Data provenance focuses on the source of the data and on the identification of data sources and their transformations undergone over time. This paper proposes a generic method for collecting provenance data, and is a follow-up of a study carried out by the same authors in a Brazilian hemotherapy center. This method is based on the W3C’s Provenance Data Model (PROV-DM), and proposes a way to capture, store and analyze anemia-index provenance data by applying a scientific workflow, together with the management of provenance of knowledge. This is an exploratory, practical and deductive study carried out with real data from 197,551 candidates for blood donors, extracted from reports ranging from 2000 to 2018 provided by a Brazilian hemotherapy center. People identified with high anemia rates were quantified and tagged as not-suitable for blood donations. The inadequate candidates were quantified with the highest rate of anemia, and out of 1011 male candidates and 4039 female candidates, women had the highest levels of inadequate blood donations. At the end of this study, it can be concluded that the generic method for collecting data provenance proposed here can be applied in several areas of knowledge.

数据出处侧重于数据源以及数据源的标识及其随时间的变化。本文提出了一种收集来源数据的通用方法，并且是该作者在巴西血液治疗中心进行的一项研究的后续研究。该方法基于W3C的出处数据模型（PROV-DM），提出了一种通过应用科学的工作流程以及知识出处管理来捕获，存储和分析贫血指数出处数据的方法。这是一项探索性，实用和演绎性研究，使用了197,551名献血者的真实数据进行了研究，这些数据摘自巴西血液治疗中心2000年至2018年的报告。贫血发生率高的人群被量化并标记为不适合献血。贫血率最高的候选人数量被量化，在1011名男性候选人和4039名女性候选人中，女性的献血不足水平最高。在研究结束时，可以得出结论，此处提出的收集数据来源的通用方法可以应用于多个知识领域。