dataesr/scientific-paragraphs-categorization huggingface dataset:
(2011)  in terms of the detection limit. However, real data are often biased by different sources (atmospheric turbulence, mechanical vibrations,. ). We performed two tests to compare both methods, one including uncorrelated Gaussian noise and another with a noise model, i. e. , correlated non-Gaussian noise. We used all observables and three data sets for each test. The Aspro2 software 4  were used to create these synthetic data sets, however, it does not have implemented the bandwidth smearing effect. We therefore did not take it into account in CANDID (this would not change the conclusion of our test). First test: we created the first "ideal" synthetic data set (i. e. , without noise) representing a uniform disk of 1 mas (3 h observation with three spectral channels with the PIONIER instrument).
Removing this possible companion from our interferometric data, we estimated the detection limit. We reached a minimum dynamic range of 0. 47%, corresponding to ∆m H > 5. 8 mag. This is a 90% completeness azimuthal value, which is a conservative value, and the component at a given position with a slightly higher contrast might be detected (as in this case). This limit allows us to exclude the presence of an additional companion with a spectral type earlier than F0V. AW Per: this Cepheid is a spectroscopic binary with an orbital period of ∼40 yr. First discovered by  Miller & Preston (1964) , it took several years to derive the first orbit from radial velocity data  (Evans 1989). It is likely that this companion is itself a binary, as argued by  Evans (1989) ,  Evans et al. (2000) , because the magnitude difference between the Cepheid and its companion is not consistent with equal masses and predictions from evolutionary tracks.
Acknowledgements. The authors would like to thank the  CHARA Array and Mount Wilson Observatory  staff for their support. Research conducted at the  CHARA Array  is funded by the  National Science Foundation  through  NSF  grant  AST-0908253 , by  Georgia State University , the  W. M. Keck Foundation , the  Packard Foundation , and the  NASA Exoplanet Science Institute. The authors also thank all the people involved in the  VLTI  project. A. G. acknowledges support from  FONDECYT  grant  3130361. J. D. M. acknowledges funding from the  NSF  grants  AST-0707927 ,  AST-0807577 , and  AST 1108963. We also acknowledge the  STSCI grant   HST-GO13841. 006-Aand HST-GO-13454. 07-A. W. G. and G. P. gratefully acknowledge financial support for this work from the  BASAL Centro de Astrofísica y Tecnologías Afines (CATA)   PFB-06/2007.
Support from the  Polish National Science Centre  grant  MAESTRO DEC-2012/06/A/ST9/00269  and the  Polish Ministry of Science grant Ideas Plus  (awarded to G. P. ) is also acknowledge. W. G. also acknowledges financial support from the  Millenium Institute of Astrophysics (MAS)  of the  Iniciativa Cientifica Milenio del Ministerio de Economía ,  Fomento y Turismo de Chile , project  IC120009. A. G. also acknowledge computational resources from  FONDECYT  grant  1130521  and  1130721. We also acknowledge support from the  ECOS/Conicyt  grant  C13U01. PIONIER is funded by the  Université Joseph Fourier (UJF) , the  Institut de Planétologie et d'Astrophysique de Grenoble (IPAG) , the  Agence Nationale pour la Recherche  ( ANR-06-BLAN-0421 ,  ANR-10-BLAN-0505 ,  ANR-10-LABX56 ), and the  Institut National des Science de l'Univers (INSU PNP  and PNPS).
The integrated optics beam combiner is the result of a collaboration between IPAG and CEA-LETI based on CNES R&T funding. This research received the support of  PHASE , the high angular resolution partnership between  ONERA, Observatoire de Paris ,  CNRS , and University Denis Diderot Paris 7. This work made use of the SIMBAD and VIZIER astrophysical database from  CDS, Strasbourg, France  and the bibliographic informations from the  NASA  Astrophysics Data System. This research has made use of the  Jean-Marie Mariotti Center SearchCal  and ASPRO services, co-developed by  FIZEAU  and  LAOG/IPAG , and of CDS Astronomical Databases SIMBAD and VIZIER.
Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global data set of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985-2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake.
For satellite-sampled lakes, accuracy of the georeferencing is roughly 0. 5 km for the ATSR series of instruments and about 2 km for the AVHRR series; because these series are combined, 2 km is listed in the database. Only lakes that exhibited a 10 × 10 km area of pure water surface were used to ensure that inaccuracies in georeferencing would not introduce any contamination by land surfaces. For in situ-sampled lakes, in most cases the coordinates reflect the general lake or a shoreline location and may not necessarily reflect the exact water temperature sampling location.
100% time_period The 3-month period during which data was averaged to create a summer surface water temperature, using abbreviations from the first letter of each month's name. This period also coincides with summer data for the meteorological variables associated with the lake. 100% contributor Names and contact information for people who contributed the data. 100% Table  1. Data labels and description for metadata file (lakeinformation. csv). Sky Pond, located in Loch Vale watershed northwest of Denver, Colorado, United States, was sampled at Sky Pond outlet where Sky Pond drains to Icy Brook. Samples were collected weekly from 1982-1991  during open water months and monthly during open water months since 1991. Water temperature samples were collected at 10 cm depth in the early afternoon.
Northeastern North America Water temperatures in Lakes Superior, Huron, Michigan, and Erie in North America were recorded every 10 min to hourly at a depth of 0. 6-1 m. The samples were obtained from three different sites in Lake Superior  12  , two sites each on Lakes Huron and Michigan, and one site on Lakes Erie. Water temperatures were recorded using 2-3 m surface buoys  25  , usually ARES or AMPS payload (http://www. ndbc. noaa. gov/ rsa. shtml) and various hulls. Table  2. Data labels and description for data values file (values. csv). See the text for how 'summer' is defined for each lake location. 'Winter' is defined as the 3-month period opposite to 'summer' in the calendar year (e. g. , winter is January-February-March for lakes where summer is July-August-September, and vice versa).
Air temperatures were obtained from National Centers for Environmental Prediction (NCEP) and Climatic Research Unit (CRU), radiation from Surface Radiation Budget (SRB) and cloud cover from Advanced Very High Resolution Radiometer Pathfinder Atmosphere Extended dataset (PATMOS). Trout Lake, Crystal Lake, Sparkling Lake, Allequash Lake, Big Muskellunge Lake are in northern Wisconsin, United States. Lake Mendota, Lake Monona, Lake Wingra, and Fish Lake are in southern Wisconsin. These lakes were sampled fortnightly, typically at 1 m intervals including the surface by the North Temperate Lakes Long Term Ecological Research program  26. Temperatures were obtained from a consistent sampling location at or near the deepest portion of the lake. Sampling was performed during the daytime, typically between 9:00 and 15:00 with a YSI Model 58 tempDO metre (https://lter.
After 1992, sampling depth for Lake Ocheechobee changed from 0 to 0. 5 m. Water temperature sampling was performed in the morning with a YSI multi-parameter in situ water analyzer. Water temperatures were measured in situ with either a YSI or Hydrolab data sonde in the morning, and calibration was done on the day or week of sampling against National Bureau of Standards thermometers. The Lake Annie Buoy is located near the centre, over the deepest part (20. 4 m) of Lake Annie, Highlands County, Florida. Sampling was carried out with automated thermistors, located at 1 m intervals from the top to the bottom of the lake. An Ecolab Templine was used between February 14, 2008 and September 15, 2010 and a Fondriest thermistor chain was used from February 14, 2011 to present. The same procedure was used for cleaning and calibration of both instruments.
Europe Lough Feeagh, located in County Mayo, Ireland was sampled daily from the surface at less than 1 m depth. Water temperatures were recorded at a site 60 m along the outflow draining straight out of the lake  33. Water temperature sampling was conducted continuously (in situ paper or sensor record), but compiled daily at midnight. From 1960-2004, a paper chart recorder with a mercury thermometer was used. From 2004 to present, a StowAway TidbiT temperature data logger from Onset (TBI32-05+37) was used. Loch Leven, situated in Perth and Kinross, Scotland, United Kingdom, was sampled biweekly, from the subsurface at an approximate depth of 0. 01 m. The samples were obtained from the Reed Bower sampling site which is an open water site that has a water depth similar to the average depth of the lake and remains accessible in most weather conditions.
