dataesr/scientific-paragraphs-categorization huggingface dataset:
OPTIMADE::Filter OPTIMADE::Filter is a Perl library for the syntactical analysis of the OPTIMADE filter language. Apart from the construction of abstract syntax trees, the library can translate simple filter strings to SQL queries. The Git repository with the source code is publicly available on GitHub (https://github. com/Materials-Consortia/OPTIMADE-Filter). pymatgen optimade module pymatgen  26  (https://pymatgen. org) is a Python library for materials science. A user-friendly OptimadeRester client has been added to a new OPTIMADE module within pymatgen to provide a way to query OPTIMADE structure resources in a way familiar to existing users of pymatgen and the Materials Project API. The Git repository with the source code is publicly available on GitHub (https://github. com/materialsproject/pymatgen).
Summary The latest OPTIMADE API specification v1. 0 10 offers holistic access to many leading crystal structure databases, namely: AFLOW, COD, TCOD, Materials Cloud, Materials Project, NOMAD, odbx, Open Materials Database (omdb), and OQMD. Open client implementations are also available (https://optimade. science, https://materialscloud. org/optimadeclient) that enable aggregated searches over many databases as well as user-friendly graphical widgets that can create an OPTIMADE filter to empower the user with even easier access to data. OPTIMADE provides researchers easy access to over 10,000,000 results for different materials, providing benchmarking opportunities and offering a huge opportunity for high-throughput screening and machine learning studies. The ability of the OPTIMADE API to search databases, expose links between databases, and deliver standardized results makes it well-positioned to significantly enhance the impact and permeability of pre-existing data silos.
Acknowledgements The authors acknowledge support from  CECAM in Lausanne (Switzerland)  and the  Lorentz Center in Leiden (Netherlands)  for hosting OPTIMADE workshops. CWA, SK, NM, GPi, MU, LT acknowledge financial support by the  MARVEL National Centre of Competence in Research  funded by the  Swiss National Science Foundation  (grant agreement ID  51NF40-182892 ), the  European Centre of Excellence MaX "Materials design at the Exascale"  (grant no. 824143 ) and by the the " MARKETPLACE "  H2020  project (grant agreement  ID 760173 ). RAr acknowledges financial support from the  Swedish e-Science Research Centre (SeRC)  and the  Swedish Research Council (VR)  project no. 2016-04810  and  2020-05402. RAv has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement No.
654360  NFFA-EUROPE. GJC would like to acknowledge financial support from the  Royal Society. SD, DWi, MWu, MH, PH, KP acknowledge financial support by the  U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division  under Contract No. DE-AC02-05-CH11231  ( Materials Project  program  KC23MP ). CD, MLE, GH, GMR,  MSchef, MSchei , and DWa acknowledge support from the  European Union 's  Horizon 2020 research and innovation program  under the European Union's Grant agreement No. 951786  ( NOMAD CoE ). MLE acknowledges the  EPSRC CDT in Computational Methods for Materials Science  for funding under grant number  EP/L015552/1. AG, ML, VH, and CW acknowledges support by  Toyota Research Institute , and the award  70NANB14H012  from  U.
S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). MGo acknowledges support by  MICCoM , as part of the  Computational Materials Sciences Program  funded by the  U. S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division. SG, AMe, AV were supported by funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 689868  (SOLSA). AJM acknowledges networking support via the  EPSRC Collaborative Computational Projects ,  CCP9  ( EP/M022595/1 ) and  CCP-NC  ( EP/T026642/1 ) and the  EPSRC High-End Computing Consorium, UKCP  ( EP/P022561/1 ). GMR acknowledges support from the  F. R. S. -FNRS. MSchei acknowledges support from the  European Union 's  Horizon 2020 research and innovation program  under the European Union's Grant agreement No.
