Find research datasets worth reusing

opencc-by-4.0Sep 2022View details →

Mouse SIRV and simulated data used in the IsoQuant publication

This dataset contain main simulated and SIRV sequencing data used in the <a href="https://doi.org/10.1038/s41587-022-01565-y">IsoQuant publication</a>. - Reduced mouse GENCODE vM26 annotation: 15% of expressed isoforms removed, expression profile based on real mouse brain sequencing data; also contains GTF with expressed transcripts. - Mouse PacBio simulated data: 6 million reads generated with <a href="https://github.com/yunhaowang/IsoSeqSim">IsoSeqSim</a>, 1.4% error rate. Provided as a sorted BAM file, reads were mapped to GRCm39 genome using minimap v2.18. - Mouse ONT R10.4 simulated data: 30 million read generated with <a href="https://github.com/bcgsc/NanoSim">NanoSim</a>, 2.8% error rate. Provided as a sorted BAM file, reads were mapped to GRCm39 genome using minimap v2.18. - Mouse ONT R9.4 simulated data: 30 million read generated with <a href="https://github.com/bcgsc/NanoSim">NanoSim</a>, 15.9% error rate. Provided as a sorted BAM file, reads were mapped to GRCm39 genome using minimap v2.18. - <a href="https://www.lexogen.com/store/sirv-set4/">Lexogen SIRV Set4</a> sequenced on ONT MinIon with R10.4 chemistry. Provided as a sorted BAM file, reads were mapped to SIRV reference using minimap v2.18. This data was used in the IsoQuant publication. - <a href="https://www.lexogen.com/store/sirv-set4/">Lexogen SIRV Set4</a> sequenced on ONT MinIon with R9.4 chemistry, filtered >=Q12. Provided as a sorted BAM file, reads were mapped to SIRV reference using minimap v2.18. This data was NOT used in the IsoQuant publication. - SIRV reference genome and reduced annotation, also available at <a href="https://www.lexogen.com/sirvs/download/">Lexogen download page</a>.

X-ray scattering datasets associated with the publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation"

X-ray scattering datasets for samples described in the 2020 publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation". This dataset includes both raw and processed X-ray scattering data for samples PTCHSiO-Pr, Bu, Hx, Oc and De, alongside background measurements files (BKG).

opencc-by-4.0Dec 2022View details →

Dataset for publication Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7

Open dataset for publication  Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7. https://doi.org/10.1186/s40069-022-00568-y File 1 - X-ray diffractogram and particle size distribution (laser granulometry) data - *.opju (Origin) File 2 - Rheological test results - *.opju (Origin) File 5 - Mechanical peformance (early strength - ultrasounds and compressive strength) and density test results - *.opju (Origin) File 4 - Mercury intrusion porosimetry test data - *.opju (Origin)

X-ray scattering datasets associated with the publication "Side chain length dependent dynamics and conductivity in self assembled ion channels"

X-ray scattering datasets for samples described in the 2022 publication "Side chain length dependent dynamics and conductivity in self assembled ion channels". This dataset includes both raw and processed X-ray scattering data for samples ILC8, ILC10, ILC12, ILC14 and ILC16 alongside background measurement files (BKG).