42). Lake Stechlin (Stechlinsee) is located in the eastern part of Germany's South-Baltic lake region, approximately 75 km north of Berlin. Data on water temperature were provided by the German Meteorological Service (DWD, 1958-1996) and the Institute of Freshwater Ecology and Inland Fisheries (IGB, including predecessor institutions, 1970-2011), respectively. Vertical profiles were taken in the morning (10:00-12:00) at the deepest location of the lake (69 m). The DWD series consisted of monthly measurements in 2 m intervals from 0 to 20 m and then in 10 m intervals to the deepest point. Profiles in the IGB series were generally taken monthly, but were taken less often in the 1980 s and then were taken biweekly during the summer after 1991. In the epilimnion, profiles were taken at 2. 5 m intervals (1.
5 °C. We used the satellite data to calculate bulk water temperatures from skin temperatures using sensorspecific correction factors derived from the Laurentian Great Lakes  11. We computed water skin temperature from the brightness temperatures using the dual-view operational split-window coefficients provided for ATSR  62. ATSR data has also been extensively validated over the oceans and over the Lake Tahoe (CA/NV) validation site operated by the Jet Propulsion Laboratory  63. We compiled time series from both series of satellite instruments independently at all 141 sites, with AVHRR data available for the period 1985-2009 and ATSR data available for the period 1991-2009. We then merged the cloud-free observations from both series of instruments into a single dataset to maximize the sampling frequency.
Air temperatures We obtained mean, minimum, maximum, and diurnal temperature range (defined as the difference between daytime maximum and nighttime minimum temperatures) for summer, winter, and annual air temperature data for each lake from two separate sources: the Climatic Research Unit (CRU) at the University of East Anglia, United Kingdom and the National Centers for Environmental Prediction (NCEP). We obtained the CRU data from version 3. 21 of their dataset using 0. 5-degree resolution grid cells  64. These data are monthly gridded files calculated from observations. Lake values were the mean of all months within the appropriate time period to match the sampling period (e. g. , July-August-September for northern hemisphere summer, January-February-March for northern hemisphere winter), see Section G below) for a given year for the single grid cell that contained the sampling location for the lake.
Exceptions included large bodies of water, such as Lake Superior and the Caspian Sea, which included an average of the grid cells that surrounded these lakes (because we used a land-based air temperature dataset, grid cells that were over only water did not have data). The air temperature data we obtained from NCEP represent version 2 of the NCEP reanalysis data  65. We retrieved air temperature data at a height of 2 m for each lake using the RNCEP package, a userfriendly package for use in the R programming language  66. Air temperatures for the NCEP reanalysis data are projected onto a global T62 Gaussian grid of 192 equally spaced points in longitude and 94 unequally spaced points in latitude  65. For lakes smaller than one cell on the T62 grid, we extracted data for the cell containing the latitude and longitude representing the approximate center of the lake.
Solar, longwave and total radiation Summer and winter surface solar (i. e. , shortwave) and longwave radiation data are a satellite product taken from the NASA/Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) radiation dataset, version 3. 0 (obtained from the NASA Langley Research Center Atmospheric Science Data Center). The data are generated on the basis of the International Satellite Cloud Climatology Project (ISCCP) DX radiance and cloud parameters  67  , using an updated version of the University of Maryland's shortwave flux algorithm and an adaptation of the longwave algorithm. The dataset has a spatial resolution of 1°× 1°and temporal resolution of 3 h. In addition to the SRB incoming shortwave radiation (SW) and longwave radiation (LW), we also provide estimates of the total incoming radiation (R tot = 0.