An excerpt of the JSON response for navigable pagination links to additional conformant materials that match the query. "links": { 685 "base_url": → "http://www. crystallography. net/tcod/optimade/v1. 0. 0/", 686 "first": { 687 "href": "http://www. crystallography. net/tcod/optimade/v1. 0. 0 → /structures?page_limit=10&filter=elements HAS ANY → \"C\",\"Si\",\"Ge\",\"Sn\",\"Pb\"" 688 }, 689 "next": { 690 "href": "http://www. crystallography. net/tcod/optimade/v1. 0. 0 → /structures?page_limit=10&page_offset=10&filter=elements → HAS ANY \"C\",\"Si\",\"Ge\",\"Sn\",\"Pb\"" 691 } 692 }, Box 4. 713 5/12 693"meta": { 694 "api_version": "1. 0. 0", 695 "data_available": 2906, 696 "data_returned": 2631, 697 "implementation": { 698 "maintainer": { 699 "email": "cod-bugs@ibt. lt" 700 }, 701 "name": "Theoretical Crystallography Open Database", 702 "source_url": "svn://www.
crystallography. net/cod/tags/tcod /v0. 2. 1/cgi-bin/optimade. pl@261229", → 703 "version": "v0. 2. 1" 704 }, 705 "last_id": "10000031", 706 "more_data_available": true, 707 "provider": { 708 "description": "Theoretical Crystallography Open Database", 709 "name": "Theoretical Crystallography Open Database", 710 "prefix": "tcod" 711 }, 712 "query": {.
Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise study sample differences contributing to differences in WMH variations across studies.
We also present a parser to harmonise WMHrelevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data.
Introduction The increasing availability of brain MRI datasets through multicentre studies, consortia, and data sharing platforms, along with the increased power of computational resources, allows for the possibil-tant issue of ensuring that measures are consistent across datasets. The process of harmonisation aims to remove non-biological variability related to the measurement process, while preserving the biological and especially the clinically-relevant variability present in the data. In this work we aimed to combine different harmonisation approaches to develop a harmonisation pipeline for MRI-derived measures of white matter hyperintensities (WMHs) of presumed vascular origin  ( Wardlaw et al. , 2013 )  on two large datasets related to healthy ageing that are part of the Dementias Platform UK  ( Bauermeister et al.
, 2020 ) : the Whitehall II imaging sub-study (WH)  ( Filippini et al. , 2014 )  and the UK Biobank (UKB)  ( Miller et al. , 2016 ). For the WH study, two MRI scanners were used. A second MRI scanner was included due to a scanner upgrade which took place two-thirds of the way into the study  ( Zsoldos et al. , 2020 ). The UKB is also focused on the ageing population, but used a different sample demographic, scanner, protocol, and set of non-imaging variables (demographic, cognitive and physiological) compared to WH. Our goal was to find the best combination of processing approaches to minimise non-biological variability in WMH measures extracted from these datasets. This would help providing a comprehensive protocol to successfully reduce biases and promote data integration. The importance of characterising ageing-associated vascular damage is increasingly recognised, since vascular disease contributes to more than half of dementia cases, often in conjunction with Alzheimer's disease pathology  ( Arvanitakis et al.
, 2016 ; Debette et al. , 2010 ). Among the signs of cerebral small vessel disease (SVD), WMHs are one of the most commonly evaluated, but their underlying pathology and clinical impact on cognition is still poorly understood  ( Wardlaw et al. , 2013 ) , and possibly affected by age  ( Zamboni et al. , 2019 ). An ability to combine datasets would give additional insight on the relationships between WMHs, its risk factors and clinical outcomes. It would not only improve statistical power, but also enable complementary information from datasets to be merged. For example, WH includes detailed longitudinal cognitive and behavioural assessments that are of great importance for understanding dementia pathology. On the other hand, the UKB dataset has a larger sample size, wider age range and more even gender balance than WH, providing data on a wider segment of the population.
