dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset The experimental corpus is derived from scholarly biomedical articles of PMC from which we extract figures and their corresponding captions. The collection is comprised of 184,614 image-caption pairs. This overall set is further split into disjunct training (164,614 pairs), validation (10,000 pairs) and test (10,000 pairs) sets. For the concept detection subtask, we used the QuickUMLS library  [21]  to identify the CUIs mentioned in the caption text.
Results The evaluation of both subtasks is conducted separately. For the concept detection task, we measured the balanced precision and recall trade-off in terms of F 1 scores. Python's scikit-learn (v0. 17. 1-2) library is used. We compute micro F 1 per image and average across all images. 393 reference captions in the test set do not contain any CUIs. The respective images are excluded from the evaluation. Caption prediction performance is assessed on the basis of BLEU scores  [19]  using the Python NLTK (v3. 2. 2) default implementation. Candidate captions are lower cased, stripped of all punctuation and English stop words. Finally, to increase coverage, we apply Snowball stemming. BLEU scores are computed per reference image, treating each entire caption as a sentence, even though it may contain multiple natural sentences.
Dataset For both subtasks 3D CT images were provided with a size of 512 × 512 pixels and number of slices varying from 50 to 400. All CT images were stored in NIFTI file format with. nii. gz file extension (g-zipped. nii files). This file format stores raw voxel intensities in Hounsfield units (HU) as well the corresponding image metadata such as image dimensions, voxel size in physical units, slice thickness, etc. For all patients automatically extracted masks of the lungs were provided. The dataset for the MDR subtask was composed of 209 MDR and 234 DS patients. The division of the data into training and test sets is shown in Table  7. The TBT task contained 800 patients divided as presented in Table  8. One 2D slice per TB type is shown in Figure  2.
Data Sets Used In this pilot task, participants had to estimate the population for different areas in two regions. To achieve this goal, organizers provided a set of satellite images (Copernicus Sentinel 2)  8. The boundaries of the areas of interest were provided as shape files. The clipped satellite images were provided as well as the meta data of the original images (before clipping). The data set consists of topographic and geographic information as follows: -ESRI (Environmental Systems Research Institute) shape files: there is a single shape file by region and the projected shape file of the region has the attributes to represent the various areas the region is composed of. -Sentinel-2 satellite images: The remote sensing imagery are from the Sentinel-2 platform. The imagery is multi spectral, cloud-free satellite imagery downloaded from Sentinel Data Hub 9.
Acknowledgements This research was supported in part by the  Intramural Research Program  of the  National Institutes of Health (NIH) ,  National Library of Medicine (NLM) , and  Lister Hill National Center for Biomedical Communications (LHNCBC). It is also partly supported European Union's  Horizon 2020 Research and Innovation programme  under the Grant Agreement n o  693210 (FabSpace 2. 0 ).
mammoth mitochondrial genomes, sampled from fossils recovered from across their Holarctic range. Our dataset includes 54 previously unpublished mitochondrial genomes and significantly increases the coverage of the Eurasian range of the species. The resulting global phylogeny confirms that the Late Pleistocene mammoth population comprised three distinct mitochondrial lineages that began to diverge ~1. 0-2. 0 million years ago (Ma). We also find that mammoth mitochondrial lineages were strongly geographically partitioned throughout the Pleistocene. In combination, our genetic results and the pattern of morphological variation in time and space suggest that male-mediated gene flow, rather than large-scale dispersals, was important in the Pleistocene evolutionary history of mammoths. Late Pleistocene mammoth remains are common across Eurasia and North America in temperate as well as high latitude areas  1, 2.
Methods Sample collection. Over several decades, we and collaborators have collected large numbers of mammoth remains from sites across Eurasia and North America. From these, we selected 63 to be included in this study, focusing on geographic regions that have been underrepresented in previous work (Fig. 1 ). When possible, we identified each of these to species level based on morphological analysis. We then generated complete mitochondrial genome sequences using one of or both approaches described below (Supplementary Materials). In addition, we obtained Accelerator Mass Spectrometry (AMS) radiocarbon dates for 30 samples for which no stratigraphic information was available. The ages of each specimen included in our global data set (either from radiocarbon or stratigraphic context) are provided in Supplementary Materials.