A Dataset of UN Agencies' Public Communication about Climate Change on Twitter

The present dataset contains the Twitter communication of eight international organizations (IOs) in different policy areas that are known to be central in communicating about climate change. The IOs are comparable in their communication, all being parts of the United Nations (UN). The IOs under consideration are: <ul> <li>Food and Agriculture Organization (FAO),</li> <li>Office for the Coordination of Humanitarian Affairs (UNOCHA),</li> <li>UN Development Programme (UNDP),</li> <li>UN Office for Disaster Risk Reduction (UNDRR),</li> <li>UN Environmental Program (UNEP),</li> <li>UN International Children’s Emergency Fund (UNICEF),</li> <li>UN High Commissioner for Refugees (UNHCR),</li> <li>World Health Organization (WHO).</li> </ul> The tweets were downloaded and parsed via the Twitter Academic Research API (<a href="https://developer.twitter.com/en/products/twitter-api/academic-research">link</a>). In total, the dataset contains 222,191 tweet IDs of the tweets posted by the above 8 UN organizations from their official accounts. This number represents the total number of tweets posted by these selected UN organizations since the beginning of their tweeting history until the end of 2019. The dataset is compliant with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter and the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management. The dataset consists of two parts: <ul> <li>Unlabeled tweet IDs of the considered IOs (8 txt-files),</li> <li>Labeled dataset of tweet IDs with labels indicating whether tweets are about climate change or not (1 csv-file).</li> </ul> Unlabeled tweet IDs The corresponding 8 txt-files contain tweet IDs of the corresponding tweets posted by the UN organizations. The files are summarised in Table 1 below.    <table align="center"> <caption>Table 1. Summary of the collected dataset files.</caption> <tbody> <tr> <td>File</td> <td>Organization</td> <td>Account</td> <td>Start date</td> <td>End date</td> <td>Tweet IDs</td> </tr> <tr> <td>tweet_ids_FAO_2009_2019.txt</td> <td>FAO</td> <td>@FAO</td> <td>Jan. 2009</td> <td>Dec. 2019</td> <td>28,630</td> </tr> <tr> <td> tweet_ids_UNDP_2009_2019.txt </td> <td>UNDP</td> <td>@UNDP</td> <td>Jul. 2009</td> <td>Dec. 2019</td> <td>47,960</td> </tr> <tr> <td> tweet_ids_UNDRR_2009_2019.txt </td> <td>UNDRR</td> <td>@UNDRR</td> <td>Oct. 2010</td> <td>Dec. 2019</td> <td>9,735</td> </tr> <tr> <td> tweet_ids_UNEP_2009_2019.txt </td> <td>UNEP</td> <td>@UNEP</td> <td>May 2009</td> <td>Dec. 2019</td> <td>21,615</td> </tr> <tr> <td> tweet_ids_Refugees_2008_2019.txt </td> <td>UNHCR</td> <td>@Refugees</td> <td>Jun. 2008</td> <td>Dec. 2019</td> <td>42,882</td> </tr> <tr> <td> ttweet_ids_UNICEF_2009_2019.txt </td> <td>UNICEF</td> <td>@UNICEF</td> <td>Jul. 2009</td> <td>Nov. 2019</td> <td>34,288</td> </tr> <tr> <td> tweet_ids_UNOCHA_2011_2019.txt </td> <td>UNOCHA</td> <td>@UNOCHA</td> <td>Jul. 2011</td> <td>Jul. 2019</td> <td>12,521</td> </tr> <tr> <td> tweet_ids_WHO_2008_2019.txt </td> <td>WHO</td> <td>@WHO</td> <td>May 2008</td> <td>Dec. 2019</td> <td>24,560</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td> </td> <td>Total</td> <td>222,191</td> </tr> </tbody> </table> The dataset contains only tweet IDs to ensure compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The tweet IDs need to be hydrated to be used. For hydrating the present dataset, the Hydrator application (<a href="https://github.com/DocNow/hydrator/releases">link</a>) may be used; see a step-by-step tutorial on how to use Hydrator (<a href="http://towardsdatascience.com/learn-how-to-easily-hydrate-tweets-a0f393ed340e#:~:text=Hydrating%20Tweets">link</a>). Labeled dataset related to climate change This is a subset of the entire dataset described above. Namely, 5,750 tweets are randomly selected from the entire dataset and labeled manually as either "climate change-related" or "not climate change-related". The dataset is available in the file dataset_UN_climate_change_labeled.csv and is summarised in Table 2 below.  <table align="center"> <caption>Table 2. Summary of the labeled dataset.</caption> <tbody> <tr> <td>Organization</td> <td>Tweets</td> </tr> <tr> <td>FAO</td> <td>753</td> </tr> <tr> <td>UNDP</td> <td>1,199</td> </tr> <tr> <td>UNDRR</td> <td>256</td> </tr> <tr> <td>UNEP</td> <td>540</td> </tr> <tr> <td>UNHCR</td> <td>1,114</td> </tr> <tr> <td>UNICEF</td> <td>910</td> </tr> <tr> <td>UNOCHA</td> <td>366</td> </tr> <tr> <td>WHO</td> <td>612</td> </tr> <tr> <td>Total</td> <td>5,750</td> </tr> </tbody> </table>