An ability to integrate WMH data across these two datasets would combine their strengths and lead to novel insights into the prognostic value of WMHs. While many harmonisation approaches have been developed and tested on T1-weighted (e. g. Fortin et al. , 2018 ; Zandifar et al. , 2018 )  and diffusion MRI (e. g. Fortin et al. , 2017 ; Mirzaalian et al. , 2016 ) , studies evaluating harmonisation approaches for T2-weighted scans and the quantification of WMHs (and other lesions) are still lacking, despite the recognition that biases are also present in this modality  ( Shinohara et al. , 2017 ; Guo et al. , 2019 ). Consortia and working groups  ( Wardlaw et al. , 2013 ; Smith et al. , 2019 )  recognised the need to standardise the assessment of cerebral SVD and proposed a set of standard definitions, acquisition protocols and a framework for developing neuroimaging biomarkers of the condition.
The HARmoNising Brain Imaging MEth-odS for VaScular Contributions to Neurodegeneration (HARNESS) initiative ( https://harness-neuroimaging. org ) also provides web-based repositories of protocols, software tools and rating scales to facilitate multicentre research. While all these resources contribute to more standardised assessment of WMHs, what is still currently lacking is a way to make quantitative measures truly consistent. The datasets we selected for this study allow us to test retrospective (i. e. after data collection) harmonisation strategies in the presence (WH scanner upgrade) and absence (WH-UKB) of prospective (i. e. prior to data collection) harmonisation. Harmonised acquisition protocols are commonly employed in consortia and multi-centre studies  ( Jack et al. , 2008 ; Potvin et al.
, 2019 )  to facilitate future integration or comparison of data, with agreement on collection procedures and common measures prior to data collection. However, even after careful protocol harmonisation, systematic differences in images across sites can remain (related to scanner vendor, model, non-linearity of imaging gradients, magnetic field homogeneity, signal-to-noise ratio etc. ) and lead to bias in the MRI-derived measures  ( Kruggel et al. , 2010 ; Potvin et al. , 2019 ; Shinohara et al. , 2017 ; Mirzaalian et al. , 2016 ; Guo et al. , 2019 ). At the image pre-processing level, harmonisation strategies aim to directly remove the non-biological variability in the images  ( Mirzaalian et al. , 2016 ; Dewey et al. , 2018 )  and provide processing procedures that ensure well-matched measures and consistent performance across datasets  ( Erus et al.
, 2018 ; Zandifar et al. , 2018 ; Guo et al. , 2019 ). At the analysis level, harmonisation approaches may further standardise measures derived across datasets and account for differences in the samples across studies, to ensure that characteristics of imaging site and study do not bias analyses  ( Fortin et al. , 2017 ; Fortin et al. , 2018 ; Pomponio et al. , 2020 ). Despite rapid progress, MRI data harmonisation remains a challenge because of the many sources that may drive variability in MR measurements across datasets. Due to the different nature of the biases involved, a single strategy is unlikely to achieve successfully harmonised data  ( Wachinger et al. , 2019 ; Glocker et al. , 2019 ). With this study we aim to combine manifold approaches to overcome such limitation. A key element of the present work involved increasing the robustness of FSL-BIANCA, a supervised classification method for segmenting WMHs  ( Griffanti et al.
, 2016 ). Briefly, BIANCA classifies the image voxels based on their intensity and spatial features using the k-nearest neighbour (k-NN) algorithm. The intensity features used by BIANCA can be extracted from multiple MRI modalities, making it a very versatile tool. Being a supervised method, it needs examples of manually segmented WMHs for training the algorithm. The output image represents the probability of each voxel being a WMH. This image can then be thresholded to obtain the final binary mask representing the WMHs (see  Griffanti et al. , 2016  for further details). BIANCA has been tested on vascular, neurodegenerative and healthy populations. It achieved excellent performance scores with respect to manual annotation and visual rating. It is registered among the software tools on the HARNESS initiative website ( https://software.
harness-neuroimaging. org/harnesssoftware-catalog/bianca. html ). We assessed the effects of a variety processing choices on the harmonisation of the identification of WMHs. As different datasets will often have different imaging modalities available, we determined the effects of using different combinations of modalities to train BIANCA. As the rater employed to perform the manual segmentations used to train BIANCA will typically be different across studies, we assessed impact of the choice of rater, and the effects of training BIANCA on individual datasets or on combined data. We also assessed the effect of using bias field correction, as the distribution of radio frequency field (RF) inhomogeneities is unique to each scanner. We first explored the impact of these processing choices on the harmonisation of WMHs measured across a scanner change, using the WH data.
