dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of clusters used in the k-means cluster analysis was set to four. This number was selected based on results from a suite of tests used to determine the optimal number of clusters using the R package "NbClust" (version 3. 0;  Charrad et al. , 2014). Each test uses a set of criteria to generate a score for the proposed number of clusters (ranged between 4 and 10). We used only tests where the optimal number of clusters was based on which proposed number of clusters had the maximum or minimum score so that the proposed cluster groups could be ranked accordingly. The mean ranking for all tests across all periods (OND and JFM for three super El Niños) was then used to determine the optimal number of clusters (Table  2 ). The response of precipitation and evapotranspiration (also obtained from GLDAS) to El Niño was also determined to compare against the soil moisture responses.
Results GLDAS soil moisture estimates were validated against all in situ soil moisture estimates as well as through the removal of three outliers (Fig. 2 ). The exclusion of the Ecuador, Australia and Brazil data resulted in an overall reduction in the number of observations by 15 % but dramatic improvement in the r 2 between GLDAS and in situ estimates from 0. 03 to 0. 54. Comparison of these datasets following the removal of outliers reveals a consistent positive bias in the GLDAS soil moisture estimates relative to in situ observations. Consequently, the equation from the best-fit linear regression line (Eq. 1) was used to reduce the bias in the GLDAS estimates (Fig. 2 ). The use of the bias-corrected soil moisture estimates from GLDAS resulted in a mean reduction of RMSE across all sites by 69 % (Fig.
4 index does not distinguish between the magnitude or type (e. g. , central Pacific or eastern Pacific) of El Niño  (Kao and Yu, 2009; Yu and Zou, 2013). As such, the correlations shown in Fig. 8  are more representative of mean El Niño conditions, while the soil moisture changes depicted in Fig. 5a  and b are representative of super El Niño conditions. We refrained from conducting the correlation between soil moisture and the Niño-3. 4 index using only months when the three super El Niños occurred because this would severely limit the number of observations available for use in the analysis. Another potential issue is related to the accuracy of the GLDAS soil moisture response to El Niño for the tropics, which was dealt with through comparison to in situ observations. The large disagreement between in situ and bias-corrected GLDAS soil moisture for some locations is likely to be the result of a mismatch in scale between these two datasets.
Several strategies exist that can increase confidence in soil moisture estimates from data products like GLDAS. First, in situ observations of soil moisture need to improve in both space and time to evaluate and constrain the land surface models used in GLDAS. The distribution of soil moisture observations is much lower in tropical regions than other areas  (Brocca et al. , 2017) , which is not surprising given the dearth of hydrologic observations available from developing countries in tropical regions  (Alsdorf et al. , 2007)  coupled with the reported decrease in hydrologic monitoring across sites worldwide  (McCabe et al. , 2017). In addition, increased participation in contributing in situ soil moisture data to online databases such as FLUXNET (ORNL DAAC, 2017b) and ISMN  (Dorigo et al.
, 2011 (Dorigo et al. , , 2013) )  would help alleviate the limited access to observational datasets. Satellite observations of soil moisture can also be used to fill this gap, but a number of issues exist with historical satellite-derived estimates of soil moisture. Substantial biases exist in retrieval algorithms  (Entekhabi et al. , 2010) , and direct estimates are that restricted to shallow soil depths are of limited value when soil moisture at greater depths is needed  (McCabe et al. , 2017). Such shortcomings have encouraged investigations into the relative influence of vegetation, soil and topography on soil moisture dynamics to better upscale point-based measurements of soil moisture to larger, remotely sensed scales  (Gaur and Mohanty, 2016). Algorithms have been developed to interpolate shallow subsurface estimates of soil moisture to the root zone, but a recent global evaluation of the accuracy of the algorithms being used for this purpose to generate Soil Moisture Active Passive (SMAP) Level 4 data was limited to 17 sites, with only one occurring within the tropical climate zone  (Reichle et al.
