Find research datasets worth reusing

opencc-by-4.0Sep 2021View details →

Benchmark dataset for preprint: "EDEN: A high-performance, general-purpose, NeuroML-based neural simulator"

The benchmark files and scripts to reproduce the figures of the preprint  "EDEN: A high-performance, general-purpose, NeuroML-based neural simulator" ( https://arxiv.org/abs/2106.06752 ) The benchmarks require a computer running Linux with Docker installed. Unpack the paper_experiments.zip file and follow the instructions in the README.md file to run the benchmarks and reproduce the figures.

opencc-by-4.0Feb 2019View details →

The Red Queen in the Repository: metadata quality in an ever-changing environment (preprint of paper, presentation slides and dataset collection with validation schemas to IDCC2019 conference paper)

This fileset contains a preprint version of the conference paper (.pdf), presentation slides (as .pptx) and the dataset(s) and validation schema(s) for the IDCC 2019 (Melbourne) conference paper: The Red Queen in the Repository: metadata quality in an ever-changing environment. Datasets and schemas are  in .xml, .xsd , Excel (.xlsx) and .csv  (two files representing two different sheets in the .xslx -file). The validationSchemas.zip holds the additional validation schemas (.xsd), that were not found in the schemaLocations of the metadata xml-files to be validated. The schemas must all be placed in the same folder, and are to be used for validating the Dataverse dcterms records (with metadataDCT.xsd) and the Zenodo oai_datacite feeds respectively (schema.datacite.org_oai_oai-1.0_oai.xsd). In the latter case, a simpler way of doing it might be to replace the incorrect URL "http://schema.datacite.org/oai/oai-1.0/ oai_datacite.xsd" in the schemaLocation of these xml-files by the CORRECT:  schemaLocation="http://schema.datacite.org/oai/oai-1.0/ http://schema.datacite.org/oai/oai-1.0/oai.xsd"  as has been done already in the sample files here. The sample file folders testDVNcoll.zip (Dataverse), testFigColl.zip (Figshare) and testZenColl.zip (Zenodo) contain all the metadata files tested and validated that are registered in the spreadsheet with objectIDs. In the case of Zenodo, one original file feed, zen2018oai_datacite3orig-https%20_zenodo.org_oai2d%20verb=ListRecords%26metadata Prefix=oai_datacite%26from=2018-11-29%26until=2018-11-30.xml , is also supplied to show what was necessary to change in order to perform validation as indicated in the paper. For Dataverse, a corrected version of a file, dvn2014ddi-27595Corr_https%20_dataverse.harvard.edu_api_datasets_export%20 exporter=ddi%26persistentId=doi%253A10.7910_DVN_27595Corr.xml , is also supplied in order to show the changes it would take to make the file validate without error.

opencc-by-4.0Nov 2019View details →

Questions for future developments in the preprints landscape

This submission includes one file complementing the F1000Research article "Preprints and Scholarly Communication: Adoption, Practices, Drivers and Barriers" - <a href="https://doi.org/10.12688/f1000research.19619.1">https://doi.org/10.12688/f1000research.19619.1</a> The table 'Questions for future developments in the preprints landscape' lists a number of key questions that we believe need to be addressed so that preprints can be supported sustainably in the future, along with their owners. More information on this study is also available in the form of a <a href="http://doi.org/10.5281/zenodo.3357727">report</a>.

