dataesr/scientific-paragraphs-categorization huggingface dataset:
A second issue is the difficulty of putting one's results in the context of other studies. The overwhelming number of brain imaging results published each year makes manual comparison both unfeasible and prone to bias. There are attempts to automatically aggregate knowledge across large sets of neuroimaging studies. For example, Neurosynth  (Yarkoni et al. , 2011)  is a metaanalysis database that collects coordinates of activation foci from published papers and generates topic maps based on the spatial distribution of those coordinates. Such maps can aid in interpretation of new results. However, comparing a new result to a set of topic maps has so far not been implemented in a user-friendly fashion. Finally, and most importantly, making meta-analytic inferences using only peak coordinates (or statistically thresholded maps) is problematic.
User Interface NeuroVault is designed to provide intuitive, interactive visualization of uploaded images. Each image is assigned its own unique URL with an embedded JavaScript 2D/3D viewer. In contrast to traditional, static figures in published articles, users can dynamically interact with images-adjusting statistical thresholds, selecting different color maps, and loading additional brain volumes into the viewer for comparison. Using two embedded open-source JavaScript viewers (Papaya-https://github. com/rii-mango/Papaya and pycortexhttps://github. com/gallantlab/pycortex), users can interrogate the data both in the volumetric space as well as on the surface (see Figure  2 ). Both viewers work inside modern web browsers and do not require any additional software to be installed. In addition to the visual representation of the volume, each page also displays any metadata associated with that image (e.
Interoperability A major goal of NeuroVault is to directly interoperate with other existing web-based neuroimaging resources, ensuring that users can take advantage of a broad range of computational tools and resources without additional effort. There are two components to this. First, in cases where other relevant resources implemented a public API, NeuroVault can provide a direct interface to those resources. For example, at the push of a single button, each map deposited in NeuroVault can be nearinstantly "decoded" using Neurosynth (see Figure  3 ). In the time of 1-2 s, the uploaded image is analyzed for its spatial correlation with a subset of the concept-based meta-analysis maps in the Neurosynth database. The user is then presented with a ranked, interactive list of maximally similar concepts, providing a quantitative, interactive way of interpreting individual statistical images that is informed by a broader literature of nearly 10,000 studies.
Meta-Analysis Using the Neurovault Data At the time of submitting this publication, there were 135 nonempty public collections (53 of them associated to a publication; for up to date stats see http://neurovault. org/collections/stats) comprised of 692 images labeled as Z, T, or F statistics. Out of these, we removed 14 outliers, and selected 678 maps to perform proof of concept analyses. The outliers were detected by using a PCA on all the statistical maps  (Fritsch et al. , 2012). We found wrongly labeled images such as brain atlases, cropped images, and images thresholded at a very high threshold. We performed metaanalyses using the remaining set of curated images with the goal of determining whether results could be obtained using a limited set of unthresholded maps that are similar to results from large coordinate-based databases.
Spatial Distribution of Activations The goal of this analysis is to explore the spatial distribution of activations across all maps in Neurovault in relation to results previously reported in the literature. The analysis aims to quantify the base rate of activation at each voxel across the entire brain-i. e. , to identify regions that are activated more or less often across different tasks. Using coordinate data from the the Neurosynth database, we generated a prior activation probability map based on over 300,000 coordinates drawn from nearly 10,000 published studies. To facilitate fair comparison with the Neurosynth map, we thresholded each map from NeuroVault at a Z or T value of 3 (F maps were excluded). This discretization step approximates the standard Neurosynth procedure of taking discrete peaks reported in studies and convolving them with 3D spheres.
Interestingly, the networks that are most prominent on this map are largely related to attention and executive control. The Neurosynth prior activation map is shown for comparison in Figure  4 , top. It displays a similar density of activation, with visible attentional networks. It is worth noting that other studies have also reported similar activation density maps (e. g. ,  Nelson et al. , 2010). However, the visual cortex is much less present in the Neurosynth map compared to the NeuroVault frequency map. This could potentially be explained by the fact that results NeuroVault includes many statistical maps from fMRI experiments contrasting a single condition with fixation cross baseline. However, most papers report contrasts between conditions removing the effect of the visual stimuli and thus the coordinate database will contain fewer activations in the visual cortex.
