Archiving for the Future is a free training course designed to teach language documenters, activists, and researchers how to organize, arrange, and archive language documentation, revitalization, and maintenance materials and metadata in a digital repository or language archive. Then entire course can be completed in approximately 3-5 hours.

This course was developed by the staff of the Archive of the Indigenous Languages of Latin America at the University of Texas at Austin in consultation with representatives of various DELAMAN (https://www.delaman.org/) archives and other digital data repositories in the United States, the United Kingdom, the European Union, Australia, and Cameroon.

The course material is based upon work supported by the National Science Foundation under Grant No. BCS-1653380 (September 1, 2016 to August 31, 2020). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

This is a teacher-led activity within the field of linguistics, using child language acquisition as example. It can however be adapted to other disciplines. In this activity, students are given a two-part assignment whereby they shall analyse results on a given topic within child language acquisition, taken from different types of published research data. This activity is particularly useful for fields where data collection is diverse in nature. The activity aims at improving the student’s understanding of the field of research and how different types of data can complement each other. Also, it aims at furthering the student’s knowledge and skills regarding structuring and documentation of data.

A key component of scientific communication is sufficient information for other researchers in the field to reproduce published findings. For computational and data-enabled research, this has often been interpreted to mean making available the raw data from which results were generated, the computer code that generated the findings, and any additional information needed such as workflows and input parameters. Many journals are revising author guidelines to include data and code availability. This work evaluates the effectiveness of journal policy that requires the data and code necessary for reproducibility be made available postpublication by the authors upon request. We assess the effectiveness of such a policy by (i) requesting data and code from authors and (ii) attempting replication of the published findings. We chose a random sample of 204 scientific papers published in the journal Science after the implementation of their policy in February 2011. We found that we were able to obtain artifacts from 44% of our sample and were able to reproduce the findings for 26%. We find this policy—author remission of data and code postpublication upon request—an improvement over no policy, but currently insufficient for reproducibility.