opencc-by-4.0May 2021View details →

Complete Rxivist dataset of scraped biology preprint data

<a href="https://rxivist.org">rxivist.org</a> allowed readers to sort and filter the tens of thousands of preprints posted to <a href="https://www.biorxiv.org">bioRxiv</a> and <a href="https://www.medrxiv.org">medRxiv</a>. Rxivist used a custom web crawler to index all papers posted to those two websites; this is a snapshot of Rxivist the production database. The version number indicates the date on which the snapshot was taken. See the included "README.md" file for instructions on how to use the "rxivist.backup" file to import data into a PostgreSQL database server. Please note this is a different repository than the one used for <a href="https://www.biorxiv.org/content/early/2019/01/13/515643">the Rxivist manuscript</a>—that is in <a href="https://doi.org/10.5281/zenodo.2465689">a separate Zenodo repository</a>. You're welcome (and encouraged!) to use this data in your research, but please cite our paper, now published <a href="https://doi.org/10.7554/eLife.45133">in eLife</a>. Previous versions are also available pre-loaded into Docker images, available at <a href="https://hub.docker.com/r/blekhmanlab/rxivist_data">blekhmanlab/rxivist_data</a>. Version notes: <ul> <li>2023-03-01 <ul> <li>The final Rxivist data upload, more than four years after the first and encompassing 223,541 preprints posted to bioRxiv and medRxiv through the end of February 2023.</li> </ul> </li> <li>2020-12-07*** <ul> <li>In addition to bioRxiv preprints, the database now includes all medRxiv preprints as well. <ul> <li>The website where a preprint was posted is now recorded in a new field in the "articles" table, called "repo".</li> </ul> </li> <li>We've significantly refactored the web crawler to take advantage of developments with the bioRxiv API. <ul> <li>The main difference is that preprints flagged as "published" by bioRxiv are no longer recorded on the same schedule that download metrics are updated: The Rxivist database should now record published DOI entries the same day bioRxiv detects them.</li> </ul> </li> <li>Twitter metrics have returned, for the most part. Improvements with the Crossref Event Data API mean we can once again tally daily Twitter counts for all bioRxiv DOIs. <ul> <li>The "crossref_daily" table remains where these are recorded, and daily numbers are now up to date.</li> <li>Historical daily counts have also been re-crawled to fill in the empty space that started in October 2019.</li> <li>There are still several gaps that are more than a week long due to missing data from Crossref.</li> <li>We have recorded available Crossref Twitter data for all papers with DOI numbers starting with "10.1101," which includes all medRxiv preprints. However, there appears to be almost no Twitter data available for medRxiv preprints.</li> </ul> </li> <li>The download metrics for article id 72514 (DOI 10.1101/2020.01.30.927871) were found to be out of date for February 2020 and are now correct. This is notable because article 72514 is the most downloaded preprint of all time; we're still looking into why this wasn't updated after the month ended.</li> </ul> </li> <li>2020-11-18 <ul> <li>Publication checks should be back on schedule.</li> </ul> </li> <li>2020-10-26 <ul> <li>This snapshot fixes most of the data issues found in the previous version. Indexed papers are now up to date, and download metrics are back on schedule. The check for publication status remains behind schedule, however, and the database may not include published DOIs for papers that have been flagged on bioRxiv as "published" over the last two months. Another snapshot will be posted in the next few weeks with updated publication information.</li> </ul> </li> <li>2020-09-15 <ul> <li>A crawler error caused this snapshot to exclude all papers posted after about August 29, with some papers having download metrics that were more out of date than usual. The "last_crawled" field is accurate.</li> </ul> </li> <li>2020-09-08 <ul> <li>This snapshot is misconfigured and will not work without modification; it has been replaced with version 2020-09-15.</li> </ul> </li> <li>2019-12-27 <ul> <li>Several dozen papers did not have dates associated with them; that has been fixed.</li> <li>Some authors have had two entries in the "authors" table for portions of 2019, one profile that was linked to their ORCID and one that was not, occasionally with almost identical "name" strings. This happened after bioRxiv began changing author names to reflect the names in the PDFs, rather than the ones manually entered into their system. These database records are mostly consolidated now, but some may remain.</li> </ul> </li> <li>2019-11-29 <ul> <li>The Crossref Event Data API remains down; Twitter data is unavailable for dates after early October.</li> </ul> </li> <li>2019-10-31 <ul> <li>The Crossref Event Data API is still <a href="https://status.crossref.org/">experiencing problems</a>; the Twitter data for October is incomplete in this snapshot.</li> <li>The README file has been modified to reflect changes in the process for creating your own DB snapshots if using the newly released PostgreSQL 12.</li> </ul> </li> <li>2019-10-01 <ul> <li>The Crossref API is back online, and the "crossref_daily" table should now include up-to-date tweet information for July through September.</li> <li>About 40,000 authors were removed from the author table because the name had been removed from all preprints they had previously been associated with, likely because their name changed slightly on the bioRxiv website ("John Smith" to "J Smith" or "John M Smith"). The "author_emails" table was also modified to remove entries referring to the deleted authors. The web crawler is being updated to clean these orphaned entries more frequently.</li> </ul> </li> <li>2019-08-30 <ul> <li>The Crossref Event Data API, which provides the data used to populate the table of tweet counts, has not been fully functional since early July. While we are optimistic that accurate tweet counts will be available at some point, the sparse values currently in the "crossref_daily" table for July and August should not be considered reliable.</li> </ul> </li> <li>2019-07-01 <ul> <li>A new "institution" field has been <a href="https://github.com/blekhmanlab/rxivist/commit/1cb570703085841e80cc3073af445bc86f0cbb63#diff-a04b1c1a66d16f9b3acfed9b9d2128c5">added</a> to the "article_authors" table that stores each author's institutional affiliation as listed on that paper. The "authors" table still has each author's most recently observed institution. <ul> <li>We began collecting this data in the middle of May, but it has not been applied to older papers yet.</li> </ul> </li> </ul> </li> <li>2019-05-11 <ul> <li>The README was updated to correct a link to the Docker repository used for the pre-built images.</li> </ul> </li> <li>2019-03-21 <ul> <li>The license for this dataset has been changed to <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY</a>, which allows use for any purpose and requires only attribution.</li> <li>A new table, "publication_dates," has been added and will be continually updated. This table will include an entry for each preprint that has been published externally for which we can determine a date of publication, based on data from Crossref. (This table was previously included in the "paper" schema but was not updated after early December 2018.)</li> <li>Foreign key constraints have been added to almost every table in the database. This should not impact any read behavior, but anyone writing to these tables will encounter constraints on existing fields that refer to other tables. Most frequently, this means the "article" field in a table will need to refer to an ID that actually exists in the "articles" table.</li> <li>The "author_translations" table has been removed. This was used to redirect incoming requests for outdated author profile pages and was likely not of any functional use to others.</li> <li>The "README.md" file has been renamed "1README.md" because Zenodo only displays a preview for the file that appears first in the list alphabetically.</li> <li>The "article_ranks" and "article_ranks_working" tables have been removed as well; they were unused.</li> </ul> </li> <li>2019-02-13.1 <ul> <li>After consultation with bioRxiv, the "fulltext" table will not be included in further snapshots until (and if) concerns about licensing and copyright can be resolved.</li> <li>The "docker-compose.yml" file was added, with corresponding instructions in the README to streamline deployment of a local copy of this database.</li> </ul> </li> <li>2019-02-13 <ul> <li>The redundant "paper" schema has been removed.</li> <li>BioRxiv has begun making the full text of preprints available online. Beginning with this version, a new table ("fulltext") is available that contains the text of preprints that have been processed already. The format in which this information is stored may change in the future; any digression will be noted here.</li> <li>This is the first version that has <a href="https://cloud.docker.com/u/blekhmanlab/repository/docker/blekhmanlab/rxivist_data">a corresponding Docker image</a>.</li> </ul> </li> </ul>

