The citation advantage of linking publications to research data

Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements actually contain well-formed links to data, for example via a URL or permanent identifier, and if there is an added value in providing such links. We consider 531, 889 journal articles published by PLOS and BMC, develop an automatic system for labelling their data availability statements according to four categories based on their content and the type of data availability they display, and finally analyze the citation advantage of different statement categories via regression. We find that, following mandated publisher policies, data availability statements become very common. In 2018 93.7% of 21,793 PLOS articles and 88.2% of 31,956 BMC articles had data availability statements. Data availability statements containing a link to data in a repository—rather than being available on request or included as supporting information files—are a fraction of the total. In 2017 and 2018, 20.8% of PLOS publications and 12.2% of BMC publications provided DAS containing a link to data in a repository. We also find an association between articles that include statements that link to data in a repository and up to 25.36% (± 1.07%) higher citation impact on average, using a citation prediction model. We discuss the potential implications of these results for authors (researchers) and journal publishers who make the effort of sharing their data in repositories. All our data and code are made available in order to reproduce and extend our results

The citation advantage of linking publications to research data

Efforts to make research results open and reproducible are increasingly reflected by journal policies encouraging or mandating authors to provide data availability statements. As a consequence of this, there has been a strong uptake of data availability statements in recent literature. Nevertheless, it is still unclear what proportion of these statements actually contain well-formed links to data, for example via a URL or permanent identifier, and if there is an added value in providing such links. We consider 531, 889 journal articles published by PLOS and BMC, develop an automatic system for labelling their data availability statements according to four categories based on their content and the type of data availability they display, and finally analyze the citation advantage of different statement categories via regression. We find that, following mandated publisher policies, data availability statements become very common. In 2018 93.7% of 21,793 PLOS articles and 88.2% of 31,956 BMC articles had data availability statements. Data availability statements containing a link to data in a repository—rather than being available on request or included as supporting information files—are a fraction of the total. In 2017 and 2018, 20.8% of PLOS publications and 12.2% of BMC publications provided DAS containing a link to data in a repository. We also find an association between articles that include statements that link to data in a repository and up to 25.36% (± 1.07%) higher citation impact on average, using a citation prediction model. We discuss the potential implications of these results for authors (researchers) and journal publishers who make the effort of sharing their data in repositories. All our data and code are made available in order to reproduce and extend our results

Rituais de sacrifício: a sobrevivência de uma antiga dimensão do corpo humano Rites of sacrifice: the survival of an ancient dimension of the human body

A força da tradição é capaz de preservar costumes que caminham na contramão da trajetória sociocultural das populações urbanas atuais. Costumes como os rituais de sacrifício, apesar de muitas vezes condenados pela sociedade e de terem sofrido um sincretismo adaptativo, ainda guardam elementos tradicionais, confirmando sua importância como mediadores entre os mundos natural e sobrenatural. Um bom exemplo são as lutas rituais Tinku, identificadas em amostras esqueléticas pré-colombianas provenientes do deserto de Atacama, Chile, e que ainda persistem entre grupos andinos, com uma abrangência temporal de pelo menos 1.200 anos. O objetivo principal dessa luta é provocar o sangramento e a morte de seus participantes, oferecidos à divindade Pachamama para propiciar a fertilidade da terra e dos animais. Os rituais de sacrifício, como símbolos de identidade social, nos ajudam a conhecer melhor o ethos de sociedades passadas e atuais.<br>The power of tradition is capable of preserving customs that go counter to the social and cultural trends in today's urban centers. Though customs such as rites of sacrifice are often condemned by society and have undergone an adaptive syncretism, they still preserve ancient traditional elements that underline their importance as mediators between the natural and supernatural worlds. A good example of this is the Tinku ritual fight, identified in samples of Pre-Columbian skeletons from the Atacama desert in Chile, which continues to this day amongst Andean groups, having survived for at least 1,200 years. The main objective in this fight is that the participants bleed to death as offerings to the divinity, Pachamama, to assure the fertility of the land and the animals. When rites of sacrifice are understood as symbols of social identity, they give us a better understanding of the ethos of past and present societies, from a very particular perspective

