|
Sort of. Yes and no. There has to be a metric to assess researcher's performance. Otherwise we won't know what research is worthwhile. When the rules of the game are known, players will find their way to cheat, or at least bend the rules to their advantage. So, for example, suppose negative results become as valuable: well, they are easier to produce. They are also less valuable as stepping stones for further research. Given that, you'd still need to have a metric that compares publishing positive results to negative results. Even if you declare them to be equally important, the shared understanding will be that they aren't. And one would be more important than the other. And here were are back to square one. There are some minor things that can be done in the near future. For example, results produced with code must come with the code that produced these results. A lot of research bodies resist this because they want to commercialize their code, or their code may inadvertently contain organization's secrets and therefore needs more auditing... but, in the end of the day, it needs to be made clear that this is a necessary and unavoidable price to pay. Data sharing is even more problematic. Beside confidentiality concerns, data is always a bargaining chip in the game of getting collaborators (and grants). Should it be made public, it loses its value to those who collected it. Right now, the trend is: if you managed to collect a worthwhile dataset, then you'll cover yourself foot to head with NDAs, contracts of all kinds etc, and will sit on it, exploiting it for a series of research. And if anyone wants to do research on the same subject, you will only invite them if they bring grants or equipment etc. But you cannot really verify results w/o having the data available. Even if you have the code. --- It's really sad to see how research is doing wrt' programming in part because of the above, but I don't think the programs outlined in OP will have a noticeable effect. They don't paint a convincing picture in terms of incentives, i.e. they don't answer the question why would researches want to do any of that RepRes and OS training. Even in computationally-heavy research today you often find that all the computation work is outsourced by the researchers and they themselves have no clue what their code is doing. Above were all sorts of arguments for why the current (or yours) approaches are ineffective. But I don't claim to know what needs to be done. |