Researchers will often ask us about our data analysis workflow. Most of our projects fall into two main categories. First, when a project starts after a proposal is awarded Second, when we provide Data Science support for research teams, Departments, Health Systems, companies, governments, or other organizations. Here are our main principles: Continuous delivery. The overarching principle behind our workflow is continuous delivery. In other words, we deliver a new set of results every week, usually on a Friday. [Read More]
Project workflow at SporeData.
Over the years, we have progressively polished a project workflow together with our collaborators in academia, government, and the corporate world. Below we provide some of the pillars behind this workflow. Written summary of the project. At the beginning of each project, we provide a written summary outlining the problem we are addressing and its importance, the methods used to address that problem, and our deliverables along with their expected impact. [Read More]
Risk sharing model for grant proposal writing.
SporeData business models
Below we explain our risk-sharing model for research proposals: Risk sharing concept. The concept behind risk-sharing is that when we come in as a subcontractor in your proposal, we will be sharing the risk of the proposal not being funded. In other words, if the funding agency awards the project, SporeData is paid to deliver the Data Science services outlined in the proposal. If the agency does not award the project, then you have no costs, and we will usually attempt to reshape the proposal and resubmit. [Read More]
Data Science support.
SporeData provides Data Science support services to a variety of groups in the US, including academic, government, and startup teams. Below we give details on these services. Traditional statistics. We cover a wide spectrum of traditional statistics, including longitudinal analyses (survival and mixed models), spatial statistics, and causal models. Machine learning models. We have a mature pipeline to process machine learning models at three levels: prognostic models (precision medicine), Natural Language Processing (conversion of free text to a spreadsheet format), and image processing (automated recognition of imaging signs and diagnoses). [Read More]
Remote data collection at SporeData.
In contrast with a Contract Research Organization (CRO), SporeData’s primary mission is related to Data Science applied to clinical and healthcare policy research. This difference means that we are usually not in charge of collecting patient data on-site. With that said, remote data collection is part of our portfolio. Below re some examples. Email surveys. We frequently prepare the design and conduct or email surveys, including those to provide population presentation, i. [Read More]
We provide a range of data-driven report formats so that you can communicate your results to different research stakeholders. Below we outlined some of the key aspects of our workflow: Use cases. Our data-driven reports cover different areas, including data quality, study recruitment, adverse events, and the monitoring of specific hypotheses over time. Static and automated. Automated static reports are typically delivered once a week or at an interval of your choice, and delivered by email in a PDF format. [Read More]
We have implemented several protocols to ensure that our designs are patient-centered. Below is a brief description: Contact and active participation throughout the study lifecycle. We create designs where patients and other stakeholders can be active participants throughout the study. These designs follow [PCORI methods recommendations]](https://www.pcori.org/research-results/about-our-research/research-methodology/pcori-methodology-standards). These activities start with joint research design sessions, where the study design is shared and discussed with patients and other stakeholders, such as healthcare professionals, hospital administrators, policymakers, government officials, among others. [Read More]
Proposal preparation workflow at SporeData
We are frequently asked about our workflow when writing a grant proposal, and so we thought about outlining it in a post: A discussion is held among all collaborators regarding the central area of research, what we want to accomplish, and the set of methods we plan on using. SporeData’s team releases a one-page Specific Aims section. The first goal of this section is to allow the whole team to contribute toward the design. [Read More]
Thinking about what we do.
We are relaunching our site, and that allows us to rethink what is it that we are doing right or wrong, and what kind of value we might be adding to all the different organizations working with us. Below is a draft of what we have been pondering: SporeData Automating scientific innovation About us We are a group of Data Scientists who are passionate about the process to design and conduct clinical and healthcare policy research. [Read More]
Our policy on intellectual property.
We are frequently asked regarding our policies related to intellectual property when signing agreements with academic institutions, governments, or private companies. Since it is easier to point all stakeholders to a public document, here are the principles we follow: Our team at SporeData focuses on providing Data Science services, and therefore we do not retain intellectual property in most circumstances. The only exceptions are when contracts or grants require or strongly suggest that we do so. [Read More]