dataesr/scientific-paragraphs-categorization huggingface dataset:
4. The original NARR data were provided by the NOAA/OAR/ESRL PSD 5  , available at http://www. esrl. noaa. gov/psd/ (last access: 14 January 2011). The NARR variables were regridded to a spatial resolution of 0. 25 • × 0. 25 • , from their original Lambert Conformal Conic Projection at 32 km resolution using both areaweighted and distance-weighted averages. An area-weighted averaging method was used for precipitation and radiation flux variables in order to conserve their total magnitude for North America. For highly spatially auto-correlated state variables (e. g. , air temperature, humidity), distance-weighted averaging was used because values for these variables tend to cluster together in space. The U direction (along longitude) and V direction (along latitude) wind speeds were combined into an overall surface wind velocity variable prior to the regridding process.
Applying this rescaling allowed us to retain the advantages provided by the NARR data product, while also leveraging the information provided by GPCP. To rescale the NARR precipitation, for each month, precipitation of all 3-hourly 0. 25 • NARR grids within each 2. 5 • GPCP grid was summed up along time, averaged over space and linearly rescaled to match the magnitude of the total monthly GPCP precipitation. Figure  2  uses a special color scheme to present the difference map between the long-term mean (1979-2010) annual total precipitation from rescaled NARR and original NARR products. An interesting pattern can be observed in Fig. 2 , where rescaling decreases precipitation in the northern part of North America while it increases in the southern part. Specifically, the rescaled product better represents the magnitude of extreme rainfall events at the coastline of Gulf of Alaska and Central America, while generally preserving both the magnitude and spatial pattern in most other areas of North America.
During the period 1979-2010, when the temporally and meridionally resolved GV product is available, atmospheric CO 2 concentrations are set directly to the GV marine boundary layer reference surface interpolated to the MsTMIP global and North American grids. Prior to 1979, we preserve the 1979-2010 mean annual cycle from GV, and impose onto it a modeled CO 2 surface that represents annual mean concentrations and a time-evolving meridional gradient. Following  Conway and Tans (1999) , the annual mean difference between MLO and SPO in the GV product is modeled as a linear function of fossil fuel (FF) emissions  (Marland et al. , 2008). Extrapolated to zero FF emissions, the pre-industrial MLO-SPO difference estimated in this manner is 0. 3 ppm. Performing this same exercise using Scripps CO 2 program observations, at MLO and SPO instead of GV, yields a stronger dependence of the meridional gradient on FF emissions and a preindustrial MLO-SPO difference of -1.
, 2006). Although numerous land cover products exist, we chose SYNMAP due to its (1) reconciliation of multiple global land cover products, i. e. , Global Land Cover Characterization Database (GLCC)  (Hansen et al. , 2000; Loveland et al. , 2000) , GLC2000 (  2003 ) and the 2001 MODIS land cover product  (Friedl et al. , 2002) ; (2) global coverage at 1 km resolution; and (3) general definition of classes based on life form, leaf type and leaf longevity which allowed for simple crosswalks to plant functional types (PFTs) used in different TBMs. Generality was a key concern as PFT schemes used in TBMs vary widely. The SYNMAP product contains 47 land cover classes such that a PFT scheme for a given TBM is a subset of SYNMAP classes based on a crosswalk between the two different schemes. To provide annual maps of LULCC, LUH and SYNMAP were merged using a set of one-to-one and one-to-many mapping rules based on map intersection during their period of overlap, i.
Major crops The SYNMAP land cover map indicates which areas are predominantly crop but does not provide additional information about the crop types contained within each grid cell. This can be important when, for example, a C4 crop like maize dominates a grid that would normally be covered by C3 veg-  etation, and vice-versa. Some models make use of such additional information to implement crop specific algorithms that capture some aspects of crop physiology and management including planting and harvesting phenology, fertilizer applications, irrigation or tillage practices. We therefore identified and extracted the four globally significant crop types (maize, rice, soybean and wheat) from the  Monfreda et al. (2008)  global crop database. The original Monfreda global crop product is a detailed database of global agricultural practices and describes the areas and yields of 175 different individual crops in 2000 at a 5 min × 5 min (approximately 10 km × 10 km) spatial resolution.