Data Generation Method 1: Multiplex PCR and pyrosequencing. For ten samples (Supplementary Materials), we generated complete mitochondrial genomes following the 2-step multiplex PCR  17  , PTS tagging  39  and Roche 454 pyrosequencing approach described in Rohland et al. 22. In brief, we divided 78 primer pairs that span the mammoth mitochondrial genome into two, non-overlapping pools of 39 primer pairs. We performed multiplex PCR with each primer pool twice to generate independent replicates for each sample. Products of the four multiplex PCR amplifications then served as respective templates for singleplex amplifications with each of the 78 primer pairs. We purified, quantified and pooled the amplified mitochondrial genome fragments in equal ratios, and built tagged/barcoded 454 sequencing libraries following Meyer et al.
39. After sequencing, we de-multiplexed sequences of each replicate of every sample and removed sequencing adapters. We mapped reads against a previously published mammoth mitochondrial genome (GenBank accession number EU153444)  20  using MIA  40. Then, we generated consensus sequences of the mitochondrial genome for each sample by retaining the majority base at each site from all reads of both replicates. We filled in gaps in the consensus sequences with additional PCR amplification and Sanger sequencing. As a positive control, we re-sequenced sample 10643 (Supplementary Materials) using the hybridization capture and Illumina high-throughput sequencing approach described below. Both approaches yielded identical consensus mitochondrial genome sequences for this sample. Data Generation Method 2: Enrichment via hybridization capture.
For the remaining 53 samples, we attempted to generate complete mitochondrial genomes using a hybridization capture approach. Following DNA extraction, we prepared barcoded Illumina sequencing libraries following a protocol modified from Meyer and Kircher 41 for highly degraded DNA templates  42. We enriched each barcoded library for mammoth mitochondrial sequences using an early version of the Agilent on-array hybridization capture approach  16. We designed 60 bp baits with 30 bp tiling from a published mammoth mitochondrial genome (GenBank accession number EU153444). Libraries of 54 Eurasian samples (53 unprocessed and one replicate as described above; marked with asterisks in Table  S1 ) were pooled and captured on Agilent capture arrays. Finally, we sequenced the enriched libraries on the GAIIx (2 × 76 bp) at the Max Planck Institute for Evolutionary Anthropology.
Next, we removed PCR duplicates in each BAM file with the script FilterUniqueBAM. py (available from https://github. com/Paleogenomics/DNA-Post-Processing. git). This script retains a single consensus sequence for reads with the same start and end coordinates. For samples for which multiple libraries and/or capture experiments had been performed, we combined mapped reads of the same sample with SAMtools merge  44  and removed duplicates with FilterUniqueBAM. py as described above. Generating consensus sequences with two filtering criteria. To control for damage-derived substitutions and sequencing error, we applied two sets of criteria to create consensus sequences and alignments from each filtered and de-duplicated library. Our relaxed alignment is generated using a less strict set of criteria for base calling and inclusion in the final sequence alignment: bases are called at sites with at least three non-duplicate reads and where the majority base is present with a frequency higher than 33% (at least two of every three bases in agreement).
Other bases are called as an N. In addition, only sequences for which no more than 33% of bases can be called as N are included in the alignment. The strict alignment was generated by requiring at least 10-fold coverage at each site and that the majority base is present in more than 90% of reads. To be included in the sequence alignment, no more than 20% of sites can be missing in the resulting consensus sequence. We used SAMtools mpileup and BCFtools to obtain coverage and variant information, and developed a script VCF2consensus. py (also available from https://github. com/Paleogenomics/DNA-Post-Processing. git) to generate consensus sequences with the two filtering criteria described above. We also included nine sequences that were obtained through multiplex PCR and pyrosequencing as described above into the two alignments.
