dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental settings and benchmark instances All the experiments have been performed on a computer with a 3. 40 gigahertz 8-core Intel Core i7-3770 processor and 16 giga-bytes RAM, running a 64-bit Linux operating system. The source codes were compiled with gcc 4. 8. 4 and -03 optimization flag. We used CPLEX 12. 9. 0 (called for brevity CPLEX in what follows) and its CALLABLE LIBRARIES framework to implement our branchand-cut algorithm. CPLEX was run in single-thread mode and all CPLEX parameters were set to their default values. In all tests we set a time limit of 600 seconds for each run. We consider four sets of benchmark instances including classical instances from the literature and synthetic graphs: •  Tang et al. (2016)  graphs. The instances by  Tang et al. (2016)  have been proposed to test the performance of an exact algorithm for interdiction problems with binary leader variables.
Precisely, we used μ ∈ { 1% , 2% ,. , 10% } plus μ ∈ { 15% , 20% } and we set k = μ • | E|. In this way, we created the ER graphs data set of 480 EICP instances. Note that also  Tang et al. (2016)  graphs are created using the ER graph generation procedure. However, in this paper we distinguish between  Tang et al. (2016)  graphs which have been used in the previous literature, and the newly generated (larger and more diversified) set of ER graphs. Similarly,  Fischetti et al. (2018)  tested their heuristics for generalized interdiction problems (including the EICP) on a family of ER graphs with n ∈ { 40 , 50 , 60 } and density p ∈ { 0 , 7 , 0. 9 }. These instances can be considered as very similar to the ones from our (much larger) ER graphs data set. • DIMACS_2-Coloring graphs. We considered 40 classical instances with 25 ≤ | V | < 150 and 75 ≤ | E| ≤ 7501 from the 2nd DIMACS challenge on Graph Coloring (see DIMACS2, 2017 ).
Two black circular targets on a white background were attached to the LVDT transducer at two points representing the gauge length of the sensor. This configuration allows the comparison between relative displacements measured by the LVDT and those determined by the optical system using the two targets. Two LED light sources were employed to assist in providing a homogenous illumination of the calibration plate during the calibration process. Before all tests, the left and right cameras must be calibrated. In order to obtain highly accurate calibration results and hence highly accurate 3D measurements, a standard calibration plate was used to perform the binocular stereo calibration. The calibration setup was carefully designed as described in (MVTech Software HALCON GmbH 2020). A 100 mm × 100 mm standard calibration platechosen according to the estimated dimensions of the region of interest to be captured as described in (MVTech Software HALCON GmbH 2020)was placed in front of the two cameras as can be seen in Figure  5 (a).
The plate images were acquired from the left and right cameras in 54 different positions and orientations relative to the stereo-vision system. Image coordinates of the calibration images were extracted and the parameters of the stereo setup were determined via a leastsquare solutionwhere suitably chosen camera parameters can be determined from the specification of the camera sensor and lens  (Hartley and Zisserman 2003). A software package was developed based on visual C++ 2010 with the use of libraries from the image processing software HALCON version 11. This software provides all the necessary tools required to perform the calibration of the binocular stereo system easily and accurately. Implemented in the Microsoft Foundation Classes (MFC), the graphical user interface (GUI) for this package provides the functions necessary to calibrate the binocular system and to record the image data during testing.
02 mm/s crosshead displacement rate of the testing machine. The LVDT sensor and the calibrated binocular stereo system were both employed to monitor displacements between two points over the gauge length (8 cm) on the glulam sample during the compressive loading. An initial pair of stereo images were captured by the two cameras before applying loads to the timber sample to allow comparison with the subsequent data. During load application, the load and relative deformationwhich was measured from the LVDTwere recorded through the data acquisition system. The GUI of the implemented software was used to automate recording the stereo images of the surface of the glulam sample with a time interval of about 1 s.
The deflection of the sample, rate of loading, and the maximum load to be applied to the system were then determined. A load of 12 kN was found to be appropriate to load the sample in the elastic range. The timber samples were tested based on these measurement data. Before applying the actual load, a pre-load with a force of 1 kN was first applied to the timber test sample to enable it to be seated properly. The sample was then efficiently held to allow the two cameras to capture the initial state of the sample and the LVDTs were initialized to zero displacements through the data acquisition system. Using a displacement control of 0. 02 mm/s, the samples were loaded constantly throughout the test. Loading and displacements were recorded simultaneously using a load cell and LVDT sensors connected to a data acquisition system at 0.