opencc-zeroJan 2019View details →

Practices and policies of preprint platforms for life and biomedical sciences

Given the increase in the use and profile of preprint servers – and alternative publishing hybrid platforms such as F1000 Research – in the life sciences, it is increasingly important to identify how many such servers and hybrids exist, to describe their scope in terms of the scientific disciplines they cover, and to compare and contrast their characteristics and policies. We surveyed forty-four (44) platforms that host preprints relevant to life and biomedical sciences and that were active online and accepting submissions on 25 June 2019. Information on preprint platform policies, features and practices was collected through online research by the authors and by surveying preprint platform representatives directly.  Full data sheets include an additional 5 platforms hosted on OSF Preprints (rows 49-53) to fulfil the wider scope for the ASAPbio project, not in disciplinary scope (biology and medical sciences) for the manuscript with Jamie Kirkham. Tables 1-5: Data (44 platforms, manuscript) are separated into five main tables of information and a list of preprint platform websites for reference. Table 1: Scope and ownership of each server Table 2: Content-specific characteristics and information relating to submission, journal transfer options, and external discoverability Table 3: Screening, moderation, and permanence of content Table 4: Usage metrics and other features Table 5: Metadata Preprint platform websites Data for each platform are listed as ‘Verified’ in the tables if these tables (V1.0 or V2.0) were seen and approved by a platform representative between January 13 and January 27, 2020. Original online survey: a blank copy of the original survey form used by online researchers (the authors) and supplied pre-filled (or empty, in some cases) to preprint platform representatives for verification (or completion, in some cases).  Final data: survey data is presented in .txt and .xlsx, as follows: <ul> <li>Row 1: Heading (where field is included in manuscript tables, the heading presented here replaces any heading used in original survey. All columns are presented in the order the information was requested on the original survey form, with some supplementary columns added and columns removed (detailed below).</li> <li>Row 2: Schema or description of field</li> <li>Row 3: Whether and where included in manuscript tables. For supporting information for table data (e.g. source information, URLs), the table location for supported data is indicated in brackets, e.g. (Table 2) and supporting information is not included in tables. Data included in manuscript tables is presented in its final form, which in some cases is simplified from the original survey data. This simplified version of the data was presented to platform representatives for additional verification (v1.0/v2.0 verification). Data not included in manuscript tables is presented here as verified by platform representatives and/or found online. Some columns from the original survey have been removed due to the information not being informative or useful: specifically, Print ISSN (not reported for any platform); End date (no platforms have an end date; although two platforms stopped accepting submissions after survey completed; Personal contact information for platform representative(s) has been removed).</li> <li>Rows 4 onwards: data for each preprint platform (44 included in manuscript (rows 4-47), plus 5 additional OSF platforms (rows 48-52)</li> <li>Columns 3-6 (D-G) report online research and verification information and Column 13 (M) reports an additional data field (number of articles) – these are supplementary to the original survey columns</li> <li>Verification status: Released V1/V2 data applies to data included in manuscript tables (as indicated in row 3); Online survey data applies to data used for manuscript tables and also to original survey data included here but not included in manuscript tables (‘Not included’ in row 3)</li> <li>Note that data fields are presented as individual columns in these sheets, while some entries in Tables 1-5 combine several data fields.</li> </ul> These data were collected in collaboration and as part of: i. An ASAPbio project, led by Dr Naomi Penfold, to develop an online directory of preprint platforms ii. A research project led by Prof Jamie Kirkham These data are supplementary outputs for both projects. Data v1.0 were presented during the ASAPbio January 2020 workshop – see Penfold, Naomi C, & Polka, Jessica. (2020, January). ASAPbio Preprint Platform Directory: 2019 data (presentation) (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.3626770. Version 3.0 updates (December 14, 2020): added new files with updated information about servers from the ASAPbio preprint directory (https://asapbio.org/preprint-servers), provided by Jessica Polka (now included as author).

opencc-by-4.0Feb 2022View details →

Preprint Citations in PLOS Dataset

Preprints are research articles that have been published online before undergoing peer review. The role of preprints in the scientific production has been growing in recent years. Our objective is to study these practices and evaluate the differences that exist between citations to preprints and citations to peer-reviewed articles. This dataset contains citation contexts to preprints extracted from the PLOS dataset. We have processed all PLOS articles published up to January 2021. Preprint citations were identified by matching cited source metadata against a list of existing preprint databases. For each citation we have extracted the sentence and its position in the IMRaD structure of the article. The data is presented in a tsv file that contains the following columns : <ul> <li>id: identifier.</li> <li>source_name: name of the preprint database where the preprint is published. In some cases source_name is "preprint kw" which means that it has been identified by the presence of the "preprint" keyword in the source metadata, but could not be linked to a known preprint database.</li> <li>jtitle: title of the PLOS journal from which the citation context is extracted.</li> <li>imrad_code: one of "I", "M", "R", "D", indicating the name of the section of the citation context in the IMRaD (Introduction, Methods, Results and Discussion) structure.</li> <li>perc: a number between 0 and 100, indicating the position of the citation context in terms of percentage of the text progression of the section in which it appears. This position has been calculated by dividing the number of the sentence of the citation context by the total number of sentences in the section.</li> <li>pub_year: publication year of the article</li> <li>sentence_text: sentence containing the citation to the preprint.</li> </ul> The full description of the dataset and the processing steps to obtain it are described in: Bertin, Marc and Atanassova, Iana (2022). "Preprint Citation Praxis in PLOS". Scientometrics.

opencc-by-4.0Apr 2022View details →

data set to bioRxiv preprint 'Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation

This is supporting data and software code for the following preprint in bioRxiv Persistent cross-species SARS-CoV-2 variant infectivity predicted via comparative molecular dynamics simulation https://www.biorxiv.org/content/10.1101/2022.04.18.488629v1

opencc-zeroJan 2019View details →

Data from: Tracking the popularity and outcomes of all bioRxiv preprints

The data used to generate figures in the manuscript titled <a href="https://www.biorxiv.org/content/early/2019/01/13/515643">"Tracking the popularity and outcome of all bioRxiv preprints,"</a> posted to bioRxiv 13 Jan 2019. <ul> <li>22 Mar 2019: PDFs of each figure from the paper have been added to the repository. In addition, the license has been changed from CC-BY-NC to CC0.</li> </ul>

opencc-zeroJun 2019View details →

Preprints in biology as a fraction of the biomedical literature

These data and chart present an approximate calculation of the proportion of preprints in biology when compared to publications in PubMed, based on monthly figures and incorporating monthly preprint submissions (or counts) across a selection of servers relevant to biology.  Version 1.0 of these data represents data from January 2007 until May 31, 2019 for preprint servers: arXiv q-bio, Nature Precedings, F1000Research*, PeerJ Preprints*, bioRxiv**, Winnower*, <a href="http://preprints.org">preprints.org</a>, Wellcome Open Research*.  * Counts may not be specific to biology preprints only; ** Counts may include all versions posted that month, so may be an overestimate for version 1 submissions. From January 2019, data has been gathered manually by the authors, as per the methods described in the .csv here, and is included here in 'Preprints_per_month_direct_2019-01to05.csv'. Until December 2018, monthly preprint submissions data are based on those contributed by Jordan Anaya (ORCID: <a href="https://orcid.org/0000-0002-6166-4113">https://orcid.org/0000-0002-6166-4113</a>) for PrePubMed, source: <a href="https://raw.githubusercontent.com/OmnesRes/prepub/master/analyses/preprint_data.txt">https://raw.githubusercontent.com/OmnesRes/prepub/master/analyses/preprint_data.txt</a>; Github repository: <a href="https://github.com/OmnesRes/prepub">https://github.com/OmnesRes/prepub</a>; website: <a href="http://www.prepubmed.org/">http://www.prepubmed.org</a>). Data are not included here, they are provided from the source linked above under MIT license associated with the website code: <a href="https://github.com/OmnesRes/prepub/blob/master/LICENSE">https://github.com/OmnesRes/prepub/blob/master/LICENSE</a>. A live version of these data and the chart are available from this GSheet: <a href="https://docs.google.com/spreadsheets/d/1bkGEcfQcL0LpIanVqNHci1ZFY6oVNGz7IQbEugzkqkU/edit?usp=sharing">https://docs.google.com/spreadsheets/d/1bkGEcfQcL0LpIanVqNHci1ZFY6oVNGz7IQbEugzkqkU/edit?usp=sharing</a>. Between version updates here, please refer to this sheet for updated counts and method updates e.g. to include more servers and ensure only version 1 submissions are counted. For more information, please contact naomi.penfold@asapbio.org. When presenting these data and/or chart, please attribute to ASAPbio (https://asapbio.org, twitter: @ASAPbio_).

opencc-by-4.0Aug 2024View details →

Dataset for the preprint: "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich"