Datasets The datasets we used in this work are WH and UKB. The first, described in  Filippini et al. , 2014  , is part of a large longitudinal study, namely the Whitehall II Study, that explores the social determinants of health. It involves a sample of British civil servants (age range 60-85 years) who were first recruited in 1985 and participated in a number of phases of clinical/cognitive assessment. Seven hundred and seventy-four participants were selected randomly to receive multimodal brain MRI scans and a detailed cognitive battery at the Oxford Centre for Functional MRI of the Brain (FMRIB) as part of the Imag-ing  Sub-study (2012 Sub-study ( -2016)). Out of those, we excluded 18 participants with evident brain abnormalities other than WMHs (e. g. tumour, stroke, multiple sclerosis), 17 due to poor quality of the available MRI scans or lack of some of the MRI contrast of interest, and 26 with missing data in the non-imaging variables of interest (details below).
The second dataset is the UKB imaging study, a sub-study of the UKB prospective epidemiological study gathering extensive questionnaires, physical and cognitive measures, and biological samples from predominantly healthy participants. The project imaging component  Miller et al. , 2016  , currently ongoing, aims to collect detailed diagnostic MRI scans from 100,000 UKB participants. The sample available at the time of our work included 14,503 subjects with scans released by January 2019 (age range 46-80 years). Out of those with available MRI data, we selected 2,295 participants who had no missing data in the non-imaging variables of interest (details below). This allowed us to avoid performing data imputation, which could have introduced an additional source of variability, while retaining a large number to focus on the methodological goal of imaging data harmonisation.
Ten further participants were excluded due to other brain abnormalities. The resulting UKB dataset was therefore composed of 2,285 participants. Non-imaging variables -In order to model the biological variability in WMH measures across datasets, we selected non-imaging variables with a potential link to WMHs. An example of such variables is age. It is one of the most important risk factor for WMHs, and WH and UKB have only partially overlapping age ranges (WH: 60-85 years; UKB: 46-80 years). Therefore, we considered particularly important to take it into account as source of biological variability when comparing measures of WMHs across datasets. A total of 33 variables, including demographic, clinical and cognitive factors were selected among those available for the WH dataset. Subsequently, when performing harmonisation between the WH and UKB datasets, we excluded 4 variables due to lack of availability for all participants within the UKB cohort, or due to substantial differences in the data collection across the two datasets (e.
g. the design or administration of certain cognitive tests). The full list of non-imaging variables selected for both datasets is presented in Table  1. MRI data acquisition -Acquisition details for the datasets involved in our analysis are listed in Table  2. For the WH study, two MRI scanners were used, due to the scanner upgrade two-thirds of the way through the study: a 3T Siemens Magnetom Verio scanner (SC1) with a 32-channel receive head coil (April 2012-December 2014) and a 3T Siemens Prisma scanner (SC2) with a 64-channel receive head-neck coil in the same centre (July 2015-December 2016). The MRI modalities used for WMH segmentation were Fluid Attenuated Inversion Recovery (FLAIR) scans, T1-weighed scans and diffusion-weighted scans (dMRI), to derive Fractional Anisotropy (FA) maps.
The MRI sequence parameters were either identical or closely matched between the two scanners. For the UKB dataset, MRI acquisition was carried out using a 3T Siemens Skyra with a 32-channel receive head coil (full details in  Miller et al. , 2016 ). As regards the MRI modalities, for the current study we used FLAIR scans and T1-weighed scans. We decided not to include dMRI within the WMH quantification pipeline, because the requirement to have 3 usable MRI modalities for each subject would have caused the exclusion of a small, yet significant amount of data (see  Alfaro-Almagro et al. , 2018  for an indication of usable data for each modality). In fact, currently released measures of WMHs for UKB are extracted using T1-weighted and FLAIR only. Moreover, unlike T1-weighted and FLAIR scans, dMRI with 6 or more directions (needed to perform Diffusion Tensor Imaging and generate FA maps) are not very common in clinical contexts.