Soil The Food and Agriculture Organization -United Nations Educational, Science and Cultural Organization (FAO-UNESCO) digitized soil map of the world  (FAO, 1971 (FAO, -1981 (FAO, , 1995 (FAO, , 2003)) , originally published in 1974, is commonly used in terrestrial biosphere modeling. Recently, however, significant improvements in soil mapping and databases of soil properties have led to a new generation of regional and global scale soil maps, such as the International Soil Reference and Information Centre (ISRIC) World Inventory of Soil Emission Potentials (ISRIC-WISE)  (Batjes, 2008)  and the harmonized world soil database (HWSD) (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2011). This new generation of soil products have increased details in the spatial distribution of soil types and more accurate characterizations of soil physical and chemical properties.
For MsTMIP, we selected and synthesized the HWSD v1. 1 for global simulations because it was the most recent global soil database that incorporates updated soil data from Europe, Africa, and China. However, in both the ISRIC-WISE and HWSD databases, soil information for North America is based on an outdated FAO-UNESCO soil map from the 1970s. Thus, even in the most updated global soil databases, North America is less reliable than the other regions due to the use of an obsolete database  (Batjes, 2005 ; FAO/IIASA/ISRIC/ISS-CAS/JRC, 2011). We therefore developed the Unified North American Soil Map (UNASM) by fusing the United States Department of Agriculture Natural Resources Conservation Services (USDA-NRCS) State Soil Geographic (STATSGO2) data set with both the soil landscapes of Canada (SLC) version 3.
Global soil: gridded HWSD The HWSD had been widely used as input for global-scale carbon cycle modeling and MIP activities (e. g. , ISI-MIP;  Warszawski et al. , 2013) , and therefore was used to define MsTMIP global soil data. The original HWSD is a 30 arcsec raster database with over 16 000 different soil mapping units that combines existing regional and national updates of the soil information worldwide, including the Soil and Terrain database (SOTER), European Soil Database (ESD), Soil Map of China, and WISE, with the information contained within the 1 : 5 000 000 scale FAO-UNESCO soil map of the world (FAO/IIASA/ISRIC/ISSCAS/JRC, 2011). Each soil mapping unit in the HWSD is composed of several different soil units (or soil types) defined by major soil group code following a combined FAO-74/FAO-85/FAO-90 soil classification system.
North American soil: Unified North American Soil Map (UNASM) A new gridded database of harmonized soil physical and chemical properties for North America was created for MsT-MIP by fusing the most recent regional soil information from US STATSGO2, SLC version 3. 2 and 2. 2, and the HWSD v1. 1. The fused database was then harmonized into two standardized soil layers as for the HWSD. The top soil layer ranges from 0 to 30 cm and the sub-soil layer ranges from 30 to 100 cm. The comparison with the subset of HWSD demonstrates the pronounced difference in the spatial distributions of soil properties and soil organic carbon mass between the UNASM and HWSD, but overall the UNASM provides more accurate and detailed information particularly in Alaska and central Canada. The methods used to develop the UNASM and the comparisons with HWSD are described in detail in  Liu et al.
Conclusions This paper presents the reasoning for, and a description of, driver data and spin-up procedures used in the setup of 11 Thematic Realtime Environmental Distributed Data Services (THREDDS), http://www. unidata. ucar. edu/software/thredds/ current/tds/ the global and North American simulations that are part of the MsTMIP activity. These data sets include climate, atmospheric CO 2 concentration, nitrogen deposition, LULCC, C3 / C4 grasses fraction, major crop, phenology, soil data and land-water mask information. In many cases, we found it necessary to develop new or enhanced data sets to serve the needs of long-term, high-resolution TBM simulations. In addition, the need for the data sets to be compatible with over 20 participating TBMs resulted in strict requirements for all data sets considered.
