We observed a difference between substrate porosity but could not attribute a specific experimental cause to the observed discrepancy (Tables ). We suspect that the strong TSD signature observed on non-porous surfaces is linked to environmental exposure and a lack of protection from and affinity towards the surface. Marrone and Hanson both suggested that hemoglobin oxidation occurs at faster rates in dried states compared to liquid states -since desiccation and clotting of blood take longer to occur on porous substrates than on non-porous substrates 111 , conversion between hemoglobin derivatives are expected to occur sooner for blood deposited on non-porous surfaces. This faster process is expected to demonstrate stronger relationships with TSD than the slower occurring processes on porous substrates, which is corroborated by the larger slopes observed for non-porous substrates in our modelling. Porous substrates might also protect the blood and biomolecules within from physicochemical degradation; greater amounts of DNA could be extracted from porous substrates than their non-porous counterparts . Based on the findings of our meta-analysis, we suspect that the choice of substrate porosity is dependent in part on the proposed analytical technique, which inherently influenced experimental conditions and results. However, this leads to a second key takeaway: TSD signatures are strongest when the fluid is deposited on non-porous surfaces. To have real-world and probative value, TSD studies should focus on these types of surfaces (e.g., 11,38,90). Additionally, recently proposed relative dating approaches -in which a bloodstain from the same individual as those from the crime scene are aged under identical conditions -might mitigate many sources of variability present in absolute dating techniques and should be further explored . Despite the large effect sizes, we are far from a predictive TSD model and implementation into casework. To help address these issues, all metadata have been made available on a public repository (). All summary statistics describing the relationship between relevant response variables and bloodstain TSD should be reported (r, R 2 , p-values, beta coefficients), and we encourage a move toward predictive modelling, cross-validation, and independent replicates. We must also increase sample sizes to fully capture variance, particularly among individuals, and report key variables like temperature and humidity values used during the study. Larger sample sizes are required for predictive modelling approaches like cross-validation. Lastly, particularly regarding the development of predictive models and applications to casework, there needs to be a place for confirmatory and negative results. Our meta-analysis suggests a publication bias, not uncommon , but in the context of developing predictive TSD models is problematic. We encourage the community to submit results to the above repository to build a database similar to DNA-Trac which documents DNA transfer studies . Ultimately, tools are being developed to increase the precision and accuracy of bloodstain TSD estimates; these could eventually be combined with techniques estimating the time of day of deposition and the age of the donor, providing a holistic framework for all bloodstain time-related matters.