We initially thresholded the NeuroVault frequency map in order to facilitate comparison with conventional coordinate based approaches (e. g. , the Neurosynth map). However, one important benefit of using unthresholded maps is the retention of additional information in the form of continuous values at all voxels. To investigate what one can gain by using unthresholded maps, we calculated a simple average of all T and Z maps across the entire NeuroVault database (Figure  4 , bottom). Unlike the frequency map, as well as the CBMA, this analysis also captures the dominant sign of the activation, accumulating power in regions that may not cross threshold in analyses from individual studies (note that doing a principled statistical inference, e. g. , computing a p-value or a posterior from this heterogeneous collection of maps would require methodological developments outside of the scope of this article).
Discussion We present NeuroVault, a web based platform that allows researchers to store, share, visualize, and decode maps of the human brain. This new resource can improve how human brain mapping experiments are presented, disseminated, and reused. Due to its web-based implementation NeuroVault does not require any additional software to be installed and thus is very easy to use. One of the biggest challenges of data sharing platforms is sustainability. Users contributing their data trust that they will be available over an extended period of time. While we cannot make any certain claims about the future, we designed the service in a way to maximize its robustness. NeuroVault is an open source project (the code is available at https://github. com/NeuroVault/ NeuroVault) that is dependent only on free and open source components (web servers, content management systems, databases, etc.
). This means that if the need arises, an individual with minimum web administration experience can set up NeuroVault to run on a new server. Software is not, however, the most important part of the project. To preserve the data we are performing daily offsite backups that are later copied to other locations. The procedure of restoring the service from scratch using the freely available code combined with these backups has been heavily tested. The last component of the service reliability is hardware. It is worth noting that statistical maps take considerably less space than other types of data such as raw fMRI datasets. A 500 GB hard drive (available for $50) can store almost 500,000 statistical maps. Furthermore, the cost of server maintenance and the connection to the Internet can easily be leveraged by existing academic institutions' infrastructures.
In short, we argue that even though no one is able to guarantee long term availability of NeuroVault, due to the nature of its design and the type of data it is dealing with, it is easy and cheap to maintain or host at a new location given there is enough interest and the service will prove to be useful to the scientific community. NeuroVault is not only a helpful tool for researchers who want to share, visualize, and decode their maps, it is also a resource for researchers wanting to perform meta-and coactivation analyses. Thanks to the public RESTful API and the CC0 licensing of the data there are no restrictions in terms of how and by whom the data can be used. We hope that this will accelerate progress in the field of human brain imaging and better integrate the growing compendium of resources, as there are many services that could benefit from interaction with NeuroVault.
We suggest that Neurosynth and BrainMap can boost the power of their meta-analyses by working with unthresholded maps stored in NeuroVault instead of peak coordinates extracted from papers. In our analyses we have showed promising results [replication of Neurosynth frequency map, DMN deactivation and ICA topic maps similar to  Smith et al. (2009) ] even with an initial heterogenous set of few maps. The power of an image based meta-analysis approach is exemplified by the by the fact that using only a few 100 maps replicated results from much bigger (coordinate-based) databases (BrainMap and NeuroSynth cover, respectively 2500 and 9000 papers). We are convinced that an increased amount of data will lead to discovering new organizational principles of brain function. The sharing of neuroimaging data can potentially raise ethical issues related to subject confidentiality  (Brakewood and Poldrack, 2013).
As NeuroVault is mainly focused on group data analyses, there is little chance that personal information will be included and lead to ethical issues, but the platform allows single subject analysis results to be uploaded. Uploading such data would require researchers to take extra care not to expose the the identity of their subjects. To minimize the amount of effort needed to create a new collection, the addition of annotated metadata is optional in NeuroVault. Nevertheless, at the users' discretion, a rich set of metadata can be manually included and stored with the statistical maps. We envision that, in the future, more and more machine-readable information will be shared and these metadata will be populated automatically to increase the potential re-use of the datasets hosted at NeuroVault.