Dataset supporting the submission of the manuscript titled "Intercomparison of biogenic CO2 flux models in four urban parks in the city of Zurich" to the to the international journal "Biogeosciences". Meteorological data Hourly aggregated meteorological dataset for the urban area of Zurich, originating from two urban stations: Kaserne (8°32'/47°23'), which is a station of the Swiss national air pollution monitoring network NABEL, and Hardau II (8°30'/47°23'), which is a station established for the ICOS-Cities project.  Zurich Kaserne is located in a large courtyard. Wind and global radiation are measured on top of a four-storey building. Wind is measured at 35 m and global radiation at 27 m above ground. Hardau II station is established on the top of a high-rise building (110 m a.g.l.). Meteorological observation gaps were filled using Copernicus ERA5-Land data. Data format: comma separated values (csv) Time step: 60 min (aggregated) Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period Period: 01/2022–09/2023 Monthly mean atmospheric CO2 concentration data derived from the ICOS-Cities Hardau II station (07/2022–09/2023) and the Beromunster station (11/2012–02/2022). Observation gaps were filled using Copernicus ERA5-Land data. Further information on the dataset can be found in the submitted manuscript.  Data format: comma separated values (csv) Time step: Monthly (mean) Time stamp: yyyy-MM-dd  Period: 11/2012–09/2023 <table> <tbody> <tr> <td> Variable </td> <td> Acronym </td> <td> Units </td> <td> Height above ground </td> <td> Location </td> <td> Geographic location </td> </tr> <tr> <td> Global radiation </td> <td> G </td> <td> W m-2 </td> <td> 27 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Air temperature </td> <td> Tair </td> <td> Degrees Celsius </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Relative humidity </td> <td> RH </td> <td> % </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Air pressure </td> <td> P </td> <td> hPa </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Wind speed </td> <td> u </td> <td> m s-1 </td> <td> 35 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Precipitation </td> <td> R </td> <td> mm </td> <td> 2 m </td> <td> Kaserne </td> <td> 8°32'/47°23' </td> </tr> <tr> <td> Downward longwave radiation </td> <td> LW </td> <td> W m-2 </td> <td> 110 m </td> <td> Hardau II, ERA-5 </td> <td> 8°30'/47°23' </td> </tr> <tr> <td> Soil temperature </td> <td> Tsoil </td> <td> Degrees Celsius </td> <td> -0.15 m </td> <td> Parks </td> <td> - </td> </tr> <tr> <td> Soil water content </td> <td> SWC </td> <td> m3 m-3 </td> <td> -0.15 m </td> <td> Parks </td> <td> - </td> </tr> <tr> <td> Atmospheric CO2 concentration </td> <td> CO2 </td> <td> ppmv </td> <td> 2 m </td> <td> Hardau II, Beromunster, ERA-5 </td> <td> 8°30'/47°23', 8°10'/47°11 </td> </tr> </tbody> </table>   In-situ ecophysiological data In-situ ecophysiology measurements performed on park trees and lawns in the city of Zurich during the ICOS-Cities project. LAI (leaf area index) was measured in dense Platanus sp. tree stands, found only in Bullingerhof and Hardaupark, during sunny conditions using a ceptometer (SS1 SunScan, Delta-T Devices). Sap flow was measured at six trees (Platanus sp., Tilia sp.), at Bullingerhof, Hardaupark and Fritschiwiese, with heat pulse sap flow sensors (3 x 3 cm probes, Implexx Sense), providing continuous measurements at 10-min sampling intervals. Daily aggregated sap flux densities (cm3 cm−2 d−1) were calculated from the 10-min data using the sensor inner thermistors, averaged for the six sampled trees. Soil and grass respiration were measured using a portable CO2 soil efflux system equipped with a 20 cm diameter survey chamber (LI-8200-01S, LI-COR Biosciences) and a CO2/H2O analyser (LI-870, LI-COR Biosciences). The observations originate from a total of 10 soil collars (Bullingerhof, Hardaupark, Fritschiwiese, Heiligfeld) separated to undisturbed grass collars (Reco, μmol CO2 m-2 s-1) and collars where the aboveground grass was clipped (Rsoil, μmol CO2 m-2 s-1). Further information on the dataset can be found in the submitted manuscript. Data format: comma separated values (csv) Time stamp: yyyy-MM-dd Period: 04/2022–09/2023   Land cover map Land cover map of part of Zurich urban area. Datasets used to derive this map: <ul> <li>·        Land Use Cadastre of the Canton of Zurich (https://www.geolion.zh.ch/geodatensatz/show?gdsid=443)</li> <li>·        Urban Atlas (https://doi.org/10.2909/fb4dffa1-6ceb-4cc0-8372-1ed354c285e6)</li> <li>·        Vegetation Height Model (VHM) from the Swiss federal forest inventory (https://opendata.swiss/de/dataset/vegetationshohenmodell-lfi)</li> <li>·        Forest Mixture from the Swiss Federal Forest Inventory (https://opendata.swiss/de/dataset/waldmischungsgrad-lfi)</li> </ul> Further information on the dataset can be found in the submitted manuscript.   