ME-ICA/tedana: 23.0.1

Release Notes This release changes many internal aspects of the code, will make future improvements easier, and will hopefully make it easier for more people to understand their results and contribute. The denoising results should be identical. Right before releasing this new version, we released version 0.0.13, which is the last version of the older code. If you want to confirm the consistency of results, these are the two versions you should compare. Instructions for comparing results are below. Key changes Large portions of the code were reorganized and modularized to make understanding the code easier and facilitate future development Breaking change: tedana can no longer be used to manually change component classifications. A separate program, ica_reclassify, can be used for this. This makes it easier for programs like Rica to output a list of component numbers to change and to then change them with ica_reclassify. The component classification process that designates components as "accepted" or "rejected" was completely rewritten so that every step in the process is modular and the inputs and outputs of every step are logged. The documentation includes descriptions of the newly outputted files and file contents. It is now possible to select different decision trees for component selection using the --tree option. The default tree is kundu and should replicate the current outputs. We also include minimal which is a simpler tree that is intended to provide more consistent results across a study, but still needs more testing and validation and may still change. Flow charts for these two options are here. Anyone can create their own decision tree. If one is using metrics that are already calculated, like kappa and rho, and doing greater/less than comparisons, one can make a decision tree with a user-provided json file. More complex calculations might require editing the tedana python code. This change also means any metric that has one value per component can be used in a selection process. This makes it possible to combine the multi-echo metrics used in tedana with other selection metrics, such as correlations to head motion. The documentation includes instructions on building and understanding this component selection process. Breaking change: No components are classified as ignored. "Ignored" has long confused users. It was intended to identify components with such low variation that it wasn't worth deciding whether to lose a statistical degree of freedom by rejecting them. They were treated identically to accepted components. Now they are classified as "accepted" and tagged as "Low variance" or "Borderline Accept". These classification tags now appear on the html report of the results. A registry of all files outputted by tedana is now stored with the outputs. This allows for multiple file naming methods and means internal and external programs that want to interact with the tedana outputs just need to load this file. Nearly 100% of the new code and 98% of all tedana code is covered by integration testing. Tedana python package management now uses pyproject.toml Minimum python version is now 3.8 and minimum pandas version is now 2.0 (might cause problems if the same python environment is used for packages that require older versions of pandas) More comprehensive documentation of changes is in pull request #756 and the full release notes are here: https://github.com/ME-ICA/tedana/releases/tag/23.0.0 Changes [REF] Decision Tree Modularization (#756) @jbteves @handwerkerd @n-reddy @marco7877 @tsal