The algorithm developed for the photogrammetric deflection measurements To find the 3D information from the stereo images recorded during testing, it is necessary to utilize an image processing algorithm and stereo vision software. HALCON, a comprehensive and powerful software, provides the operators for imagebased applications with image processing libraries and a graphical user interface optimized for usability. As shown in Figure  9 , an image processing algorithm was implemented in this software to process the stereo images. As well as determining the presence of targets in each camera's image of the timber sample, the algorithm determines the 3D displacements of the circular targets to obtain their relative displacements from the stereo images of the timber sample recorded during testing.
These 3D data will then be used to evaluate the performance of LVDT and to collect the sample displacements at various locations as previously mentioned. The displacement measurements from these targets captured in stereo images involved several steps during image processing. The main steps involved in the algorithm are illustrated in Figure  10. Figure  11  illustrates the concept and steps involved in the algorithm. The process starts with reading the calibration files containing the internal and external parameters of the binocular stereo system that were recorded during the calibration process. The stereo images of the timber sample recorded from the left and right cameras during testing were then displayed to be used in the image recognition process. The sub-pixel-precise segmentation method provided by the software was used to reduce the area of interest by using the threshold values of the pixels to correspond to targets, in order to separate the different regions and determine the black regions.
The third column data, indicated by the blue colour, represents the maximum displacement of the targets attached directly to the LVDT transducer whereas the column data, indicated by the red and green colour, represent, respectively, the maximum displacement of the targets placed on the steel bracket and plate of the mounting system. They represent the holder's motion data. Finally, the last column data represents the maximum displacement measured by the LVDT transducer. The top bracket, which was close to the loading plate, had a higher displacement (0. 054 mm) than that of the bottom plate (0. 007 mm), which was far from the loading plate. If the motion data of the transducer holder are added to the transducer data, this results in a displacement similar to that of the timber. These results show that the transducer results are not reliable since the setup is not stable due to the motion of the mounting system during the compressive test perpendicular to the grain.
austraits, a curated plant trait database for the Australian flora We introduce the austraits database -a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e. g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e. g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual-and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.
One such region where trait data is sparsely documented is Australia; a continent with a flora of c. 28,900 native vascular plant taxa  13  (including species, subspecies, varietas and forma). While significant investment has been made in curating and digitising herbarium collections and observation records in Australia over the last two decades (e. g. The Australian Virtual Herbarium houses ~7 million specimen occurrence records; https://avh. ala. org. au), no complementary resource yet exists for consolidating information on plant traits. Moreover, relatively few Australian species are represented in the leading global databases. For example, the international TRY database  12  has measurements for only 3830 Australian species across all collated traits. This level of species coverage limits our ability to use traits to understand and ultimately manage Australian vegetation  14.
While initiatives such as TRY  12  and the Open Traits Network  15  are working towards global synthesis of trait data, a stronger representation of Australian plant taxa in these efforts is essential, especially given the high richness and endemicity of this continental flora, and the unique contribution this makes to global floral diversity  16, 17. Here we introduce the AusTraits database (hereafter AusTraits), a compilation of plant traits for the Australian flora. Currently, AusTraits draws together 283 distinct sources and contains 997,808 measurements spread across 448 different traits for 28,640 taxa. To assemble AusTraits from diverse primary sources and make data available for reuse, we needed to overcome three main types of challenges (Fig. 1 ): (1) Accessing data from diverse original sources, including field studies, online databases, scientific articles, and published taxonomic floras; (2)  Harmonising these diverse sources into a federated resource, with common taxon names, units, trait names, and data formats; and (3) Distributing versions of the data under suitable license.
To meet this challenge, we developed a workflow which draws on emerging community standards and our collective experience building trait databases. By providing a harmonised and curated dataset on 448 plant traits, AusTraits contributes substantially to filling the gap in Australian and global biodiversity resources. Prior to the development of AusTraits, data on Australian plant traits existed largely as a series of disconnected datasets collected by individual laboratories or initiatives. AusTraits has been developed as a standalone database, rather than as part of the existing global database TRY  12  , for three reasons. First, we sought to establish an engaged and localised community, actively collaborating to enhance coverage of plant trait data within Australia. We envisioned that a community would form more readily to fill gaps in national knowledge of traits with local ownership of the resource.