Data specifications: CRS: EPSG:32632 - WGS 84 / UTM zone 32N - Projected Spatial Extent: 461972.1, 5246490.4 : 463972.1, 5248490.4 Temporal Extent: 2023 Units: meters Width: 2000 Height: 2000 Bands: 1 Pixel Size: 1,-1 Data type: Float32 - Thirty two bit floating point GDAL Driver Description: GTiff GDAL Driver Metadata: GeoTIFF   Legend: 30           Grass 40           Crops 50           Paved 60           Buildings 70           Deciduous trees 80           Water   CO2 fluxes Hourly mean CO2 fluxes estimated by the models diFUME, JSBACH, SUEWS and VPRM for the trees and lawns of the Zurich urban parks: Bullingerhof, Hardaupark, Fritschiwiese and Heiligfeld. GPP stands for gross primary productivity, Reco for ecosystem respiration and NEE for net ecosystem exchange. All fluxes are in units: μmol CO2 m-2 s-1. The parameter sets used by each model are presented in the Tables below. Further information on the dataset can be found in the submitted manuscript. Parameters used by diFUME model <table> <tbody> <tr> <td> Parameter </td> <td> Value </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> A_max </td> <td> 15 </td> <td> μmol CO2 m-2 s-1 </td> <td> maximum leaf gross photosynthetic rate </td> </tr> <tr> <td> a </td> <td> 0.045 </td> <td> mol CO2 mol-1 PAR </td> <td> quantum yield for CO2 assimilation </td> </tr> <tr> <td> a_1 </td> <td> 25 </td> <td> N/A </td> <td> empirical coefficient in Leuning (1995) model </td> </tr> <tr> <td> b_ </td> <td> 0.65 </td> <td> N/A </td> <td> empirical coefficient in β-factor formula </td> </tr> <tr> <td> b_1 </td> <td> 5 </td> <td> N/A </td> <td> empirical coefficient </td> </tr> <tr> <td> D_o </td> <td> 0.3 </td> <td> kPa </td> <td> empirically determined coefficient for the VPD scalar inside Leuning (1995) model </td> </tr> <tr> <td> D_sc </td> <td> 1 </td> <td> N/A </td> <td> daylight scalar for dark respiration inhibition during day (1: no inhibition) </td> </tr> <tr> <td> E_0 </td> <td> 487.75 </td> <td> K </td> <td> temperature sensitivity parameter for soil respiration </td> </tr> <tr> <td> g_o </td> <td> 0.01 </td> <td> mol CO2 m-2 s-1 </td> <td> residual stomatal conductance for CO2 (g_s when Anet = 0, PAR = 0). </td> </tr> <tr> <td> Q_10 </td> <td> 1.85 </td> <td> N/A </td> <td> temperature sensitivity of leaf respiration </td> </tr> <tr> <td> R_(l,ref) </td> <td> 1.53 </td> <td> μmol CO2 m-2 s-1 </td> <td> reference leaf respiration at Tair = 25 °C </td> </tr> <tr> <td> R_(S,ref) </td> <td> 2.49 </td> <td> μmol CO2 m-2 s-1 </td> <td> reference soil respiration at Tsoil = 10 °C </td> </tr> <tr> <td> T_opt </td> <td> 23 </td> <td> °C </td> <td> optimum air temperature for gross photosynthesis </td> </tr> <tr> <td> T_0 </td> <td> -46 </td> <td> °C </td> <td> low-temperature limit for soil respiration </td> </tr> <tr> <td> T_(ref,S) </td> <td> 10 </td> <td> °C </td> <td> reference soil temperature for soil respiration </td> </tr> <tr> <td> T_(ref,l) </td> <td> 25 </td> <td> °C </td> <td> reference air temperature for leaf respiration </td> </tr> <tr> <td> W </td> <td> 10 </td> <td> °C </td> <td> width of the bell-shape curve at f(T_air )  = 0.5 </td> </tr> <tr> <td> θ_ref </td> <td> 0.4 </td> <td> m3 m-3 </td> <td> saturated soil volumetric water content  </td> </tr> <tr> <td> θ_g </td> <td> 0.1 </td> <td> m3 m-3 </td> <td> minimum soil volumetric water content, limit to stomatal conductance </td> </tr> <tr> <td> θ_0 </td> <td> 0.04 </td> <td> m3 m-3 </td> <td> minimum soil volumetric water content, limit to soil respiration </td> </tr> </tbody> </table>   Parameters used by JSBACH model <table> <tbody> <tr> <td> Parameter </td> <td> Trees </td> <td> Lawn </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> J_max </td> <td> 104.5 </td> <td> 148.6 </td> <td> μmol(CO2) m-2(leaf) s-1 </td> <td> Maximum electron transport rate at 25 °C </td> </tr> <tr> <td> T_alt </td> <td> 4.0–4.5 </td> <td>   </td> <td> °C </td> <td> Alternation temperature </td> </tr> <tr> <td> θ_cap </td> <td> 0.32– 0.39 </td> <td> 0.32– 0.34 </td> <td> m m-1 </td> <td> Volumetric soil field capacity </td> </tr> <tr> <td> θ_pwp </td> <td> 0.13– 0.21 </td> <td> 0.135–0.165 </td> <td> m m-1 </td> <td> Volumetric wilting point </td> </tr> <tr> <td> V_max </td> <td> 55.0 </td> <td> 78.2 </td> <td> μmol(CO2) m-2(leaf) s-1 </td> <td> Maximum carboxylation rate at 25 °C </td> </tr> <tr> <td> z_root </td> <td> 0.5 </td> <td> 0.12 </td> <td> m </td> <td> Root depth </td> </tr> <tr> <td> CC </td> <td> 1.25 </td> <td> 1.25 </td> <td> N/A </td> <td> Relative cost to produce one carbon </td> </tr> <tr> <td> f_faeces </td> <td> 0.3 </td> <td> 0.3 </td> <td> N/A </td> <td> Fraction of carbon from herbivore faeces that goes into the green litter pool </td> </tr> <tr> <td> f_leaf </td> <td> 0.4 </td> <td> 0.4 </td> <td> N/A </td> <td> A fixed fraction of canopy maintenance respiration that makes up the dark respiration </td> </tr> <tr> <td> k </td> <td> 0.1 </td> <td> 0.09 </td> <td> N/A </td> <td> LAI growth rate during growth phase </td> </tr> <tr> <td> LAI_max </td> <td> 3.6–4.1 </td> <td> 3.0 </td> <td> m2 m-2 </td> <td> Maximum leaf area index </td> </tr> <tr> <td> p </td> <td> veg: 0.004 rest: 0.1 </td> <td> growth: 0.1 dry: 