We performed a preliminary analysis to assess the best combination of settings that produced consistent performances for segmentation accuracy and specificity across datasets. The best settings were found to be in line with the suggested parameters in  ( Griffanti et al. , 2016 )  and previously used in studies using BIANCA on the WH dataset  ( Griffanti et al. , 2018 ). Therefore, we fixed the following parameters for BIANCA throughout our study: 2000 training points representing WMH lesions, 10,000 points representing non-lesion voxels, a patch size of dimension 3 voxels and a spatial weighting coefficient equal to 2. The number of k nearest neighbours used in the algorithm was set to k = 40, since it provided good performance in previous studies using k-NN for white matter lesion segmentation  ( Anbeek et al.
, 2004 ; Steenwijk et al. , 2013 ; Griffanti et al. , 2016 ). A subset of manually segmented WMH images was available from each dataset to train BIANCA and to evaluate its segmentation performance in a cross-validated manner. The segmented data included 24 participants from the WH1 dataset, 24 from the WH2 dataset and 12 from the UKB dataset. The 24 subjects from WH1 were manually annotated by two raters (R1, R2). Rater 2 repeated their annotation a year later (R2a, R2b) enabling us to assess the effects of within-and betweenrater variability on the WMH measures. Rater 2 also labelled the 24 scans from the WH2 dataset. For UKB we used the manual masks of 12 subjects used in the released imaging pipeline  ( Alfaro-Almagro et al. , 2018 ).
05 and * * for p -values < 0. 01). Variables requiring the application of non-imaging harmonisation strategies are highlighted in bold. Legend: N/A = excluded due to lack of availability for all participants within the UK Biobank cohort, ~= excluded due to substantial differences in the data collection across the two datasets. affecting data and required some changes in the harmonisation pipelines applied. b-value (s/mm 2 ) -- -- 1500 -- -- 1500 -- -- Directions (n. ) -- -- 6 0 + 6 b0 (1 reversed PE) -- -- 6 0 + 6 b0 (1 reversed PE) -- -- Scanner upgrade (Whitehall) -We started the analysis with the scanner upgrade scenario (WH1 and WH2) that included prospective harmonisation in the study design: the same non-imaging variables were collected and the MRI protocol was as close as possible for the two scanners.
Retrospective harmonisation was therefore not needed for the non-imaging data but carried out on the images. The availability of manual masks from multiple raters, 'traveling heads' data and FA maps for most of the participants allowed us to study the impact of: (i) the rater performing the manual labelling, (ii) the process of bias field correction on FLAIR images, (iii) the composition of the dataset used to train BIANCA (training set) and (iv) the inclusion of FA as one of the MRI modalities used by the segmentation tool to derive intensity features. We compared one option at a time using the metrics described in the Evaluation metrics section, while keeping the others fixed, in order to understand how each one could influence the results. We then identified optimal pre-processing and analysis strategies to reduce non-biological variability across datasets, while retaining or taking into account (modelling) the biological variability.
We evaluated this aspect on the 'traveling heads' data available for the WH dataset. Corresponding scans from each of the 5 subjects were first registered to each other and then resampled into the space half-way between the two. We then calculated the cost function (correlation ratio) between the registered images as a measure of image similarity that is not influenced by head position (lower cost function indicates more similar images). The same procedure was repeated on the bias field corrected images. The values of the cost function before and after BC were compared with a paired t -test. Secondly, we investigated the effect of BC on BIANCA performance (i. e. overlap with manual WMH masks) as described in the Evaluation metrics section. The manual rater was R2 for both datasets and the training set for BIANCA was the same (24 subjects from WH1).