Find research datasets worth reusing

openother-pdMay 2018View details →

Gates Open Research - publication data

Gates Open Research (https://gatesopenresearch.org/) publication metadata and events data, as of 1 March 2018.

opencc-by-4.0Jun 2018View details →

Datentabelle Inhalte der Open-Access-Policies deutscher Hochschulen

Open-Access-Policies von 47 deutschen Universitäten und 5 Fachhochschulen wurden inhaltlich ausgewertet und den &bdquo;Open Access Policy Guidelines for Research Performing Organizations“ des Projekts PASTEUR4OA (1) gegenübergestellt. Diese Auswertung von Open-Access-Policies kann Hochschulen, die eine Open-Access-Policy neu erarbeiten oder eine bestehende Policy überarbeiten, Anregungen geben, welche Inhalte berücksichtigt werden sollten. Open access policies of 47 German universities and 5 universities of applied sciences were analyzed and checked against the "Open Access Policy Guidelines for Research Performing Organizations" of the project PASTEUR4OA (1). This evaluation of Open Access Policies can give research institutions that are developing an Open Access Policy or revising an existing policy suggestions on which content should be considered. (1) Tsoukala, V. &  Angelaki, M., 2015, http://doi.org/10.5281/zenodo.51926

opencc-by-4.0Jun 2018View details →

CABRISS Open Dataset

Raw data generated by the CABRISS project along one example of the PV value chain using recycled silicon.

opengpl-2.0Jun 2018View details →

Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem - the dataset

<pre>Replication pack, FSE2018 submission #164: ------------------------------------------ </pre> <pre>**Working title:** Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem **Note:** link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. Content description =================== - **ghd-0.1.0.zip** - the code archive. This code produces the dataset files described below - **settings.py** - settings template for the code archive. - **dataset_minimal_Jan_2018.zip** - the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) - **dataset_full_Jan_2018.tgz** - full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. - **build_model.r, helpers.r** - R files to process the survival data (`survival_data.csv` in **dataset_minimal_Jan_2018.zip**, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in **dataset_full_Jan_2018.tgz**) - **Interview protocol.pdf** - approximate protocol used for semistructured interviews. - LICENSE - text of GPL v3, under which this dataset is published - INSTALL.md - replication guide (~2 pages)</pre> <pre>Replication guide ================= Step 0 - prerequisites ---------------------- - Unix-compatible OS (Linux or OS X) - Python interpreter (2.7 was used; Python 3 compatibility is highly likely) - R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): - up to 2Tb of disk space (see Step 2 detalization levels) - at least 16Gb of RAM (64 preferable) - few hours to few month of processing time Step 1 - software ---------------- - unpack **ghd-0.1.0.zip**, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. - copy `settings.py` into the extracted folder. Edit the file: * set `DATASET_PATH` to some newly created folder path * add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` - install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` - install libarchive and headers: `sudo apt-get install libarchive-dev` - (optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. - install Python libraries: `pip install --user -r requirements.txt` . - disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. Step 2 - obtaining the dataset ----------------------------- The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: ####Option 2.a, difficulty level: easiest Just use the precomputed data. Step 1 is not necessary under this scenario. - extract **dataset_minimal_Jan_2018.zip** - get `survival_data.csv`, go to the next step ####Option 2.b, difficulty level: easy Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. - create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` - extract **dataset_minimal_Jan_2018** to the newly created folder - rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv - edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. ####Option 2.c, difficulty level: medium Use predownloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. - Download **dataset_full_Jan_2018.tgz** from http://k.soberi.us/dataset_full_Jan_2018.tgz . This file is not included in this archive because of its size (5.4Gb compressed, 34Gb unpacked). - edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` - extract the content of this archive into `<DATASET_PATH>`. - clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. ####Option 2.d, difficulty level: hard Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). - ensure the `<DATASET_PATH>` is empty - add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. Step 3 - run the regression --------------------------- install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>

opencc-zeroDec 2011View details →

FIgS. 18–19. Urodacus butleri, n. sp., holotype ♂ (WAM T85141), dextral pedipalp chela manus, illustrating distribution of trichobothria (open circles). 18. External aspect. 19. Ventral aspect. Scale bar = 0.5 mm. Abbreviations: Db, dorsal basal; Dt, dorsal terminal; Est, external subter- minal; Et, external terminal; V, ventral.