opencc-by-4.0Dec 2022View details →

Data of publication: "Many-Body Radiative Decay in Strongly Interacting Rydberg Ensembles"

The uploaded files contain the data of the simulations presented in the figures in <a href="https://doi.org/10.1103/PhysRevLett.129.243202">https://doi.org/10.1103/PhysRevLett.129.243202</a>.

opencc-by-4.0Jan 2023View details →

Data from: Unveiling the paths of COVID-19 in a large city based on public transportation data

Human mobility plays a key role in the dissemination of infectious diseases around the world. However, the complexity introduced by commuting patterns in the daily life of cities makes such a role unclear, especially at the intracity scale. Here, we propose a multiplex network fed with 9 months of mobility data with more than 107 million public bus validations in order to understand the relation between urban mobility and the spreading of COVID-19 within a large city, namely, Fortaleza in the northeast of Brazil. Our results suggest that the shortest bus rides in Fortaleza changed ≈ 25% more than the longest ones after an epidemic wave. Such a result is the opposite of what has been observed at the intercity scale. We also find that mobility changes among the neighborhoods are synchronous and geographically homogeneous. Furthermore, we find that the most central neighborhoods in mobility are the primary targets for infectious disease outbreaks, which is quantified here in terms of the linear relation between the disease arrival time and the average of the closeness centrality ranking. These central neighborhoods are also the top neighborhoods in the number of reported cases at the end of an epidemic wave as indicated by the exponential decay behavior of the disease arrival time in relation to the number of accumulated reported cases with decay constant λ ≈ 33 days. We believe that these results can help in the development of new strategies to impose restriction measures in the cities guiding decision-makers with smart actions in public health policies, as well as supporting future research on urban mobility and epidemiology.

Code for the publication "DOSE - Global data set of reported sub-national economic output"

The zipped file of this repository contains code and auxiliary data to reproduce the results of the publication: Wenz et al, "DOSE - Global data set of reported sub-national economic output" The respective DOSE database, version2 can be found here: <a href="http://doi.org/10.5281/zenodo.4681305">https://zenodo.org/record/7573249#.Y_RYTnbMI2w</a> Please see the README document for descriptions of the required dependencies.

opencc-by-4.0Dec 2006View details →

Fig. 4 in EDITORIAL The Natal Museum and its journals: celebrating a century of publication, 1906-2005

Fig. 4. Number of natural science pages (upper curve) and nomenclatural acts (lower curve) counted by 5-year clusters.

Data for publication "Lipid oxidation controls peptide self-assembly near membranes through a surface attraction mechanism"

The data provided refer to our published article: T. John,* S. Piantavigna, T. J. A. Dealey, B. Abel, H. J. Risselada, L. L. Martin*, Lipid oxidation controls peptide self-assembly near membranes through a surface attraction mechanism, Chem. Sci. 14 (2023), 3730-3741. <a href="https://doi.org/10.1039/d3sc00159h">https://doi.org/10.1039/d3sc00159h</a>.

opencc-by-4.0Jan 2023View details →

Supporting publication for 'Prevalence sample-based guidance for reporting 2022 data'

The record is aimed at helping the reporting countries to submit their sample-based level data to the EFSA Data Collection Framework. We include here two excel files and one XML file, and we give below specific information on their use. The two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes, and offer examples on how prevalence data can be reported using SSD2 and how data are aggregated afterwards. The XML file is the same example as in the Excel file with similar title but in the XML format that allows for it be uploaded in the Data Collection Framework.