While we will never have a counterfactual, a vibrant community excited to be part of this initiative has indeed been established and coverage is much higher for Australian species than has been achieved since TRY's inception. Local ownership also aligns well with funding opportunities and national research priorities, and enables database coordinators to progress at their own speed. Second, we wanted to apply an entirely open-source approach to the aggregation workflow. All the code and raw files used to create the compiled database are available, and this database is freely available via a third party data repository (Zenodo) which is itself built for long term data archiving, with an established API. Finally, we targeted primary data sources, where possible, whereas TRY accepts aggregated datasets.
The hope was that this would increase data quality, by removing intermediaries and easier identification of duplicates. While independent, the overall structure of AusTraits is similar to that of TRY, ensuring the two databases will be interoperable. Both databases are founded on similar principles and terminology  18, 19. Increasingly, researchers and biodiversity portals are seeking to connect diverse datasets  15  , which is possible if they share a common foundation. We envision AusTraits as an on-going collaborative initiative for easily archiving and sharing trait data about the Australian flora. Open access to a comprehensive resource like this will generate significant new knowledge about the Australian flora across multiple scales of interest, as well as reduce duplication of effort in the compilation of plant trait data, particularly for research students and government agencies seeking to access information on traits.
Trait definitions. A full list of traits and their sources appears in Supplementary Table  1. The li t of sources in AusTraits was developed gradually as new datasets were incorporated, drawing from original source publications and a published thesaurus of plant characteristics  19. We categorised traits based on the tissue where it is measured (bark, leaf, reproductive, root, stem, whole plant) and the type of measurement (allocation, life history, morphology, nutrient, physiological). Version 3. 0. 2 of AusTraits includes 358 numeric and 90 categorical traits. Database structure. The schema of AusTraits broadly follows the principles of the established Observation and Measurement Ontology  18  in that, where available, trait data are connected to contextual information about the collection (e.
g. location coordinates, light levels, whether data were collected in the field or lab) and information about the methods used to derive measurements (e. g. number of replicates, equipment used). The database Fig. 1  The data curation pathway used to assemble the AusTraits database. Trait measurements are accessed from original data sources, including published floras and field campaigns. Features such as variable names, units and taxonomy are harmonised to a common standard. Versioned releases are distributed to users, allowing the dataset to be used and re-used in a reproducible way. contains 11 elements, as described in Table  1. This format was developed to include information about the trait measurements, taxon, methods, sites, contextual information, people involved, and citation sources.
For storage efficiency, the main table of traits contains relatively little information (Table  2 ), but can be cross linked against other tables (Tables  3 4 5 6 7 8 ) using identifiers for dataset, site, context, observation, and taxon (Table  1 ). The dataset_id is ordinarily the surname of the first author and year of publication associated with the source's primary citation (e. g. Blackman_2014). Trait values were also recorded as being one of several possible value types (value_type) (Table  9 ), reflecting the type of measurement submitted by the contributor, as different sources provide different levels of detail. Possible values include raw_value, individual_ mean, site_mean, multisite_mean, expert_mean, experiment_mean. Further details on the methods used for collecting each trait are provided in a methods table  (Table 5 ).
The second file, metadata. yml, contains relevant metadata for the study, as well as options for mapping trait names and units onto standard types, and any substitutions applied to the data in processing. These two files provide all the information needed to compile each study into  a standardised AusTraits format. Successive versions of AusTraits iterate through the steps in Fig. 1 , to incorporate new data and correct identified errors, leading to a high-quality, harmonised dataset. After importing a study, we generated a detailed report which summarised the study's metadata and compared the study's data values to those collected by other studies for the same traits. Data for continuous and categorical variables are presented in scatter plots and tables respectively. These reports allow first the AusTraits data curator, followed by the data contributor, to rapidly scan the metadata to confirm it has been entered correctly and the trait data to ensure it has been assigned the correct units and their categorical traits values are properly aligned with AusTraits trait values.
Names were aligned without name authorities, as we found this information was rarely reported in the raw datasets provided to us. Second, we used the aligned name to update any outdated names to their current accepted name, using the information provided in the APC. If a name was recorded as being both an accepted name and an alternative (e. g. synonym) we preferred the accepted name, but also noted the alternative records. For phrase names, when a suitable match could not be found, we manually reviewed near matches via web portals such as the Atlas of Living Australia to find a suitable match. The final resource reports both the original and the updated taxon name alongside each trait record (Table  2 ), as well as an additional table summarising all taxonomic name changes (Table  6 ) and further information from the APC and APNI on all taxa included (Table  7 ).