Data availability. In situ data that were used for this research are available at the Next-Generation Ecosystem Experiments (NGEE) -Tropics data archive (https://ngt-data. lbl. gov/; NGEE, 2020). All data that were used were cited in Table  1  or credit was given in Acknowledgements. Global soil moisture data products used in this study are publicly available at the following locations: GLDAS at https://disc. gsfc. nasa. gov/datasets?page=1&keywords=GLDAS (NASA, 2020a); ERA5 at https://cds. climate. copernicus. eu/ cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview (ECMWF, 2020); MERRA2 at https://disc. gsfc. nasa. gov/ datasets?keywords=22MERRA-222&page=1&source= Models2FAnalysesMERRA-2 (NASA, 2020b); and GLEAM at https://www. gleam. eu/#downloads (ESA CCI, 2020). Supplement. The supplement related to this article is available online at: https://doi.
Acknowledgements. This project was supported as part of the  Next-Generation Ecosystem Experiments -Tropics , funded by the  United States Department of Energy Office of Science Office of Biological and Environmental Research  through the  Terrestrial Ecosystem Science program. Data obtained from French Guiana were recorded thanks to an  "investissement d'avenir" grant  from the  Agence Nationale de la Recherche  ( CEBA  no. ANR-10-LABX-25-01 ;  AR-BRE  no. ANR-11-LABX-0002-01 ). Data obtained from one of the Panama sites were recorded thanks to an award from the  National Science Foundation  (NSF; no. 1360305 ). In situ data collected from Indonesia were made possible by the  Deutsche Forschungsgemein-schaft  (DFG;  German Research Foundation ; project no. 192626868  of  SFB 990 ) and the  Ministry of Research and Technology/National Research and Innovation Agency (Ministry of Research, Technology and Higher Education; Ristekdikti)  in the framework of the collaborative  German-Indonesian  research project  CRC 990.
We would also like to thank  Charu Varadharajan  and  Emily Robles  for providing valuable assistance with database access and guidance on data storage during the course of this research. The  Pacific Northwest National Laboratory  is operated for the  Department of Energy by Battelle Memorial Institute  (contract no. DE-AC05-76RL01830 ). Financial support. This research has been supported by the  U. S. Department of Energy, Office of Science  (grant for  Next-Generation Ecosystem Experiments -Tropics ). Review statement. This paper was edited by  Roger Moussa  and reviewed by two anonymous referees.
4A )  [107]. Interestingly, if a large number of cross-linkers is required to condense a polymer to a small blob, surprisingly, only ∼ 4% are removed to create the decondensed DSB phase. In addition, the mean radius of gyration R g ≈150nm is mostly unchanged between the unbroken locus and DSB. By removing the number of connectors, the mean maximal distance between the two monomers increases from 0. 33µm in the unbroken case to 0. 75µm after DSB. Separation of the two DSB ends cannot be too large due to remaining connectors that maintain the two extremities. To conclude, randomly positioned cross-linkers can be used to model chromatin condensation and dynamics. Their minimum number can be adjusted to match experimental data of SPTs and HiC, offering a simple tool to represent chromatin organization at a given spatial scale, that can be used to explore the local chromatin environment and dynamical properties.
The encounter probability between monomers, which depends on the number of added connectors N c is usually a free parameter to be fitted from empirical data. Using the RCL model it is possible to fit the empirical loci encounter probability from HiC data and derive the minimal number of crosslinks in a genomic section at a coarse-grained resolution. The RCL model could therefore be used to obtain structural parameters not necessarily contained in the HiC data, such as the radius of gyration, the MSD of any loci, and the mean first encounter time between any two loci. Finally, we summarize the procedure of construction of a polymer model: 1. First, select the resolution to coarse-grain chromatin, so that one bead represents a given Xkbps. For example, X = 3kbps with 100 beads, leads to a representation of 3MBp.
Type 1 Matlab codes to compute the four parameters from SPTs  [3, 4]  are available at http : //bionewmetrics. org/analysis -of -spts/. Fast algorithms of RCLpolymers  [14]  are available at http : //bionewmetrics. org/randomlycrosslinkedpolymer//. Trajectories and polymer can be visualized in 3D and quantities such as mean-square-radius of gyration (MSRG), encounter probability (EP), and variance of the distance between loci, the MSD and mean first encounter time (MFET) between loci are computed. Extension of this framework to include a single and multiple DSB, is provided in a Julia framework at http : //bionewmetrics. org/randomlycross-linked-polymer -f ollowing -dsb/.