Current efforts (e. g. , the previously mentioned BrainSpell), can aid the process of annotating papers (and their corresponding maps) through crowdsourcing. Ideally, machine-readable metadata would be made available directly by the software packages used to generate the statistical maps. For example, the NeuroImaging Data Model (NIDM;  Keator et al. , 2013)  is a metadata standard that could be used to withstand metadata loss between an analysis and the upload of the statistical maps into NeuroVault. The NIDM-Results standard captures not only the statistic map, but also the design matrix, residuals, group mask, and many other pieces of information useful for future analysis. Currently only SPM natively exports to this file format, but we have adopted third party scripts to convert outputs of the FSL analyses (FEAT folders) to NIDM-Results on the server side and thus capture richer metadata in a fully automated way, and a solution for AFNI is currently being implemented.
To exemplify the importance of such metadata, we present a hypothetical study that aims to train a classifier to predict some outcome from activation maps. It could be the case that effects are due to metadata variables such as the source, software, or scanner, and this finding would only be apparent given that this information is available. It is also worth pointing out that NeuroVault is not only supporting task-based fMRI results. Results from resting state fMRI, PET, VBM, DWI, and most interestingly source reconstructed EEG/MEG experiments can be used with the platform as long as they are NIFTI files in MNI space. We plan to expand this to FreeSurfer surfaces, CIFTI files, and connectomes in the near future. Historically, aggregating results across modalities has been difficult, and we hope that this platform can start to improve upon this situation, by providing one common place for storing and sharing statistical maps.
Limitations and Future Directions One of the biggest limitations of the NeuroVault database is its size and the voluntary nature of data contributions. For any metaanalysis to be meaningful the sample of included studies needs to be representative. Including only papers that have corresponding statistical maps in NeuroVault instead of all papers might create unpredictable biases (although this bias is most likely to be toward inclusion of more trustworthy results; see  Wicherts et al. , 2011). One-way of dealing with this is to enforce deposition of statistical maps across all published research. This would be a drastic move, and some data sharing initiatives in neuroimaging in the past were met with considerable opposition from the community  (Van Horn and Gazzaniga, 2013). Instead we have reached out to leading journals in the field to encourage (but not require) authors of accepted papers to deposit statistical maps in NeuroVault.
So far, NeuroImage, F1000Research and Frontiers in Brain Imaging Methods have joined us in the quest of providing better and more open representation of experimental results. We hope that with time publishing statistical maps will become standard practice. NeuroVault fills a specific niche in the neuroinformatics ecosystem. The main purpose is to collect, store, and share statistical maps. We leave the task of extracting knowledge (tags, labels terms) out of papers and associating them with the statistical maps to other platforms BrainSpell, Neurosynth, and BrainMap. We also do not aspire to provide a platform for performing meta analyses (neurosynth and BrainMap facilitate this). This decision is intentional and was made to focus on one specific task and do it well. Thus, in the future we want to focus on (i) making the platform more attractive for researchers (so the motivation for data deposition will increase), (ii) making the data deposition process easier and automatic extraction of metadata more effective, and (iii) reaching out to the community to make sharing of statistical maps a common practice.
In terms of the first goal we are working hard on adding new features that will help researchers to understand and visualize their maps. One of such features (currently in beta) is map comparison: users will be able to compare their map with all the other maps deposited in the database and thus easily find experiments with similar imaging results. The second goal will involve tighter integration with the most popular software packages (capitalizing on the NIDM-Results standard). We plan to provide a single click solution for uploading maps to NeuroVault that will be available within analysis software such as SPM, FSL, and AFNI. Finally the third goal, probably the most important, and also the hardest, involves continuous conversations with academic journals and conference organizations such as the OHBM.