FIgS. 18–19. Urodacus butleri, n. sp., holotype ♂ (WAM T85141), dextral pedipalp chela manus, illustrating distribution of trichobothria (open circles). 18. External aspect. 19. Ventral aspect. Scale bar = 0.5 mm. Abbreviations: Db, dorsal basal; Dt, dorsal terminal; Est, external subter- minal; Et, external terminal; V, ventral.

opencc-zeroDec 2011View details →

FIgS. 15–17. Urodacus butleri, n. sp., holotype ♂ (WAM T85141), dextral pedipalp patella, illustrating distribution of trichobothria (open circles). 15. Dorsal aspect. 16. External aspect. 17. Ventral aspect. Scale bar = 0.5 mm.

FIgS. 15–17. Urodacus butleri, n. sp., holotype ♂ (WAM T85141), dextral pedipalp patella, illustrating distribution of trichobothria (open circles). 15. Dorsal aspect. 16. External aspect. 17. Ventral aspect. Scale bar = 0.5 mm.

openother-openAug 2018View details →

Envixlab/OpenMICE: OpenMICE: an open spatial and temporal data set of small mammals in south-central Italy based on owl pellet data

Provided in support of the Data-paper: OpenMICE: an open spatial and temporal data set of small mammals in south-central Italy based on owl pellet data by Paniccia, C., M. Di Febbraro, L. Delucchi, R. Oliveto, M. Marchetti, and A. Loy. 2018. Ecology. <a href="https://github.com/Envixlab/OpenMICE/files/2273658/OpenMICE.sqlite.zip">OpenMICE.sqlite.zip</a>

5G-Xcast Open Spectrum data 5/6

This dataset is part 5/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

opencc-by-sa-4.0Jul 2018View details →

5G-Xcast Open Spectrum data 6/6

This dataset is part 6/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

5G-Xcast Open Spectrum data 4/6

This dataset is part 4/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

5G-Xcast Open Spectrum data 2/6

This dataset is part 2/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

5G-Xcast Open Spectrum data 1/6

This dataset is part 1/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

5G-Xcast Open Spectrum data 3/6

This dataset is part 3/6 of the open spectrum measurement data from Horizon 2020 -funded 5G-PPP project 5G-Xcast. The whole dataset includes a continuous 8-day period of spectrum data from January 20th to January 27th from four years, 2015 to 2018. The data and the measurement system is described in the Open Spectrum data.pdf file.

opencc-by-4.0Oct 2018View details →

Experteninterview mit Steffi Grimm für die Bachelorarbeit "Open Science an der HAW Hamburg - Empfehlungen zur Open-Access-Beratung"

Da sich die HAW Hamburg am hochschulübergreifenden Programm Hamburg Open Science beteiligt, ging die Autorin im Rahmen ihrer Bachelorarbeit Open Science an der HAW Hamburg - Empfehlungen zur Open-Access-Beratung der Frage nach, ob die Hochschule eine Publikations- bzw. Open-Access-Beratung einführen sollte. Hierfür wurden Best-Practice-Beispiele aus anderen Hochschulen analysiert und vier ausführliche Experteninterviews mit verschiedenen FachvertreterInnen geführt. Im Rahmen der Experteninterviews wurde Steffi Grimm (zum Zeitpunkt der Erhebung) aus dem Team Universitätsverlag/Hochschulschriften/Open Access der Universitätsbibliothek der Technischen Universität Berlin interviewt. Das Experteninterview fand am 19.12.2017 am Telefon statt. Die Länge des Interviews betrug 77 Minuten. Das folgende Transkript wurde auf die relevanten Informationen gekürzt und zur besseren Lesbarkeit stilistisch bearbeitet. Die Veröffentlichung der kompletten Bachelorarbeit soll noch erfolgen.

Experteninterview mit Michaela Voigt im Rahmen von Hamburg Open Science

Da sich die HAW Hamburg am hochschulübergreifenden Programm Hamburg Open Science beteiligt, wurde im Rahmen von Hamburg Open Science zur Entwicklung eines Konzeptes für eine Open-Access-freundliche Publikationskultur Michaela Voigt aus dem Team Universitätsverlag/Hochschulschriften/Open Access der Universitätsbibliothek der Technischen Universität Berlin interviewt. Das Experteninterview fand am 01.08.2018 an der UB der TU Berlin statt. Die Länge des Interviews betrug 49 Minuten. Das folgende Transkript wurde auf die relevanten Informationen gekürzt und zur besseren Lesbarkeit stilistisch bearbeitet.