Types 2-3 The numerical code to generate a generalized RCL polymer framework, consisting of two interacting Topologically Associating Domains (TADs)  [81]  is available at http : //bionewmetrics. org/reconstruction-of -a-polymermodel -f rom -the -chromatin -capture -data/. Short random and fixed long-range connectors are added between TADs. Chromatin parameters can be calibrated directly from chromatin conformation capture data (3C, 5C, HiC) given TAD boundaries positions. Peaks within and between TADs located in the encounter probability matrix are detected and converted in a number N c of connectors. This framework enables to study the influence of short-range random connectors within TADs and long-range fixed connectors within and between TADs on the dynamic properties of loci in cross-linked chromatin.
Importantly, however, the main effects of our Feedback and Difficulty manipulations reliably and consistently impacted SPEs across all measures. To explore the source of these differences in SPEs, we split task choice and task ability rating data into the six types of learning blocks (Fig. 3a, b ). Strikingly, subjects chose Feedback-Difficult tasks more frequently when paired with No-Feedback-Easy tasks Fig. 2  Behavioral dissociation between objective performance and SPEs in Experiment 1 (N = 29). a Performance (mean percent correct) was better for easy than difficult tasks, but was not different in tasks with and without feedback. Global self-performance estimates (SPEs) as measured directly by task choice (b) or indirectly via task ratings (c) were higher in the presence than in the absence of feedback, despite objective performance being unchanged.
Methods Participants. In Experiment 1, 39 human subjects were recruited online through the Amazon Mechanical Turk platform. Since we had no prior information about expected effect sizes, we based our sample size on similar studies conducted in the field of confidence and metacognition. Subjects were paid $3 plus up to $2 bonus according to their performance for a ~30 min experiment. They provided informed consent according to procedures approved by the UCL Research Ethics Committee (Project ID: 1260/003). The challenging nature of the perceptual stimuli, which appeared only briefly, ensured that it was impossible for subjects to perform above chance level if they were not paying careful and sustained attention during the experiment. To further ensure data quality, standard exclusion criteria were applied.
Ten participants were excluded for responding at chance level and/or always selecting the same rating, leaving N = 29 participants (17 f/12 m, aged 22-31 and not color-blind according to self-reports) for data analysis. In Experiment 2, 31 subjects were recruited using the same protocol as in Experiment 1. Identical exclusion criteria were applied leading to the exclusion of two subjects, leaving N = 29 subjects for analysis (9 f/20 m, aged 19-35). In Experiment 3, to examine between-subjects relationships between the formation of self-performance estimates (SPEs) and metacognitive ability, 73 subjects were originally recruited online. After application of identical exclusion criteria to those used in Experiments 1 and 2, we additionally excluded subjects who failed comprehension questions about usage of the confidence scale (subjects passed if they rated "perfect" performance at least 10% greater than "chance" performance), leaving N = 46 subjects for analysis (16 f/30 m, aged 20-50).
e. , test block] trials and on the accuracy of your ratings". As data were collected online, instructions were as selfexplanatory and progressive as possible, including practice trials with longer stimulus presentation times on one task (one color cue) at a time. Each learning block featured two tasks, with each trial starting with a central color cue presented for 1200 ms, indicating which of the two tasks will be performed in the current trial (Fig. 1c ). The stimuli were black boxes filled with white dots randomly positioned and presented for 300 ms, during which time subjects were unable to respond. We used two difficulty levels characterized by a constant dot difference, but the spatial configuration of the dots inside a box varied from trial to trial. One box was always half-filled (313 dots out of 625 positions), whereas the other contained 313 + 24 dots (difficult conditions) or 313 + 58 dots (easy conditions).