Conclusion In this work we have described NeuroVault-a web-based repository that allows researchers to store, share, visualize, and decode unthresholded statistical maps of the human brain. This project not only helps individual researchers to disseminate their results and put them in the context of existing literature, but it also enables aggregation of data across studies. Through our analyses we have shown that with only a few hundred statistical maps we can achieve results comparable to those obtained with thousands of sets of coordinates. NeuroVault is free and unencumbered by data use agreements. The data is available and the database queryable via the web interface and RESTful API. This simple and modern platform opens the door to developing novel methods to draw inferences from a meta-analytic database.
Acknowledgments This work was partially funded by the  National Institutes of Health (NIH) ,  R01MH096906  [TY],  NSF   OCI-1131441  [RP],  International Neuroinformatics Coordinating Facility (INCF)  and the  Max Planck Society  [KJG, DSM]. We thank the  International Neuroinformatics Coordinating Facility (INCF) Neuroimaging Data Sharing (Nidash ) task force members for their input during several discussions.
For instance, in our current research on criminal organizations, a criminal described the conditions, in which he had found himself, as a 'rule of terror' , which provides a vivid picture of a war in the underworld  [9]. In the following example we make intensive use of these textual annotations. The qualitative research provided the basis for the development of a conceptual model that is ready for being implemented as an agent-based simulation model. We aim to show how GT assists formal knowledge representation in software engineering. Coding can be assisted with a number of software tools such as Atlas. ti or MaxQDA. We analyzed the data by using MaxQDA and then imported it to a tool called CCD  [4, 6]. CCD is a tool for developing conceptual models. A feature particularly relevant for software engineering is that it already generates templates of program codes for the simulation model  [6].
The conceptual model consists of a web of so-called event-action sequences, i. e. , a situational condition triggers an action by the agents which in turn generate new situational conditions. These sequences represent the dimensions of and relation between the codes. Moreover CCD preserves traceability to the data basis by annotations of -in terms of Grounded Theory -in vivo codes that justify the sequences. Conceptual modeling is a software engineering equivalent to open coding, whereby the condition-action sequences already represent generalized abstractions.
fumigatus RIP may also occur at CpG sites in TEs in addition to CpA  [11] [12] [13] , suggesting the involvement of Dnmt1 Mtases recognizing mainly CpA but also CpG di-nucleotides. However it has also been suggested that the RIP-like process based on 5mC methylation followed by deamination may operate in a different manner in basidiomycetes than ascomycetes and that the rid homologue may have diverged during the evolutionary time separating these two phyla  [31]. In this study, we conducted a bioinformatics analysis of C to T mutation bias in 10 fungal genomes with different TE content, which were recently analysed using the TEdenovo  [32, 33]  and the TEannot  [34]  from the REPET TE annotation package in the context of international fungal genome projects. We used the consensus sequences of TE families and corresponding TE genomic sequences for each genome obtained from the TEdenovo and the TEannot pipelines, respectively.
Recent TE invasions in fungi explain the lack of correlation between genome size and taxonomy We used the REPET TE annotation package to produce a standardized genome-wide annotation of TEs from 10 fungi (Table  1 , Additional file 1) with different lifestyles, including seven ascomycetes (Botrytis cinerea (T4 and B05. 10 isolates), Sclerotinia sclerotiorum  [36] , Blumeria graminis fp. hordeï  [37] , Leptosphaeria maculans  [19] , Magnaporthe oryzae  [38] , Tuber melanosporum  [39] ) and three basidiomycetes (Puccinia graminis and, Melampsora larici-populina  [40]  and Microbotryum violaceum (in prep; Microbotryum violaceum Sequencing Project, http:// www. broadinstitute. org). Among the 10 species studied, BcinT4 (genome abbreviation see Table  1 ) had the lowest TE content (1% of its genome), whereas Bgra had the highest (75% of its genome Table  1 , Figure  1A ).