We did not include the multiplex PCR results of sample 10643 because the mitochondrial genome of this sample was also generated with hybridization capture and has been included in each alignment. Data from other studies. We also included in our study 79 North American mammoth mitochondrial genomes that were generated recently 6 (Supplementary Materials). To provide consistency between these and our data, we obtained raw sequencing libraries from each of these 79 samples (GenBank accession numbers KX027489-KX027568), and used the read processing, de-multiplexing and filtering approaches described above to generate consensus sequences. Finally, we also downloaded 17 previously published complete mitochondrial genome sequences (GenBank accession numbers EU153445-EU153458, NC007596 and DQ316067; Supplementary Materials)  [18] [19] [20] [21].
These mitochondrial genomes were included as published in both the relaxed and strict data sets. Alignment, sequence partition and model selection. We aligned each data set using Seaview v4. 0  45  with the algorithm muscle -maxiters2 -diags, and adjusted alignments manually in Se-Al v2. 0  46. For both data sets, we created four sequence partitions: protein-coding genes, the control region, transfer RNA (tRNA) and ribosomal RNA (rRNA). We reverse-complemented genes in the reverse strand, and concatenated genes and regions of the same partition category with Biopython 47. We performed model selection for partitions of the control region, tRNA and rRNA separately in jModelTest 2. 1. 3  48 , selecting the best model for each partition from full models with gamma distribution (+ G), invariable sites (+ I) and equal/unequal base frequencies.
We inspected the combined results in Tracer v1. 6  58  , and determined convergence of each parameter, all of which had their ESS values larger than 200. We identified the Maximum Clade Credibility (MCC) tree in TreeAnnotator v1. 8. 0, and visualized and graphically edited the MCC tree using FigTree v1. 4. 0 59. The resulting phylogenies for the strict and relaxed data sets were similar both in topology and timescale (Figs  S1  and S2 ). It has been observed that estimated evolutionary rates can differ significantly depending on calibration approach, with faster rates estimated from relatively shallow timescales and slower rates estimated from deeper timescales. Based on the fossil record, tip-only calibrations are likely to be inappropriate for our data set  60, 61. We therefore performed additional analyses on the relaxed data set in which we incorporated both the radiocarbon-dated tips and a calibration point that lies much deeper within the tree to infer the evolutionary rate (the root-and-tip-dating method)  6, 62.
We did not perform this dating on the strict data set, which did not include the oldest (and least well-preserved) mammoth samples. We aligned the relaxed data set to the complete mitochondrial genome of an Asian elephant (GenBank accession number EF588275), and assumed a normally distributed divergence time between the Asian elephant and mammoths of 6. 7 million years with a standard deviation of 0. 5 million years  22. Model testing and BEAST analyses were performed as described above. As expected, rates estimated from the relaxed data set were approximately two times faster when only the tip calibration was used compared to the root-and-tip-dating approach (Fig. S3 ). To determine the clade and haplogroup designation of our new mammoth sequences, we downloaded partial mitochondrial sequences published previously (GenBank Accession numbers: FJ015093-FJ015152; KC427894-KC427981)  [3] [4] [5].
Acknowledgements We are grateful to the  Senckenberg Natural History Collections Dresden ,  Department of Geology , for the permission of sampling. We thank  Matthias Meyer  for assistance with DNA extraction and sequencing,  Enrique Baquedano  and  Diego J. Alvárez Lao  for providing us with information on the Spanish Mammoth sites, and  Javier Casado Hernández  for helping in the sampling of the bones curated at the  Museo de los Orígenes in Madrid. B. S. and D. C. were supported by grants to B. S. from the  Packard Foundation  and the  Gordon and Betty Moore Foundation. M. H. is supported by E. R. C. consolidator grant 310763 GeneFlow. Data generation was funded by the  Max-Planck-Society  and the  German Research Foundation  ( HO 3492/1-1 ). JE and HP were supported by an  NSERC  grant and a  Canada Research Chair.
Spectroscopy has been crucial for our understanding of physical and chemical phenomena. The interpretation of interstellar line spectra with radiative transfer calculations usually requires two kinds of molecular input data: spectroscopic data (such as energy levels, statistical weights, transition probabilities, etc. ) and collision data. This contribution describes how such data are collected, stored, and which limitations exist. Also, here we summarize challenges of atomic/molecular databases and point out our experiences, problems, etc. , which we are faced with. We present overview of future developments and needs in the areas of radiative transfer and molecular data.