Data records Access. Static versions of AusTraits, including version 3. 0. 2 used in this descriptor, are available via Zenodo  363. Data is released under a CC-BY license enabling reuse with attribution -being a citation of this descriptor and, where possible, original sources. Deposition within Zenodo helps makes the dataset consistent with FAIR principles  364. As an evolving data product, successive versions of AusTraits are being released, containing updates and corrections. Versions are labeled using semantic versioning to indicate the change between versions  365. As   validation (see Technical Validation, below) and data entry are ongoing, users are recommended to pull data from release, to ensure results in their downstream analyses remain consistent as the database is updated. The R package austraits (https://github.
Data coverage. The number of accepted vascular plant taxa in the APC (as of May 2020) is around 28,981  13. Version 3. 0. 2 of AusTraits includes at least one record for 26,852 taxa (~93% of known taxa). Five traits (leaf_ length, leaf_width, plant_height, life_history, plant_growth_form) have records for more than 50% of known species (Fig. 2a ). Across all traits, the median number of taxa with records is 62. Supplementary Table  1  shows the number of studies, taxa, and families with data in AusTraits, as well as the number of geo-referenced records, for each trait. Looking across traits and tissue categories, coverage declined gradually, with moderate   coverage(>20%) for more than 50 traits (Fig. 2 ). Coverage for root, stem and bark traits declined much faster than trait measurements for other plant tissues (Fig.
5 ), though there are some notable exceptions. One exception is for root traits, where taxa within Poaceae have large amounts of information available relative to other plant families. A cluster of taxa within the family Myrtaceae which are largely from Western Australia have little leaf information available. Comparing coverage in AusTraits to the global database TRY, there were 76 traits overlapping. Of these, AusTraits tended to contain records for more taxa, but not always; multiple traits had more than 10 times the number of taxa represented in AusTraits (Fig. 6 ). However, there were more records in TRY for 25 traits, in particular physiological leaf traits. Many traits were not overlapping between the two databases (Fig. 6 ). We noted that AusTraits includes more seed and fruit nutrient data; possibly reflecting the interest in Australia in understanding how fruit and seeds are provisioned in nutrient-depauperate environments.
technical Validation We implemented three strategies to maintain data quality. First, we conducted a detailed review of each source based on a bespoke report, showing all data and metadata, by both an AusTraits curator (primarily Wenk) and the original contributor (where possible). Measurements for each trait were plotted against all other values for the trait in AusTraits, allowing quick identification of outliers. Corrections suggested by contributors were combined back into AusTraits and made available with the next release. Version 3. 0. 2 of AusTraits, described here, is the sixth release. Second, we implemented automated tests for each dataset, to confirm that values for continuous traits fall within the accepted range for the trait, and that values for categorical traits are on a list of allowed values.
Data that did not pass these tests were moved to a separate spreadsheet ("excluded_data") that is also made available for use and review. Third, we provide a pathway for user feedback. AusTraits is an open-source community resource and we encourage engagement from users on maintaining the quality and usability of the dataset. As such, we welcome reporting of possible errors, as well as additions and edits to the online documentation for AusTraits that make using the existing data, or adding new data, easier for the community. Feedback can be posted as an issue directly at the project's GitHub page (http://traitecoevo. github. io/austraits. build).
Usage Notes Each data release is available in multiple formats: first, as a compressed folder containing text files for each of the main components, second, as a compressed R object, enabling easy loading into R for those using that platform. Using the taxon names aligned with the APC, data can be queried against location data from the Atlas of Living Australia. To create the phylogenetic tree in Fig. 6 , we pruned a master tree for all higher plants 366 using the package V. PhyloMaker 367 and visualising via ggtree 368. To create Fig. 3a , we used the package plotbiomes  369  to create the baseline plot of biomes. Fig. 6  The number of taxa with trait records in AusTraits and global TRY database (accessed 28 May 2020). Each point shows a separate trait.
minimum of measurements on multiple individuals of the taxon at a single site site_mean Value is the mean or median of measurements on multiple individuals of the taxon at a single site site_max Value is the maximum of measurements on multiple individuals of the taxon at a single site multisite_min Value is the minimum of measurements on multiple individuals of the taxon across multiple sites multisite_mean Value is the mean or median of measurements on multiple individuals of the taxon across multiple sites multisite_max Value is the maximum of measurements on multiple individuals of the taxon across multiple sites expert_min Value is the minimum observed for a taxon across its range or in this particular dataset, as estimated by an expert based on their knowledge of the taxon. Data fitting this category include estimates from floras that represent a taxon's entire range.