Regressors were z-scored to ensure comparability of regression coefficients. In addition, we examined potential recency effects to ask whether subjects weighted all trials equally when forming global SPEs. We performed a logistic regression with accuracy (X) predicting task choice (Y) (Supplementary Fig. 1 ). We included four regressors (X1-X4) corresponding to the four quartiles of each block, in chronological order, with all six pairings pooled together. Subjects were treated as fixed-effects due to a limited availability of task choice data points per subject precluding the use of full random-effect models. To provide evidence that task choice and task ability ratings were consistent proxies for SPEs, we calculated how often the chosen task was rated higher than the unchosen one, and we compared the mean ratings given for chosen and unchosen tasks (paired t test).
Acknowledgements The  Wellcome Centre for Human Neuroimaging  is supported by core funding from the  Wellcome Trust   203147/Z/16/Z. S. M. F. is supported by a  Sir Henry Dale Fellowship  jointly funded by the  Wellcome Trust  and  Royal Society  ( 206648/Z/17/Z ). P. D. was supported by the  Gatsby Charitable Foundation  and the  Max Planck Society.
We present a global dataset of anthropogenic carbon dioxide (CO 2 ) emissions for 343 cities. The dataset builds upon data from CDP (187 cities, few in developing countries), the Bonn Center for Local Climate Action and Reporting (73 cities, mainly in developing countries), and data collected by Peking University (83 cities in China). The CDP data being self-reported by cities, we applied quality control procedures, documented the type of emissions and reporting method used, and made a correction to separate CO 2 emissions from those of other greenhouse gases. Further, a set of ancillary data that have a direct or potentially indirect impact on CO 2 emissions were collected from other datasets (e. g. socio-economic and traffic indices) or calculated (climate indices, urban area expansion), then combined with the emission data.
Ancillary emission drivers data related to processes impacting the use of carbon fuels allow us to better understand differences in CO 2 emission patterns between cities. Key drivers are population and Gross Domestic Product (GDP) per capita. For instance, analysis of the relationships between per-capita consumption and per-capita GDP was applied to energy consumption in cities by Creutzig et al. 1  , who derived a typology of urban energy use and mitigation potentials and highlighted city compactness and high gasoline prices as explanatory variables for lower energy use in affluent cities. A comprehensive global city dataset with Scope-1 CO 2 emissions with data related to emission drivers is still missing, although there are several regional datasets  [8] [9] [10] [11] [12] [13]  as well as recent efforts to estimate city carbon footprints using regional  14  and national  15  consumption-based models.
Sorting out the best available information on data sources and a better understanding of the methods used to produce them help address inconsistencies across the set of cities studied; however, full traceability to the original data used in each city emission inventory is often not possible, and city-level CO 2 emissions and driver-related data are not always consistent across space and time. Further, the scope of gases and sectors covered by the datasets are often uneven and activity data could have been scaled down from national data when local activity data are unavailable. Here, we present a global dataset of Scope-1 city CO 2 emissions based on CDP  16  , the carbonn Climate Registry of the Bonn Center for Local Climate Action and Reporting (http://carbonn. org), and a set of Chinese cities compiled by Beijing University (private communication).
CO 2 emission data are complemented with key ancillary data associated with emissions. The dataset includes 343 cities in nine geographic regions. The majority (88%) of these cities reported emissions between 2010-2015. When the original data reported total emissions from multiple GHGs, we developed a simple correction procedure to remove the contribution of methane (CH 4 ). Scope-2 emission data are provided as additional information when available. Wherever possible, independent consistency checks of the data are performed. The final dataset of Scope-1 CO 2 emissions and ancillary data is organized as a tab-separated values (TSV) file of dimensions 343 rows (cities) × 179 columns. In the following, we describe the methodology used to produce the final dataset (Methods) and details about each individual dataset (Data Records).
Methods Figure  1  shows the different datasets (D*) used to construct the final consolidated dataset of Scope-1 CO 2 emissions and ancillary data. Table  1  lists each D* and the method steps in which they intervene. Figure  2  gives the flow chart followed to produce the final dataset from individual D*. Most data handling in the Methods (reading, processing, validating, visualizing, compiling, saving) was done in Jupyter notebooks (http://jupyter. org/, open-source web application for code, visualizations, etc. ) using pandas DataFrames (http://pandas. pydata. org/), a data structure that allows data sorting, counting or conditional replacement to be executed on tabular data in lines of code. Table  2  (available online only) lists the attributes preserved in the final dataset, along with their source dataset and references.