PyBIDS: Python tools for BIDS datasets

Brain imaging researchers regularly work with large, heterogeneous, high-dimensional datasets. Historically, researchers have dealt with this complexity idiosyncratically, with every lab or individual implementing their own preprocessing and analysis procedures. The resulting lack of field-wide standards has severely limited reproducibility and data sharing and reuse.To address this problem, we and others recently introduced the Brain Imaging Data Standard (BIDS; (Gorgolewski et al., 2016)), a specification meant to standardize the process of representing brain imaging data. BIDS is deliberately designed with adoption in mind; it adheres to a user-focused philosophy that prioritizes common use cases and discourages complexity. By successfully encouraging a large and ever-growing subset of the community to adopt a common standard for naming and organizing files, BIDS has made it much easier for researchers to share, reuse, and process their data (Gorgolewski et al., 2017).The ability to efficiently develop high-quality spec-compliant applications itself depends to a large extent on the availability of good tooling. Because many operations recur widely across diverse contexts—for example, almost every tool designed to work with BIDS datasets involves regular file-filtering operations—there is a strong incentive to develop utility libraries that provide common functionality via a standardized, simple API.PyBIDS is a Python package that makes it easier to work with BIDS datasets. In principle, its scope includes virtually any functionality that is likely to be of general use when working with BIDS datasets (i.e., that is not specific to one narrow context). At present, its core and most widely used module supports simple and flexible querying and manipulation of BIDS datasets. PyBIDS makes it easy for researchers and developers working in Python to search for BIDS files by keywords and/or metadata; to consolidate and retrieve file-associated metadata spread out across multiple levels of a BIDS hierarchy; to construct BIDS-valid path names for new files; and to validate projects against the BIDS specification, among other applications.In addition to this core functionality, PyBIDS also contains an ever-growing set of modules that support additional capabilities meant to keep up with the evolution and expansion of the BIDS specification itself. Currently, PyBIDS includes tools for (1) reading and manipulating data contained in various BIDS-defined files (e.g., physiological recordings, event files, or participant-level variables); (2) constructing design matrices and contrasts that support the new BIDS-StatsModel specification (for machine-readable representation of fMRI statistical models); and (3) automated generation of partial Methods sections for inclusion in publications.PyBIDS can be easily installed on all platforms via pip (pip install pybids), though currently it is not officially supported on Windows. The package has few dependencies outside of standard Python numerical and image analysis libraries (i.e., numpy, scipy, pandas, and NiBabel). The core API is deliberately kept minimalistic: nearly all interactions with PyBIDS functionality occur through a core BIDSLayout object initialized by passing in a path to a BIDS dataset. For most applications, no custom configuration should be required.Although technically still in alpha release, PyBIDS is already being used both as a dependency in dozens of other open-source brain imaging packages –e.g., fMRIPrep (Esteban et al.,2019), MRIQC (Esteban et al., 2017), datalad-neuroimaging (https://github.com/datalad/datalad-neuroimaging), and fitlins (https://github.com/poldracklab/fitlins) – and directly in many researchers’ custom Python workflows. Development is extremely active, with bug fixes and new features continually being added (https://github.com/bids-standard/pybids), and major releases occurring approximately every 6 months. As of this writing, 29 people have contributed code to PyBIDS, and many more have provided feedback and testing. The API is relatively stable, and documentation and testing standards follow established norms for open-source scientific software. We encourage members of the brain imaging community currently working in Python to try using PyBIDS, and welcome new contributions

ME-ICA/tedana: 0.0.12

Summary This would ordinarily not have been released, but an issue with one of our dependencies means that people cannot install tedana right now. The most notable change (which will potentially change your results!) is that PCA is now defaulting to the "aic" criterion rather than the "mdl" criterion. What's Changed [DOC] Add JOSS badges by @tsalo in https://github.com/ME-ICA/tedana/pull/815[FIX] Fixes broken component figures in report when there are more than 99 components by @manfredg in https://github.com/ME-ICA/tedana/pull/824[DOC] Add manfredg as a contributor for code by @allcontributors in https://github.com/ME-ICA/tedana/pull/825DOC: Use RST link for ME-ICA by @effigies in https://github.com/ME-ICA/tedana/pull/832[DOC] Fixing a bunch of warnings &amp; rendering issues in the documentation by @handwerkerd in https://github.com/ME-ICA/tedana/pull/840[DOC] Replace mentions of Gitter with Mattermost by @tsalo in https://github.com/ME-ICA/tedana/pull/842[FIX] The rationale column of comptable gets updated when no manacc is given by @eurunuela in https://github.com/ME-ICA/tedana/pull/855Made AIC the default maPCA option by @eurunuela in https://github.com/ME-ICA/tedana/pull/849[DOC] Improve logging of component table-based manual classification by @tsalo in https://github.com/ME-ICA/tedana/pull/852[FIX] Add jinja2 version pin as workaround by @jbteves in https://github.com/ME-ICA/tedana/pull/870 New Contributors @manfredg made their first contribution in https://github.com/ME-ICA/tedana/pull/824 Full Changelog: https://github.com/ME-ICA/tedana/compare/0.0.11...0.0.1