1 (Other) -gravel content a Native temporal periods of environmental driver data sets compiled for MsTMIP are extended to be compatible with the simulation time period (1801-2010) defined by MsTMIP. Please refer to Sect. 4. spin-up data package to see how data with shorter native temporal period are extended back to 1801 to address the needs of MsTMIP simulations. b CRU-NCEP: Climate Research Unit and National Centers for Environmental Prediction; NARR: North American Regional Reanalysis; SYNMAP: SYNergetic land cover MAP; GIMMSg: Global Inventory Monitoring and Modeling System version g; NDVI: normalized difference vegetation index; LAI: leaf area index; fPAR: fraction of photosynthetically active radiation; HWSD: Harmonized World Soil Database; STATSGO2: State Soil Geographic data version 2; SLC: Soil Landscapes of Canada.
DNAs from cell lines with a known TP53 status were used as controls for each exon (Supplementary Table  S5  ). Amplification reactions were carried out for 50 cycles to produce sufficient material for melt analysis. Conditions were as follows; one cycle at 95ºC for 15 minutes; 50 cycles at 95ºC for 10 seconds; annealing conditions (Supplementary Table  S6  ) for 5 or 20 seconds; and 72ºC for 20 seconds. The final elongation step was 72ºC for 7 min. Since no data are collected during amplification, the PCR cycling was done in a simple thermal cycler. The melt analysis (dissociation) was performed in the 7900HT Fast Real-Time PCR System (Applied Biosystems), an HRM-capable real-time PCR instrument. Conditions were as follows: 95ºC for 1 second, 72ºC for 90 seconds, and an HRM step rising from 72ºC to 95ºC at 0.
1ºC per second. The melt (dissociation) curves were analyzed using Applied Biosystems HRM Software v2. 0. The HRM products of all samples that appeared mutated or unclear in the dissociation curve analyses were subsequently sequenced. All mutated samples were confirmed by standard PCR followed by agarose gel electrophoresis and sequencing. Primers used for standard PCR were the same as for HRM. Amplification was as follows: 45 cycles of 30 sec at 95ºC; annealing conditions for 30 sec (Supplementary Table  S6  ); and one min at 72ºC.
In this paper, a dataset of geotagged photos on a world-wide scale is presented. The dataset contains a sample of more than 14 million geotagged photos crawled from Flickr with the corresponding metadata. To guarantee the spatial representativeness of the dataset, a crawling approach based on the small-world phenomena and the Flickr friendship's graph is applied. Furthermore, the noisiness of user-provided tags is reduced through an automatic tag cleaning approach. To enable efficient retrieval, photos in the dataset are indexed based on their location information using quad-tree data structure. The dataset can assists different applications, especially, search-based automatic image annotation and reverse geotagging 1.
Geotagged images provide an additional context for searchbased image annotation. The location information can be used to narrow the search space, thus, identifying the neighborhood of a to-be-annotated image can be done more efficiently (e. g. [17, 15] ). Furthermore, datasets of geotagged images can also assist the task identifying the location of non-geotagged ones. This process, called reverse geotagging, exploits the different features of community photos, such as textual metadata (tags), location information (geotags), and visual features to mine the location of an input image (e. g. [1, 8] ). To support the mentioned research directions, a dataset of geotagged photos with the associated metadata is presented in this paper. The dataset is obtained from Flickr by employing a crawling strategy based on the small-world phenomena  [14]  and Flickr friendship's graph to ensure the spatial representativeness of the collected data.
To improve the quality of the associated user-tags, a cleaning procedure is applied to remove noisy tags. Furthermore, to achieve efficient retrieval, the dataset is indexed based on the geographical information using the quad-tree data structure. The rest of the paper is organized as follows. In the next section, geo-based crawling techniques as well as a subset the most used geotagged image datasets are reviewed. Our data crawling strategy, the tag cleaning approach, the applied spatial indexing method as well as diverse statistics on the created dataset are presented in section 3. The work is then concluded in section 4.