0.015 </td> <td> N/A </td> <td> LAI shedding rate (trees: vegetative and rest phase; grass: growth and dry season) </td> </tr> <tr> <td> r_d </td> <td> 0.605 </td> <td> 0.8602 </td> <td> μmol(CO2) m-2(leaf) s-1 </td> <td> Dark respiration at 25 °C, fraction of Vmax </td> </tr> </tbody> </table>   Parameters used by SUEWS model <table> <tbody> <tr> <td> Parameter </td> <td> Trees </td> <td> Lawn </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> f_i </td> <td> 0.21 </td> <td> 0.18 </td> <td> N/A </td> <td> Fraction of each vegetation type i </td> </tr> <tr> <td> F_(pho,max,i) </td> <td> 8.3 </td> <td> 8.92 </td> <td> μmol m-2 s-1 </td> <td> Maximum potential photosynthesis </td> </tr> <tr> <td> LAI_(max,i) </td> <td> 4.8 </td> <td> 3 </td> <td> m2 m-2 </td> <td> Full leaf-on summertime value </td> </tr> <tr> <td> LAI_(min,i) </td> <td> 0.66 </td> <td> 1.6 </td> <td> m2 m-2 </td> <td> Leaf-off wintertime value </td> </tr> <tr> <td> T_L </td> <td> -10 </td> <td> -10 </td> <td> °C </td> <td> Lower air temperature limit </td> </tr> <tr> <td> T_H </td> <td> 55 </td> <td> 55 </td> <td> °C </td> <td> Upper air temperature limit </td> </tr> <tr> <td> G_5 </td> <td> 30 </td> <td> 30 </td> <td> °C </td> <td> Parameter related to temperature dependence </td> </tr> <tr> <td> G_3 </td> <td> 0.66 </td> <td> 0.538 </td> <td> N/A </td> <td> Parameter related to VPD dependence </td> </tr> <tr> <td> G_4 </td> <td> 0.89 </td> <td> 0.87 </td> <td> N/A </td> <td> Parameter related to VPD dependence </td> </tr> <tr> <td> G_6 </td> <td> 0.36 </td> <td> 0.55 </td> <td> mm-1 </td> <td> Parameter related to soil moisture dependence </td> </tr> <tr> <td> G_2 </td> <td> 477 </td> <td> 263.5 </td> <td> W m-2 </td> <td> Parameter related to  dependence </td> </tr> <tr> <td> Δθ_WP </td> <td> 132.5 </td> <td> 143 </td> <td> mm </td> <td> Wilting point deficit </td> </tr> <tr> <td> K_(↓max) </td> <td> 1200 </td> <td> 1200 </td> <td> W m-2 </td> <td> Maximum incoming shortwave radiation </td> </tr> <tr> <td> a_i </td> <td> 0.78 </td> <td> 1.7 </td> <td> N/A </td> <td> Empirical soil and vegetation respiration coefficient </td> </tr> <tr> <td> b_i </td> <td> 0.08 </td> <td> 0.06 </td> <td> N/A </td> <td> Empirical soil and vegetation respiration coefficient </td> </tr> <tr> <td> ω_(1,GDD,i) </td> <td> 0.04 </td> <td> 0.04 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> ω_(2,GDD,i) </td> <td> 0.0005 </td> <td> 0.0005 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> ω_(1,SDD,i) </td> <td> -1.5 </td> <td> -1.5 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> ω_(1,SDD,i) </td> <td> 0.0025 </td> <td> 0.0025 </td> <td> N/A </td> <td> Parameter for LAI calculation </td> </tr> <tr> <td> GDD </td> <td> 300 </td> <td> 300 </td> <td> days </td> <td> The growing degree days (GDD) needed for full capacity of the leaf area index </td> </tr> <tr> <td> SDD </td> <td> -300 </td> <td> -300 </td> <td> days </td> <td> The senescence degree days (SDD) needed to initiate leaf off </td> </tr> <tr> <td> T_(base,GDD) </td> <td> 5 </td> <td> 5 </td> <td> °C </td> <td> Base Temperature for initiating growing degree days (GDD) for leaf growth </td> </tr> <tr> <td> T_(base,SDD) </td> <td> 10 </td> <td> 10 </td> <td> °C </td> <td> Base temperature for initiating senescence degree days (SDD) for leaf off </td> </tr> </tbody> </table>   Parameters used by VPRM model <table> <tbody> <tr> <td> Parameter </td> <td> Trees </td> <td> Lawn </td> <td> Units </td> <td> Description </td> </tr> <tr> <td> λ </td> <td> <div> -0.16 </div> </td> <td> <div> -0.13 </div> </td> <td> <div> μmol CO2 m-2 s-1 </div> </td> <td> <div> light use efficiency </div> </td> </tr> <tr> <td> PAR_0 </td> <td> <div> 356.99 </div> </td> <td> <div> 545.61 </div> </td> <td> <div> μmol m-2 s-1 </div> </td> <td> <div> half-saturation value </div> </td> </tr> <tr> <td> α </td> <td> <div> 0.22 </div> </td> <td> <div> 0.40 </div> </td> <td> <div> μmol CO2 m-2 s-1 /0C </div> </td> <td> <div> empirical coefficient </div> </td> </tr> <tr> <td> β </td> <td> <div> 1.09 </div> </td> <td> <div> 0.42 </div> </td> <td> <div> μmol CO2 m-2 s-1 </div> </td> <td> <div> empirical coefficient </div> </td> </tr> <tr> <td> T_max </td> <td> <div> 40 </div> </td> <td> <div> 40 </div> </td> <td> <div> °C </div> </td> <td> <div> maximum temperature for photosynthesis </div> </td> </tr> <tr> <td> T_min </td> <td> <div> 0 </div> </td> <td> <div> 2 </div> </td> <td> <div> °C </div> </td> <td> <div> minimum temperature for photosynthesis </div> </td> </tr> <tr> <td> T_opt </td> <td> <div> 20 </div> </td> <td> <div> 18 </div> </td> <td> <div> °C </div> </td> <td> <div> optimal temperature for photosynthesis </div> </td> </tr> <tr> <td> T_low </td> <td> <div> 0 </div> </td> <td> <div> 0 </div> </td> <td> <div> °C </div> </td> <td> <div> to account for the persistence of soil respiration in winter </div> </td> </tr> </tbody> </table>   Data format: comma separated values (csv) Time step: 60 min (aggregated) Time stamp: yyyy-MM-dd HH:mm, UTC, end of aggregated period Period: 01/2022–09/2023