Introduction Many fields in astronomy such as astrophysics, astrochemistry and astrobiology, depend on data for atomic and molecular (A + M) collision and radiative processes. Among these amount of data collections there are atomic and molecular processes and spectral regions that even today are poorly represented. Therefore, there is an urgent need to collect these data in the databases as well as to develop methods for improving the existing ones. Also, this require a joint effort both of scientists and IT software specialists to develop state-of-the-art infrastructures satisfying their needs, such as Virtual Laboratory  [1] [2] [3].
The Base Astrophysical Targets Nowadays, the data in in the field of astrophysics modeling are especially important and needed for simulations/calculations. For example the A + M data for hydrogen are important for development of atmosphere models of solar and near solar type stars and for radiative transport investigations as well as an understanding of the kinetics of stellar and other astrophysical plasmas  [4, 5]. Modern codes for stellar atmosphere modelling, like e. g. , PHOENIX (see e. g. ,  [6] [7] [8] ) require the knowledge of atomic data, so that the access to such atomic data, via online databases become very important. The helium A + M data are of interest particularly for helium-rich white dwarf atmospheres investigations  [9, 10]. Such data are also important in modelling early Universe chemistry (see the paper of  Coppola et al.
Database Description MolD database contains cross-sections for photodissociation processes  [22] , as well as corresponding data on molecular species and molecular state characterisations. MolD project is part of Serbian Virtual Observatory (SerVO) 1 and Virtual Atomic and Molecular Data Center (VAMDC) 2 (see Figure  2 ). MolD application is implemented as a customisation and extension of NodeSoftware provided by VAMDC. It complies to VAMDC interoperability standards and protocols for distributed remote queries. The underlying technology is Python-based, with Django as a Web framework  [23] , MySQL as a relational database system  [24]. The web application runs on Apache web server. The data model of MolD application is tailored to specifically suit the needs of the theoretical photodissociation data, and yet to easily map onto VAMDC's standardized XSAMS 3 (XML Schema for Atoms, Molecules and Solids format) schema for representation and exchange of atomic and molecular data 4.
• Via MolD homepage (http://servo. aob. rs/mold). There is an AJAX-enabled (Asynchronous JavaScript and XML) web form for data querying as well as calculating and plotting average thermal cross sections along available wavelengths for a given temperatiure. During 2017, MolD entered stage 3 of development. Currently, the database includes cross-section data for processes which involve species such as He + 2 , H + 2 , MgH + , HeH + , LiH + , NaH +. These processes are important for exploring of the interstellar medium, the early Universe chemistry as well as the modeling of different stellar and solar atmospheres (see papers  [11, 15, 16, 20, 22] ). Our plans include transition to new versions of Django framework and NodeSoftware, with ongoing incremental inclusion of A + M data from our papers.
Future Developments and Concluding Remarks Exploitation the full potential of A + M data and database services is an ongoing challenge in virtual data centers. There are still many limitations and problems that users are facing with such as poor documentation, missing of data evaluation, no open access, etc. The aim of MolD database is to be accessible, and be used by the wider scientific community, through VAMDC and to follow certain protocols and defined rules in order to eliminate such limitations and problems. The next step of development i. e. , the stage three of MolD development will be the implementation of possibility to fit the tabulated data. We plan to develop fitting formulas for photodissociation cross section as the function of the temperature and wavelength. Also, we intend to update the current database with newly calculated/measured data.
Acknowledgments: The authors are thankful to the  Ministry of Education, Science and Technological Development of the Republic of Serbia  for the support of this work within the projects  176002  and  III44002. This work has also been supported by the  VAMDC (Virtual Atomic and Molecular Data Centre ). VAMDC is funded under the  Combination of Collaborative Projects and Coordination and Support Actions Funding Scheme of The Seventh Framework Program. We acknowledge the contribution of late  A. A. Mihajlov  who collected and calculated much of the data that are presented within the MolD database.