93*SW+0. 97*LW) that is absorbed by a dark, water surface under ice-free conditions (i. e. , an assumed shortwave albedo of 7% and longwave emissivity of 0. 97). The SRB radiation data have been shown to be one of the best global radiation datasets and exhibit smaller biases than reanalysis data and other satellite products. For example, Gui et al. 68  assessed SRB solar radiation data using ground measurements collected at 36 globally distributed sites from 2000-2002. Results indicate that the downwelling solar irradiance shows good overall agreement with ground measurements, except for a few large biases in Southeast Asia (Sukothai, Bukit, and Kogma). These biases range from -90. 2 W m -2 (-22%) at Sukothai to +45. 8 W m -2 (+12. 8%) at Bukit when comparing SRB solar radiation data with ground observations.
Cloud cover Instrumentation measuring global cloud cover was rarely continuous during the full observational period of this study (1985-2009). The NOAA 5-channel Advanced Very High Resolution Radiometer (AVHRR) cloud imagery record began in 1981, and cloud statistics derived using Pathfinder Atmosphere's Extended (PATMOS-x) processing system are available with global coverage  70. To calculate seasonal means for this study we used temporally interpolated values representing 13:30 local time for cloud amount (fractional cloud cover) from a 1°re-gridded version of this dataset  71. We calculated mean cloud cover (0-1) during the summer and winter periods using the average of these daytime values for 1985-2009.
Technical Validation Quality control and assurance of in situ data In order to efficiently assemble a complete product from the disparate data sources used for this dataset, we composed a set of scripts in the R programming language  73. After the assembly of the dataset was complete, we examined the distribution of mean lake temperatures using a series of histograms and maps. The histograms allowed us to identify outliers while the maps provided geographic plots that could be examined for unusual observations or spatial patterns that might indicate technical errors during the assembly of the dataset. To maximize our chances of identifying erroneous values, the histograms and maps were examined by all co-authors. To ensure that the geomorphometric data in the completed dataset were accurate we had several individuals independently check the values against reputable sources.
The final step taken to assure the quality of our assembled dataset was to share the derived mean summer temperatures and geomorphometric data with the original data providers. We sent an email to each of the data providers containing the relevant data for their lake(s) along with a request to validate the values that we have included in the completed dataset. This final step was probably the most important as it allowed us to get feedback from the individuals that are most familiar with the lakes in our dataset. Whenever there were inconsistencies in data, we worked directly with the original data provider to resolve the issue and update the data accordingly.
In order to estimate the amount of error attributable to differences in sampling interval, we designed bootstrap simulations in the R programming language. For these simulations, we used temperature records from six sites on four lakes that had daily temperature data available (Figure  3 ). For each site, we constructed simulated temperature datasets consisting of a subset of the daily temperature observations. We used sampling intervals ranging from once per day to once every 34 days and constructed 20000 simulated datasets for each sampling interval. The date of the first sample for each of the 20000 datasets was chosen as a random date in June and then the following samples were equally spaced in time starting from that date. Therefore, variation among the simulated datasets for each sampling interval was driven by random variation in the choice of the initial sampling date.
We then used linear interpolation to calculate daily temperatures for July, August, and September. The mean of these daily temperatures represented the annual mean summer temperature. We treated the mean annual summer temperature calculated with a sampling interval of 1 (daily measurements) as the 'true' mean temperature. For all of the other sampling intervals we calculated the difference between the true mean and the mean calculated with interpolation. The difference was calculated by year for each simulated dataset. We then took the average error for the 20,000 simulated datasets for each sampling interval and plotted the results. In this manner, we were able to construct a relationship between sampling interval and error in our temperature estimates (Figure  4 ). Our results demonstrated that although errors in our temperature estimates did increase at large sampling intervals, they were relatively modest, even at a sampling interval of once every 34 days (-0.
Acknowledgements We would like to thank the numerous field and research scientists who worked tirelessly to collect and document data from each lake over the past 25+ years. We thank  Tim Kratz  for helping with the initiation of this project. The Lake Baikal data are part of a dataset (No. 2005620028 ) registered with the government of the Russian Federation and collected by many  Irkutsk State University  staff, now supported by  Russian Ministry of Education and Science , research project  GR 01201461929 ,  National Science Foundation  ( DEB-1136637 ) supported additional data management. Data for the Austrian lakes were extracted from the year books of the Austrian Hydrological Survey, Department IV/4-Water cycle,  Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management.