Dataset for "Open-Circuit Voltage Deficit in Cu2ZnSnS4 Solar Cells by Interface Band Gap Narrowing"

Files in "ExchangeCorrelation": POSCAR_material_Exc material <ul> <li>CdS </li> <li>CZTS = Cu2ZnSnS4</li> <li>CZTSe = Cu2ZnSnSe4</li> </ul> Exchange-correlation functionals <ul> <li>PBE</li> <li>RP = revised PBE</li> <li>PBEsol</li> <li>AM05</li> <li>SCAN</li> <li>HSE06</li> <li>6_6: PBE+U (U= 6 eV for Cu d and Zn d)</li> <li>8_8: PBE+U (U= 8 eV for Cu d and Zn d)</li> </ul> Files in "Interface" POSCAR_S_fixed: CZTS/CdS interface, CdS layers were fully relaxed. POSCAR_S_PBE_U: CZTS/CdS interface, the atomic coordinates were relaxed using PBE+U POSCAR_S_SCAN_U: CZTS/CdS interface, the atomic coordinates were relaxed using SCAN+U POSCAR_Se_fixed: CZTSe/CdS interface, CdS layers were fully relaxed. POSCAR_Se_PBE_U: CZTSe/CdS interface, the atomic coordinates were relaxed using PBE+U POSCAR_Se_SCAN_U:  CZTSe/CdS interface, the atomic coordinates were relaxed using SCAN+U

Innovazione & imprecazione #6 -> L'open business model

Nella puntata di parlerà di: <ol> <li>cos'è l'open business model</li> <li>la differenza tra capitale cognitivo e informativo</li> <li>i modelli outside-in e inside-out per la Ricerca e Sviluppo</li> <li>l'esempio di Amazon e Lego</li> </ol>

Aegina beaches – Open Days Technical (ICCS)

This dataset was used during FabSpace 2.0 Open Days presentations. This is a private data belonging to National Ecological Research Center (PA.KOE). This dataset demonstrates which beaches are clean and which unsuitable for swimming

opencc-by-4.0Dec 2018View details →

Is there a social life in Open Data? Open datasets exploring practices in Educational Technology Research

In the landscape of Open Science, Open Data [OD] plays a crucial role as data are one of the most basic components of research despite their diverse formats across scientific disciplines. Opening up data is a recent concern for policy makers and researchers as the basis for good open science practices. The common factor underlying these new practices – the relevance of promoting open data circulation and reuse – is mostly a social form of knowledge sharing and construction. However, while data sharing is being strongly promoted by policy making and is becoming a frequent practice in some disciplinary fields, open data sharing is much less developed in social sciences and in educational research. The Open Data hereby introduced is composed of two integrated datasets which collect data relating practices of Open Data publication and sharing in the field of Educational Thecnologies. This data was collected to suport the aim of investigating open data sharing in a selection of open data repositories as well as in the academic social network site ResearchGate. The research questions addressing this study are: <ol> <li>Do researchers in the field of Educational Technology publish Open Datasets [ODs]?</li> <li>To which extent are ODs compliant with the FAIR data principles?</li> <li>What is the social life relating to the ODs in terms of the metrics provided by the OD portals? As a subsidiary question: 3.a- To what extent do open data portals allow researchers to cultivate social practices around OD?</li> </ol>   In order to investigate OD presence in ResearchGate, the following research aim guided the second part of the study: Analysis of the presence of the same selected OD in ResearchGate and of the type of social activity OD exhibited by OD according to ResearchGate mètrics. The Open Data here presented is composed by two datasets. <ol> <li>23 datasets extracted out from 82 randomly selected datasets, from an inital total of 633. The data set presents a Codebook explaining the several categories of analysis or variables and the values assigned to the same. Since the analysis was performed in two phases (first selection and screening, second selection and classification) the codebook is divided in two tables. Moreover, the data set contains 6 Worksheets, ordered taking the workflow as reference: Codebook, Dataset_Extraction, Analysis_Workflow, Selected_Analysis. This dataset has been produced and curated by J.E.Raffaghelli</li> <li>The analysis of correspondance for the 23 datasets in ResearchGate. The data set presents a Codebook, and is divided into two worksheets: Codebook, ODT&RG. The last worksheet introduces the comparison between the social mètrics within RG of the found datasets and the relating papers. This dataset has been produced and curated by S. Manca and J.E. Raffaghelli.</li> </ol>