A dedicated experimental test bench has been used to measure the travel time of the injected gas from the valve to the divertor subvolume, using a manifold with the same pipe length as envisaged for the actual ITER device and studying a wide range of gas mixtures and throughputs. Once the gas has arrived in the divertor, we use the existing database of SOLPS4. 3 simulations of the ITER divertor to estimate, for the case of neon as radiative species, the neon residence time, together with some new time-dependent SOLPS-ITER simulations to assess the time response of the divertor vertical target heat flux.
With these criteria in mind, we apply to the hot monoblock an additional transient surface heat flux. In the scenario cases presented here, the surface heat flux is ramped up in 500 msec, and increases to 40 and 80 MW •m -2 respectively, after which it is maintained constant at the new higher level, corresponding to a hypothetical "fast plasma reattachment event", of the kind which has been proposed in one theory of plasma detachment which relies on a bifurcation mechanism for the divertor target to switch between attached and detached states  [8]. We use the ANSYS © finite element software package to follow the temperature evolution of the monoblock and ascertain the time at which any of the two criteria above are met, starting from the beginning of the heat flux ramp. The results of these calculations are shown in Table  1.
Semi-static approach Because of the complexity and extensive CPU resources required by fully time-dependent SOLPS simulations, these can only be attempted for a very limited number of cases and some initial results will be presented in Section 4. 2. Useful information on the dynamical behaviour of impurities can, however, be extracted from the existing database of time independent plasma boundary simulations for all-metal walls compiled at the ITER Organization using the SOLPS4. 3 code for the case of Ne injection into the burning plasma SOL  [4]. The operational space for such scenarios in terms of peak target heat flux density, q pk (which can be on either inner or outer divertor vertical target but is usually on the outer target) and divertor neutral pressure, p n , measured at the entrance of the pumping duct, is shown in Fig.
Fully time-dependent simulations In order to more fully understand the behavior of the ITER divertor plasma under fast Ne injection, we concentrate on the case identified by the blue star in Figs. 4 5 6 7. This is the highest heat load case from the ITER database with Ne and P SOL = 100 MW, with q pk ∼ 16 MW/m 2 (in reality, due to divertor target shaping which is not accounted for in the 2D SOLPS simulations, this value could increase to as much as 23 MW/m 2  [13]  ). A set of four SOLPS-ITER time-dependent simulations has been established, using this static case at q pk = 16 MW/m 2 as their starting point, and adding to them a time-varying gas puffing source at the base of the divertor dome structure. The gas puff strengths in each of the four cases were chosen to follow the gas flows measured in the ITER GIS experimental test facility described in Section 3 , for the gas mixtures indicated in Table  2  , i.
e. the curves given in Fig. 3. Modifications were required to the SOLPS-ITER code to be able to allow for arbitrary (and continuous) time variation of the gas puff strength. Considering that the base case (star point in Figs. 4 5 6 7 ) already includes a Ne gas puff (in the upper port) of 1. 5 × 10 20 particles/s ( ∼0. 6 Pa m 3 /s), the Ne injection from the divertor therefore very quickly (a few msec only after the entrance of the first wave of gas into the divertor volume) becomes the dominant plasma Ne source. Similarly, the accompanying D 2 gas puff, in our simulation, which is 5, 10, or 20 times the Ne puff, depending on the case (cf. Table  2  and Fig. 3  ), is comparable to the gas puff strength from the upper port, which, for the base case scenario, is 5. 5 × 10 21 particles/s ( ∼21 Pa •m 3 /s).
Unfortunately, the nuclear nature of the ITER device imposes long pipe lengths ( ∼20 m) between actuator valves and gas injection points into the torus and this strongly limits the time response for heat flux control. Experimental tests, reported here, show that only if the gas injection system uses high throughput, along with gas mixtures employing hydrogenic carriers, can response times of order ∼0. 5 s be obtained. The existing database of steady state (time independent) divertor SOLPS4. 3 boundary plasma simulations with Ne injection into an all-metal ITER has been used to study the Ne residence time in the divertor, finding the approximate power law relation τ Ne ∝ p n -1. 2 , leading to rather short values ( ∼0. 1 s) at the high divertor neutral pressures ( ∼10 Pa) where ITER expects to operate at high fusion gain.