#Awaken my love vinyl reuse code how to
With no restrictions on the data format or level of documentation, learning how to analyze diverse open datasets can take substantial effort, and scientists are limited in their ability to perform meta-analyses across datasets. However, the same features that reduce the barriers for sharing can also increase the barriers for reuse. As data can be hosted on these repositories for free, they greatly lower the barriers to sharing. In addition, the lab of György Buzsáki maintains a databank of recordings from more than 1000 sessions from freely moving rodents ( Petersen et al., 2020). More recently, an increasing number of researchers are choosing to make data public via generalist repositories such as Figshare, Dryad, and Zenodo, or the neuroscience-specific G-Node Infrastructure. Our own meta-analysis of these articles shows that 28 out of 150 datasets have been reused at least once, with four reused more than 10 times each. The website includes a list of 111 publications and preprints based on CRCNS data. The repository does not enforce formatting standards, and thus each dataset differs in its packaging conventions, as well as what level of preprocessing may have been applied to the data. This is especially impressive given that CRCNS was launched by a single lab in 2008. To date, CRCNS hosts 150 datasets, including extensive neurophysiology recordings from a variety of species, as well as fMRI, EEG, and eye movement datasets. A number of these datasets have been shared via the website of CRCNS ( Teeters et al., 2008), far-sighted organization focused on aggregating data for computational neuroscience within the same searchable database. Data from ‘calibration’ experiments, in which activity of individual neurons is monitored via two modalities at once, have been extremely valuable for improving data processing algorithms ( GENIE Project, 2015 Henze et al., 2009 Huang et al., 2021 Neto et al., 2016). Electrophysiological recordings from nonhuman primates, which require tremendous dedication to collect, are often reused in multiple high-impact publications ( Churchland et al., 2010 Murray et al., 2014). Without a doubt, reanalysis of neurophysiology data has already facilitated numerous advances. It also gives researchers a chance to test hypotheses on existing data, refining and updating their ideas before embarking on the more costly process of running new experiments. It encourages meta-analyses that integrate data from multiple studies, providing the opportunity to reconcile apparently contradicting results or expose the biases inherent in specific analysis pipelines ( Botvinik-Nezer et al., 2020 Mesa et al., 2021). It increases the number of eyes on each dataset, making it easier to spot potential outlier effects ( Button et al., 2013). Sharing data brings other benefits as well. A scientific ecosystem in which data is extensively shared and reused would give researchers more freedom to focus on their favorite parts of the discovery process.
#Awaken my love vinyl reuse code software
Data collection requires a different skillset than analysis, especially as the field demands more comprehensive and higher-dimensional datasets, which, in turn, necessitate more advanced analytical methods and software infrastructure. Moreover, while many scientists relish running experiments, others find their passion in analysis. Not every lab has the financial or personnel resources to accomplish this. Accordingly, the tools to study it have become intricate and costly, generating ever-growing torrents of data that need to be ingested, quality-controlled, and curated for subsequent analysis. Why share data? The central nervous system is among the most complex organs under investigation. We distill some of the lessons learned about open surveys and data reuse, including remaining barriers to data sharing and what might be done to address these. Data from these surveys have been used to produce new discoveries, to validate computational algorithms, and as a benchmark for comparison with other data, resulting in over 100 publications and preprints to date. Here, we take stock of the Allen Brain Observatory, an effort to share data and metadata associated with surveys of neuronal activity in the visual system of laboratory mice. While embraced in spirit by many, in practice open data sharing remains the exception in contemporary systems neuroscience. As the complexity of modern scientific instrumentation has made exact replications prohibitive, sharing data is now essential for ensuring the trustworthiness of one’s findings. Nullius in verba (‘trust no one’), chosen as the motto of the Royal Society in 1660, implies that independently verifiable observations-rather than authoritative claims-are a defining feature of empirical science.
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