Crise de la reproductibilité

Résumé

La crise de la reproductibilité (replication crisis ou replicability crisis ou reproducibility crisis en anglais) est la crise méthodologique dans le domaine des sciences selon laquelle de nombreux résultats publiés dans des revues scientifiques sont difficiles, voire impossibles à reproduire au cours d'études subséquentes. Initiée au milieu des années 2000, la crise prend de l'ampleur au milieu des années 2010, nourrie par la publication de plusieurs articles sur le phénomène. La crise n'est pas propre à un domaine unique bien qu'elle semble moins toucher les sciences fondamentales et appliquées que les sciences médicales. Les facteurs qui en sont responsables semblent nombreux. Des pistes d'amélioration de la reproductibilité au sein des publications scientifiques, dont notamment l'amélioration des critères de publication, sont explorées. Historique vignette|redresse|Article de Ioannidis. En 2005, John Ioannidis, professeur de médecine et chercheur à l'école de médec

Source officielle

https://fr.wikipedia.org/wiki/Crise_de_la_reproductibilit%C3%A9

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La psychologie (du grec , « âme », et , « parole, discours »), est une discipline scientifique qui s'intéresse à l'étude du corpus des connaissances sur les faits psychiques, les comportements et le

La méthode scientifique désigne l'ensemble des canons guidant ou devant guider le processus de production des connaissances scientifiques, qu'il s'agisse d'observations, d'expériences, de raisonnement

La reproductibilité d'une expérience scientifique est une des conditions qui permettent d'inclure les observations réalisées durant cette expérience dans le processus d'amélioration perpétuelle des

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Carbon turnover and microbial activity in an artificial soil under imposed cyclic drainage and imbibition

Tatjana Milojevic

Water table fluctuations generate temporally and spatially dynamic physicochemical conditions that drive biogeochemical hot spots and hot moments in the vadose zone. However, their role in the cycling of soil C remains poorly known. Here, we present results from unvegetated column experiments filled with 45 cm of artificial soil containing 10% humus, and inoculated with a natural microbial extract. In one series of three replicate columns, five cycles, each consisting of a 4-wk drainage followed by a 4-wk imbibition period, were imposed, whereas in a second series, the water table remained static. Depth-resolved O-2 concentration profiles and headspace CO2 effluxes were markedly different between the two regimes. In the fluctuating regime, drainage periods yielded 2.5 times greater CO2 effluxes than imbibition periods. At the end of the experiment, the fluctuating water table columns exhibited a distinct zone of organic C (OC) depletion in the depth interval of 8-20 cm that was not observed under the static regime. Although this zone showed elevated levels of adenosine triphosphate (ATP), the microbial biomass was actually lower than at the corresponding depth interval of the static regime. A vertically stratified microbial community established in all columns that depended on oxygenation with depth. The 16S ribosomal RNA (rRNA) gene analyses showed a slightly higher diversity in the soil exposed to moisture fluctuations, but there was no clear difference in major taxa and microbial community composition between treatments. These results thus suggest that the localized enhancement of OC degradation induced by the water table fluctuations was driven by a more active, rather than a more abundant or compositionally very different, microbial community.

WILEY2020

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Efficient Protocols for Enforcing Causal Consistency in Geo-Replicated Key-Value Data Stores

Kristina Spirovska

Modern large-scale data platforms manage colossal amount of data, generated by the ever-increasing number of concurrent users. Geo-replicated and sharded key-value data stores play a central role when building such platforms. As the strongest consistency model proven not to compromise availability, causal consistency (CC) is perfectly positioned to address the needs of such large-scale systems, while preventing some ordering anomalies. Transactional Causal Consistency (TCC) augments CC by providing richer transactional semantics, simplifying the development of distributed applications. However, achieving CC/TCC in an efficient manner is very challenging. In this thesis we introduce several protocols and designs for high performance causally consistent geo-replicated key-value data stores in several different settings. First, we present a new approach to implementing CC in geo-replicated data stores, called Optimistic Causal Consistency (OCC). By introducing a technique that we call client-assisted lazy dependency resolution, OCC makes it possible for updates replicated to a remote data center to become visible immediately, without checking if their causal dependencies have been received. We further propose a recovery mechanism that allows an OCC system to fall back on a pessimistic protocol to continue operating during network partitions. We show that OCC improves data freshness, while offering performance that is comparable or better than its pessimistic counterpart. Next, we address the problem of providing low-latency TCC reads under full replication. We present Wren, the first TCC system that at the same time achieves low latency by implementing nonblocking read operations and efficiently scales out by sharding. Wren introduces new protocols for transaction execution, dependency tracking and stabilization. The transaction protocol supports nonblocking reads by providing a transaction snapshot as a union of a fresh causal snapshot installed by every partition in the local data center and a client-side cache for writes that are not yet included in the snapshot. The dependency tracking and stabilization protocols require only two scalar timestamps, resulting in efficient resource utilization and providing scalability in terms of shards and replication sites, under a full replication setting. In return for these benefits, Wren slightly increases the visibility latency of updates. Finally, we present PaRiS, the first TCC system that supports partial replication and provides low latency by implementing non-blocking parallel read operations. PaRiS relies on a novel protocol to track dependencies, called Universal Stable Time (UST). By means of a lightweight background gossip process, UST identifies a snapshot of the data that has been installed by every data center in the system. PaRiS equips clients with a private cache, in which they store their own updates that are not yet reflected in the snapshot. The combination of the UST-defined snapshot with client-side cache enables interactive transactions that can consistently read from any replication site without blocking. Moreover, PaRiS requires only one timestamp to track dependencies and define transactional snapshots, thereby achieving resource efficiency and scalability in terms of shards and replication sites, in a partial replication setting.

EPFL2020

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Replication is a widely used method to protect large- scale data storage systems from data loss when storage nodes fail. It is well known that the placement of replicas of the different data blocks across the nodes affects the time to rebuild. Several systems described in the literature are designed based on the premise that minimizing the rebuild times maximizes the system reliability. Our results however indicate that the reliability is essentially unaffected by the replica placement scheme. We show that, for a replication factor of two, all possible placement schemes have mean times to data loss (MTTDLs) within a factor of two for practical values of the failure rate, storage capacity, and rebuild bandwidth of a storage node. The theoretical results are confirmed by means of event-driven simulation. For higher replication factors, an analytical derivation of MTTDL becomes intractable for a general placement scheme. We therefore use one of the alternate measures of reliability that have been proposed in the literature, namely, the probability of data loss during rebuild in the critical mode of the system. Whereas for a replication factor of two this measure can be directly translated into MTTDL, it is only speculative of the MTTDL behavior for higher replication factors. This measure of reliability is shown to lie within a factor of two for all possible placement schemes and any replication factor. We also show that for any replication factor, the clustered placement scheme has the lowest probability of data loss during rebuild in critical mode among all possible placement schemes, whereas the declustered placement scheme has the highest probability. Simulation results reveal however that these properties do not hold for the corresponding MTTDLs for a replication factor greater than two. This indicates that some alternate measures of reliability may not be appropriate for comparing the MTTDL of different placement schemes.

2010

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Efficient Protocols for Enforcing Causal Consistency in Geo-Replicated Key-Value Data Stores

Kristina Spirovska

EPFL2020