EFSA's Public MetaPath database

This MTB file contains a collection of maps publicly available that can be used with the MetaPath platform to search and analyse experimental data on metabolism or catabolism. The External Scientific Report by EFSA's contractor, the German Federal Institute for Risk Assessment (BfR), describes how the extraction and coding of the data on which this database was based was conducted.  For more details and background information please consult <a href="https://www.efsa.europa.eu/en/applications/pesticides/tools">EFSA website</a>   Intellectual Property Rights Notice The reproduction, distribution, redistribution, exploiting, making commercial or further use of information, documents and data posted or otherwise made available on this website or in the websites linked to it may be subject to protection under intellectual property rights regulations, data exclusivity clauses or other applicable law, and their utilisation without obtaining the prior permission from the right(s)holder(s) of the respective information, documents and data might violate the pre-existing rights of the respective right(s)holder(s). For materials subject to intellectual property rights or other rights of a third party, the User must comply with the terms of use associated with such material or obtain the necessary and written permission for reproduction, distribution or any other use from the right(s)holder(s). EFSA does not accept any responsibility, and shall not be held liable, for any violation of any pre-existing rights or other infringements related to information, documents and data made available on this website or in the websites linked to it.

Looking Forwards and Backwards: 10 Years of Publications

The dataset is basis for the publication "Looking Forwards and Backwards: 10 Years of Publications" in the Special Issue "Scholarly communication over a decade of Publications" in the journal Publications (MPDI). The dataset includes the original data generated and applied for the analysis as well as the figures shown in the publication. The Data Management Plan File lists all files inklc. descriptions.

opencc-by-4.0Dec 2022View details →

Database Publication - Exploring adult age-at-death research in anthropology: Bibliometric mapping and content analysis, Forensic Sciences, 2023

The dataset contains the publications' ID and DOI numbers used in the bibliometric analysis for the paper "Exploring adult age-at-death research in anthropology: Bibliometric mapping and content analysis," which was published in Forensic Sciences. Dimensions only granted the authors permission to disclose the publication IDs and DOIs for this study. However, using the identification codes, it is possible to extract titles' full metadata through Dimensions (<a href="https://www.dimensions.ai/">https://www.dimensions.ai/</a>).

Graph 7: Publication of Heiner Müller Plays Per Year.

Graph 7 shows the sum of Heiner Müller plays published per year from 1957-1998.

Dataset Graphs 3-5: Publication Types for Heiner Müller's Writings 1967–1977, 1978-1988, 1989-98.

Dataset for Graphs 3-5. Data collected based on the Heiner Müller Werkausgabe. Graphs 3-5 shows the publication types that Heiner Müller used to publish his writing in the years from 1967–1977, 1978-1988, 1989-98.

Dataset Graph 7: Publication of Heiner Müller Plays Per Year.

Dataset for Graph 7. Data collected based on the Heiner Müller Werkausgabe. Graph 7 shows the sum of Heiner Müller plays published per year from 1957-1998.

Data set for figure 2-4 from publication "Missed Evaporation from Atmospherically Relevant Inorganic Mixtures Confounds Experimental Aerosol Studies",

Data set for figure 2-4 from publication "Missed Evaporation from Atmospherically Relevant Inorganic Mixtures Confounds Experimental Aerosol Studies".

CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 2

This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset and the Musall et al. mouse dataset processed by MINE. This set contains the last fish and the mouse MINE model weights. The other 24 fish are contained in Set 1.

CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 1

This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset processed by MINE. This set contains 24/25 fish. The last fish and mouse MINE model weights are contained in Set 2.