Step 1. Scope-1 GHG emissions from CDP The 2016 edition of the CDP emissions dataset  16  (D CDP2016 ) provides annual mean GHG emissions in CO 2 equivalents from 187 cities in eight geographic regions. The CDP data are collected through an Online Response System that allows cities to report climate hazards, climate actions, targets and emission inventories. The emissions disclosed to CDP are provided directly by city governments. Cities first identify the inventory boundary and their emissions sources. Most cities use the scopes framework to report Scope-1 and Scope-2 emissions. Scope-1 are the direct territorial emissions from residential and industrial heating, transport, industrial sectors and power plants within the territory of cities  5. Scope-2 are the emissions from purchased energy generated upstream from the city, mainly electricity  5.
We highlight that this is a simplification; however, deriving city-specific estimates from country-specific emission factors seems reasonable and agrees with the approach taken by UNFCCC for the official national reporting. Regarding whether per capita waste generation (solid waste and waste water) might differ between rural and urban areas, we assume that for OECD countries, there is only a minor difference since consumption and income levels are closely correlated. For emerging countries, the difference in per capita waste of rural and urban population could be larger or CH 4 emissions from waste might not even be covered in all rural areas as managed, and this accounted for in the used CH 4 emission dataset. A lower bound of f min g 1 À Á Scope-1 CO 2 emissions was obtained using the same approach, considering this time CH 4 emissions from waste and from natural gas.
Integrate two other emission datasets focusing on developing countries Most of the cities that disclosed their emissions to CDP are from USA, Canada, Australia, New Zealand (43%) and Europe (31%). Therefore, the D CDP dataset lacks coverage for cities in developing regions, which limits the use of the data for global analysis and synthesis of differences across cities. To fill this gap, we collected two additional emission datasets: Step 4. Match ancillary data with Scope-1 CO 2 emissions Ancillary data related to emissions were collected to match Scope-1 CO 2 emissions. Spatial units of these ancillary data were checked for consistency with emissions data. The consistency of spatial and temporal units between ancillary data and emissions, and within ancillary data themselves, were checked and assigned a Quality Flag (see Technical Validation).
Socio-economic and observational climate data First, we used three datasets previously reported in Creutzig et al. Altogether, the D GEA+ attributes include population, area, population density, national diesel and gasoline prices, average household size, bounding geographical features, a commerce index (https://www. scribd. com/document/17016734/MasterCard-Worldwide-Centers-of-Commerce-Index-2008), an urbanization index (http://www. un. org/en/development/desa/population/publications/pdf/urbanization/ WUP2011_Report. pdf), and climate indices of heating and cooling degree days (HDD and CDD). HDD and CDD are commonly used to estimate the climate-dependent demand for energy needed to heat or cool a building, respectively, and are defined as the yearly sum of the difference between a base temperature T b and daily temperature, T daily , whenever the daily temperature is lower (HDD) or greater (CDD) than T b : HDD ¼ X days MAX T b -T daily ; 0 À Á ð6Þ CDD ¼ X days MAX T daily -T b ; 0 À Á ð7Þ In D GEA , D UITP and D WB , HDD and CDD were given for a 5-year average over 2007-2011 for 13 base temperatures in increments of 0.
5 °C (T b = 12. 5 °C-18. 5 °C for HDD and T b = 17 °C-23 °C for CDD). Those values were computed by the online software tool Degree Days. net (www. degreedays. net) created by BizEE Software (http://www. bizeesoftware. com/) using daily temperature data obtained from Weather Underground local weather stations in different cities (www. wunderground. com). The software returns monthly averages across the year range specified. We merged D GEA+ with D emissions. Missing ancillary data in this merged dataset were then completed from external sources and merged again into D emissions in the order listed below (for details on ancillary data types and methods, see Data Records).