BACKGROUND Creating photo datasets with the associated metadata from community contributed photos is an essential component of several research activities which aim at extracting new information from user-collective knowledge. In addition to the commonly available image metadata, such as user-tags and image titles, several efforts have been made to provide information about the location of image capture. This became feasible according to the increasing number of geotagged images shared on the web. Before we present our contribution in this regard, we discuss different strategies for creating image datasets based on geographical information and provide a compact report of the available datasets.
Geo-based Data Crawling Crawling image data from online collections has been the subject of several research efforts. The authors in  [12]  propose an approach to crawl geotagged photos based on keyword search. For this purpose, photo sharing services are first queried using keywords (e. g. city names). Next, all geotagged images annotated with that keywords are retrieved. The datasets presented in  [8, 11, 18, 9, 22]  have been created by using the geographic query feature provided by Flickr API. The quires are built based on the geographic boundaries of specific cities or urban centers. A first effort to build world-scale photo dataset was introduced in  [16]. For this purpose, the authors divide the world map into a grid of overlapping tiles. After that, the boundaries of each tile are used to query Flickr.
A world-scale photo dataset is also presented in  [3]. The authors propose a crawling strategy which aims at gathering photos from Flickr, so that the real spatial distribution of the data is preserved. That means, the density of photos collected from a given place should reflect the popularity of that place among photographers. The crawling method starts by randomly selecting a photo identifier from the pool of Flickr photo identifiers. Next, the uploader of that photo is identified and the corresponding geotagged photos are downloaded with the associated metadata. Additional photos are then acquired by traversing the friendship graph of the initial user to identify new users and downloading the corresponding geotagged photos. To crawl more data, the complete process is repeated by selecting a new photo identifier.
Geotagged Photo Datasets In the recent years, a number of photo datasets which provide location information (explicitly or implicitly) have been made available for research purposes. For the Photo Annotation and Retrieval Task, ImageCLEF initiative 4  provides a dataset based on MIRFlickr  [10]. It contains 1 million Flickr images with a subset of 25,000 manually annotated photos. MIRFlickr provides different kinds of metadata about the downloaded images, such as the EXIF files and the associated user-tags. However, by investigating the EXIF descriptors, we found out that location information are either missing or inaccurate for a large part of the photos in the dataset. NUS-Wide is another dataset based on Flickr  [2]. It consists of 269,648 images with the associated user-tags as well as six types of low-level image features.
Additionally, the dataset provide a ground-truth for 81 concepts. However, only a small part of the photos in the dataset are geotagged (around 50,000). Additional dataset of about 1 million photos was introduced in  [11]. The data were crawled from Flickr and correspond to 22 European cities. The dataset was extended in  [18]  to 40 world cities with a total of about 2,23 million images. However, these datasets provide only the photos without the associated metadata. The authors of  [22]  prov i d eas c r i p tf o rad a t a s e tc a l l e dParis500k. The dataset contains more than 500 thousands photos taken in the city of Paris. A further dataset with a main focus on reverse geotagging is presented by the MediaEval benchmarking initiative  5. The dataset, named MediaEval Placing Task 2013 Data Set  [7]  contains around nine million geotagged images crawled from Flickr.
OUR DATASET To ensure the quality of our dataset we defined the following criteria. First, the dataset should be big enough to  Additionally, the data should be spatially representative. That means, the density of the data corresponding to a certain location should reflect the popularity of that location among photographers. Another aspect is the quality of the provided metadata. An important resource for metadata is user-tags. However, user-tags are inherently noisy  [13]. Therefore, they must undergo a cleaning procedure before they can be used by further applications. The phases of creating our dataset according to the mentioned criteria are discuss in detail in the next subsections.
Compared to the map of Paris shown in Figure  2. a, we observe dense amounts of photos around touristic attractions, such as the city center, around Eiffel Tower and along the Seine River. We also compared our dataset to the findings of a study conducted by Crandall et al  [3]   of the user who uploaded the photo, the title of the photo (if existing), the list of associated user-tags, the location information represented by the longitude and the latitude, the accuracy level of the location information as defined by Flickr, the date of photo capture, the date when the photo was upload to Flickr server, and the information needed to construct the photo URL 7.
