly mean ozone fields of the MSR. The MSR dataset results from a 30-year data assimilation run with 14 corrected satellite datasets as input, and is available on a grid of 1× 1 1/2° for the time period (1978-2008). The fourteen total ozone satellite datasets are from the instruments TOMS (on the satellites Nimbus-7 and Earth Probe), SBUV (Nimbus-7, NOAA-9, NOAA-11 and NOAA-16), GOME (ERS-2), SCIAMACHY (Envisat), OMI (EOS-Aura), and GOME-2 (Metop-A).