openmit-licenseMar 2022View details →

CLDF dataset derived from a preprint of 'Új magyar etimológiai szótár' [New Hungarian Etymological Dictionary] by Károly Gerstner (ed.)

Cite the source of the dataset as: <blockquote> Gerstner, Károly (ed.) (2011-2023). Új magyar Etimológiai Szótár. Hungarian Academy of Sciences, Budapest. http://uesz.nytud.hu/. </blockquote>

opencc-by-4.0Sep 2023View details →

Exploring the Impact of Neuroscience Preprints: A Citation Analysis

1. Neuroscience_Records_Contain_Reference_to_Preprints.Scopus.V3.xlsx This Excel file contains the titles, DOIs, references, and EIDs of those Neuroscience publications (journal articles, books/book chapters, conference papers, notes, etc.) from 2004 to 2022 that have at least one reference to a preprint. For example, if a Neuroscience journal article has 40 references and one of these references is a preprint, then it's included in this Excel file. These records are retrieved from Scopus through the following query: REFSRCTITLE ( "OSF Preprints" OR "open science foundation Preprints" OR *africarxiv* OR *agrixiv* OR *arabixiv* OR *arxiv* OR *biohackrxiv* OR *biorxiv* OR *bodoarxiv* OR *cogprints* OR *eartharxiv* OR *ecoevorxiv* OR *ecsarxiv* OR *edarxiv* OR *engrxiv* OR *frenxiv* OR "INA-Rxiv" OR *indiarxiv* OR *lawarxiv* OR "LIS Scholarship Archive" OR *marxiv* OR *mediarxiv* OR *metaarxiv* OR mindrxiv OR *nutrixiv* OR paleorxiv OR "Preprints.org" OR psyarxiv OR *repec* OR *socarxiv* OR *sportrxiv* OR "Thesis Commons" OR "CoP preprint" OR "FocUS Archive preprint" OR "PeerJ preprint" OR "Law Archive preprint" OR *medrxiv* ) AND SUBJAREA ( neur ) AND PUBYEAR < 2023   2. ReferencesToPreprints.V3.txt References of the publications are split through a Python code (SplitReferences.py) and organized into separate lines in a text file. For example, if a publication has 40 references, all of these 40 references are split into 40 separate lines. After splitting references, those lines containing one of these words/terms ("OSF Preprints" OR "open science foundation preprints" OR africarxiv OR agrixiv OR arabixiv OR arxiv OR biohackrxiv OR biorxiv OR bodoarxiv OR cogprints OR eartharxiv OR ecoevorxiv OR ecsarxiv OR edarxiv OR engrxiv OR frenxiv OR "INA-Rxiv" OR indiarxiv OR lawarxiv OR "LIS Scholarship Archive" OR marxiv OR mediarxiv OR metaarxiv OR mindrxiv OR nutrixiv OR paleorxiv OR "Preprints.org" OR psyarxiv OR repec OR socarxiv OR sportrxiv OR "Thesis Commons" OR "CoP preprint" OR "FocUS Archive preprint" OR "PeerJ preprint" OR "Law Archive preprint" OR medrxiv) are selected (through RetrieveLinesContainingSpeceficString.py) and organized into this text file (ReferencesToPreprints.V3.txt). Each reference contains an EID (separated by ";") in order to specify which publication contains this specific reference. After this step, through a Python code (AddPreprintServerToEndOfLines.py) the name of a certain preprint was added to the end of each line. For example, if a line (or a reference) contains "biorxiv", the word "biorxiv" will be added to the end of this line after the "@" sign.

opencc-by-4.0Dec 2019View details →

Pre- and post-publication citations to published arXiv preprints

This dataset contains citations to published preprints, both before they are published and after they are published. Details of the data are provided in the <code>README.md</code>.

opencc-by-4.0Mar 2019View details →

Preprints: what do scientists think?

Episode Summary: In this episode we are discussing preprints and how they fit into Open Access publishing. Our interview guests will be Dr Manvendra Singh, a post-doc, and Elias Lowenstein, a PhD researcher, both from the Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC). We will cover what pre-prints are, what possible benefits they bring, and how they can help promote open access more widely. Resources and Links: <ul> <li><a href="https://docs.google.com/spreadsheets/d/17RgfuQcGJHKSsSJwZZn0oiXAnimZu2sZsWp8Z6ZaYYo/edit#gid=0">Preprint Archive Database</a></li> <li><a href="https://en.wikipedia.org/wiki/List_of_academic_journals_by_preprint_policy">Preprint-Journal Compatibility List</a></li> <li>Scientists: <ul> <li><a href="https://www.researchgate.net/profile/Manvendra_Singh2">Manvendra Singh</a></li> <li><a href="https://www.researchgate.net/profile/Elijah_Lowenstein">Elias Lowenstein</a></li> </ul> </li> </ul> Episode Quotes: “With preprints it is a copyright you have, to the world: you gave this information first”

opencc-by-4.0Feb 2022View details →

Reliability of citations of medRxiv preprints in articles published on COVID-19 in the world leading medical journals

Articles published on COVID in 2020 in the BMJ, The Lancet, the JAMA and the NEJM were manually screened to identify all articles citing at least one preprint from medRxiv. We searched PubMed, Google and Google Scholar to assess if the preprint had been published in a peer-reviewed journal, and when. Published articles were screened to assess if the title, data or conclusions were identical to the preprint version.

opencc-by-4.0Apr 2022View details →

Supplementary Material of "How Does Author Affiliation Affect Preprint Citation Count? Analyzing Citation Bias at the Institution and Country Level"

The source code and dataset for the following paper: Nishioka, C., Färber, M., and Saier, T. How Does Author Affiliation Affect Preprint Citation Count? Analyzing Citation Bias at the Institution and Country Level. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2022 (JCDL '22), 2022.

opencc-by-4.0Jul 2022View details →

GENeSYS-MOD Transport Sensitivities: Data and model code for Hainsch (preprint): Identifying policy areas for the transition of the transportation sector

This dataset contains all GENeSYS-MOD input data for Hainsch (preprint): Identifying policy areas for the transition of the transportation sector. doi: 10.5281/zenodo.6919452. With the input data files and the GAMS files, the model results presented in the preprint can be replicated. Furthermore, the output folder contains the result files for the base case and all sensitivities as well as the Tableau files which were used to generate the result figures.

opencc-by-4.0May 2022View details →

PreprintMatch: a tool for preprint publication detection applied to analyze global inequities in scientific publishing

Dataset underlying the paper "PreprintMatch: a tool for preprint publication detection applied to analyze global inequities in scientific publishing." preprint-paper-matches.csv lists all matches found by our algorithm between bioRxiv/medRxiv and PubMed, and preprint_affiliations.csv lists all extracted affiliations from bioRxiv/medRxiv. The Rxivist data dump (https://zenodo.org/record/4738007) was used for all preprint data, and the scrips to download PubMed data are available on our GitHub repository, https://github.com/PeterEckmann1/preprint-match. The full database dump, with all data used in the study, is available on Google Drive at https://drive.google.com/file/d/1ZoafhYUP-DO4Hd_4A_v7mbQLjN3JPzJv/view?usp=sharing. The PostgreSQL database can be restored using the pg_restore command.