Tag Preprocessing Before dealing with syntactic problems of user-tags, a filtering step is applied to remove tags corresponding to stop words. For this purpose, we manually identified a list of stop words. This includes non-descriptive tags, such as the words photo, picture and the like. Another kind of stop words are tags referring to technical terms, such as camera types and camera settings (e. g. canon, longexposure, d40x). Furthermore, tags specific to Flickr, e. g. flickr. com, platinumheartaward, etc. and other tags referring to dates, web services or photo editing programs are also added to the stop word list. An additional refinement step is to filter tags with low frequency. Usually, tags that are used by a small number of users are noisy since they might be too specific. Accordingly, we eliminated tags which were used by less than 5 users from the dataset.
Indexing using To efficiently process geographic queries, the entries of the dataset have to be spatially indexed. For this purpose, we provide an approach for indexing large amounts of data using the quad-tree data structure  [6]. Quad-tree is a hierarchical data structure which is based on the principle of recursive decomposition. It is wildly used for indexing two dimensional data, such as geographical coordinates. For this purpose, data points are recursively divided into four regions until a stopping condition is met. This condition is defined in terms of the maximum allowed capacity of a single quad-tree region. With a large number of data points, a direct application of the quad-tree algorithm becomes impractical. Additionally, using a relatively low maximum capacity threshold leads to immense memory requirements due to the high recursion depth.
To deal with this problem, we propose a method for distributing the computation of the quad-tree. Initially, we dived the world map into tiles. A tile is created only if there are photos in the dataset taken in the area specified by that tile. After that, dense tiles are further divided into sub-tiles. This process is repeated as long as the number of photos in the tile exceeds a predefined upper bound (Figure  5 ). In the next step, the quad-tree algorithm is applied on each tile (Figure  6 ). The final index consists of the boundaries of each tile as well as the corresponding quad-tree regions. The boundaries of a region are defined by the coordinates, i. e. , longitude and latitude pairs, of the left bottom and the right top corners of the bounding box, respectively. To allow flexible retrieval, the index also keeps track of the neighborhood information of each quad-tree region.
This can be useful when a specific quad-tree region is sparse. Accordingly, additional data points can be efficiently retrieved by extending the result set to data points of neighboring quad-tree regions. We applied the described approach on our dataset using initial squared tiles of size 10 × 10. After that, the boundaries of each tile (the width and the height) are shrinked to the minimum possible rectangular area which contains the complete set of data points associated with the original tile. Next, tiles containing more than 300,000 photos were further divided. Figure  5  shows the results of this phase. The produced tiles show an approximation of the continents of the world. Additionally, we can see that tiles corresponding to areas of high photo density (e. g. parts of North America and Europe) are further divided into sub-tiles shown as smaller rectangles inside the corresponding tiles.
Finally, we applied the quad-tree algorithm on each tile using a maximum capacity threshold of 800 data points per a quad-tree region (Figure  6 ). We collected statistics about the generated tiles and the corresponding quad-trees. Indexing the collection of 14,1 million geographical coordinates resulted in 215 tiles with an average of 312 quad-tree regions per tile. Each tile contains about 65,500 data points (images) on average, however, with a large standard deviation of about 122,000. This due to the sharp differences in the density of photos from place to place. In fact, the density of photographed places follow the power law. There are very few places in the world which are frequently photographed, while quit large number of places are photographed much less (see Figure7). Figure  6 : Quad-tree regions for our dataset.
SUMMARY In this paper a dataset of geotagged images on worldwide scale is presented. The dataset contains a snapshot of Flicker of 14,1 million images with the corresponding metadata. The dataset can be used to assist research on  The representativeness of the data was achieved through a crawling approach based on Flickr friendship's graph. Additionally, the associated user-tags were cleaned to boost their utility. Finally, efficient retrieval can be performed using the provided spatial index which is based on the quad-tree data structure.