The average depth is from 50 m to 120 m, with the maximum value up to 138 m  [10]. This layer is the main source for abstraction with a capacity approximately from 2. 6 L/s to 19. 3 L/s. Leveling data had been surveyed along selected routes in 2003 and in 2009. Each route leveling procedure was carried out by using an optical micrometer to determine the difference in level between points, allowing the elevation of given points above the mean sea level to be computed  [20]. A zoom version of Figure  2a  is provided in Figure  A2  in the Appendix to facilitate visualization leveling results. The velocity (mm/year) of 19 route leveling measurements was calculated for comparison with the multi-temporal InSAR approach. The vertical velocity of ground motion varies from -47 to 2 mm/year with about a -15-mm/year average.
SAR Data The Phased Array L-band Synthetic Aperture Radar (PALSAR) of the ALOS (Advanced Land Observation Satellite) data descending stack is from the Japan Aerospace Exploration Agency. In single-imaging mode, the resolution is about 4. 7 m in the slant range and 4. 5 m in the azimuth direction  [21]. Each image has been resampled on a common grid (so-called master) on 11 December 2008. Table  A2  in Appendix provides detailed information. It is important to note that the Ho Chi Minh City site and other Asian areas are outside the Copernicus data policy of ESA. There is very little ESA data, e. g. , ENVISAT ASAR, suitable for such applications, which typically require a stack of multi-temporal data. In this work, we find that the ALOS PALSAR data are the best dataset available for this study.
PS/DS Processing Chain Detail In this work, we are in principle following the two-step approach in the MLE framework, to exploit not only PS, but also DS information for estimating the deformation. The reader is referred to  [13]  for the full rigorous mathematical descriptions. In the following, the PS/DS processing chain adapted in this work can be described as follows: (1) Carry out coregistration of the slave SAR to the master SAR. Each image within the ALOS data stack has been resampled on a common master grid on 11 December 2008. A digital elevation model (DEM) is built from land surveying data in 2003, which has an accuracy of about 0. 1 m and 0. 4 m in flat and topographic areas, respectively. The surveyed DEM has been transformed to each master grid and then compensated for the topographic contribution.
(2) For each pixel, find the family of statistically homogeneous pixels (SHP) by applying the two-sample Kolmogorov-Smirnov test  [13]. A window of 15 × 15 is used for the ALOS PALSAR dataset. (3) Define DS at those pixels for which the number of SHP is larger than a certain threshold. The threshold is chosen as 50 pixels to maintain the point-wise radar response of PS. We thus expect the number of looks for sample coherence estimation to range from 50 to 225. (4) For all DS, estimate the sample coherence matrix taking advantage of the SHP families identified in Step 2 above. (5) Apply the phase linking algorithm to each coherence matrix associated with each DS to squeeze optimized phases. (6) Select the DS exhibiting a phase linking coherence value higher than 0. 2 and substitute the phase values of the original SAR images with their optimized values.
Ground Subsidence Results The period of 2006 to 2010 using ALOS PALSAR data was processed by using the PS/DS processing chain mentioned in Section 3. 1. More than 400,000 PS/DS measuring points in the ALOS PALSAR L-band (LOS angle: 34. 9 degree) dataset were identified within an area extent of about 750 km 2. We note that there is an area that lacks PS/DS points in the north part of Tan Binh District, which is the Tan Son Nhat airport. This can be due to temporal decorrelation caused by vegetation. Long wavelength P band is foreseen to yield better performances; see, for example,  [32]. Assuming that most of the measured deformation corresponds to vertical displacement of the surface due to subsidence, we can then obtain vertical displacement through straightforward geometrical arguments. The assumption is supported by the fact that tectonics are found only in northwestern and in central Vietnam  [33].