Dinucleotide signatures at C to T mutation sites in fungal TEs are associated with specific Dnmt1 subfamilies We next addressed whether the differences in C to T mutation bias in TEs and their associated dinucleotide signatures among fungi could be explained by the presence/absence in these various species of different genes involved in this process. RIP and MIP require cytosine methyltransferases of the Dnmt1 family; therefore, we searched for 25 fungal genomes (included the 10 fungal genomes studied) for genes encoding Dnmt1 MTases. Forty-four proteins containing the cytosine specific methyltransferase Panther domain (PTHR10629) were recovered from these 25 genomes. This domain overlaps on both ends the C5-cytosine methyltransferase PFAM domain (PF00045), which is specific to the cytosine DNA MTase family (Figure  5B ).
Several domains were irregularly distributed among fungal Dnmt1 proteins. For instance, the PROSITE domain C5_MTASE_1 (PS00094), which is part of the PF00145 domain (i. e. the PFAM equivalent of PTHR10629), was detected in almost all (11 out of 12) Dnmt1 class IIB (Dim-2) proteins, but only in a subcluster (comprising 4 out of 12) of class IIA proteins that are highly related to Masc2. This domain is absent in almost all (18 out of 19) Dnmt1 class I (Masc1/RID proteins). By contrast, C5_MTASE_2 (PS00095), a C-5 cytosine-specific DNA methylase C-terminal aminoacid signature, was found in 50% of the Masc1/RID proteins and in class II proteins, this domain was restricted to the Masc2-like class IIA subcluster (Figure  5 ). Thorough examination of the organization of the predicted DNA MTase domain (PF00145) in fungal Dnmt1 homologs revealed that it is on average smaller (240 aa) in class I proteins than in class II proteins (400 aa).
This difference is due to a large region upstream from the C-terminal catalytic domain that is present only in the Dnmt1 class II proteins. Another amino-acid probably lost the gene, the C5METTRFRASE (PR00105) from the PRINTS fingerprint protein database, was present in almost all (80%) Dnmt1 proteins. Nevertheless, it is probable that these three patterns (C5_MTASE_1, C5_MTASE_2 and C5METTR-FRASE) were probably present in a common ancestor of the two classes because we found relics of these motifs in all 44 proteins analyzed (Additional file 2). We also used the MEME tool to identify conserved motifs in fungal Dnmt1 proteins. This analysis revealed that 19 out of 30 conserved motifs where specific to either Dnmt1 class I or class II proteins: five motifs  (15, 17, 20, 26, 29)  were present only in Dnmt1 class I proteins whereas the other 14 motifs were present only in Dnmt1 class II proteins (Additional file 3).
Evolution of Dnmt1 subgroup proteins Here we showed that a part of the PFAM Domain PF00145 specific to DNA methyltransferase (C5_MTASE) is missing from all the RID/Masc1-like proteins (Figure  5B ; Additional file 2). In addition, we found four motifs specific to these genes (Additional file 3,  Motif 15, 17, 20, 26). The loss of part of the C5_MTASE domain associated with the acquisition of these four motifs may have resulted in the specialization of the gene responsible for the RIP process, the efficiency of which depends on the organism (Figure  3 ). A fifth motif (Additional file 3, Motif 29) only present in B. cinerea, S. sclerotiorum and M. oryzae could be involved in the recognition of the dinucleotide target CpT because this signature was detected only in TE copies from these species.
TE consensus and genomic copy resources Datasets of TE families were obtained through several genome sequencing projects that we were involved in. The REPET TEdenovo pipeline  [32]  was used to detect TEs in genomic sequences and to provide a consensus sequence for each family. TEs were then classified according to structural and functional features (LTR, TIR, RT, transposases, polyA tail) and similarities with characterized TEs from the Repbase Update database  [50]. The REPET TEannot pipeline  [34]  and the previously obtained TE consensus libraries were used to annotate TE genomic copies, including nested and degenerated ones, in each of the 10 genomes. Basic TE annotations have already been published for most of these species, which included: (i) six ascomycetes: Botrytis cinerea T4 (BcinT4 and B05.