Find research datasets worth reusing

Figure 2 – C in Description of a new species from Africa in genus Coccothera Meyrick, 1914, with additional taxonomic and faunistic information on two other Coccothera species (Lepidoptera: Tortricidae: Grapholitini)

Figure 2 – C. spissana (Zell.) ♂ P. 3524 KL, 12 mm. Spain. Fuerteventura.

Figure 5 – C in Description of a new species from Africa in genus Coccothera Meyrick, 1914, with additional taxonomic and faunistic information on two other Coccothera species (Lepidoptera: Tortricidae: Grapholitini)

Figure 5 – C. spissana (Zell.) ♂ P. 3524 KL, Spain, Fuerteventura.

Figure 7 – C in Description of a new species from Africa in genus Coccothera Meyrick, 1914, with additional taxonomic and faunistic information on two other Coccothera species (Lepidoptera: Tortricidae: Grapholitini)

Figure 7 – C. nicomacha (Meyr.) ♂ P. 3536 KL, 11 mm. Tanzania, Iringa.

Figure 4 – C in Description of a new species from Africa in genus Coccothera Meyrick, 1914, with additional taxonomic and faunistic information on two other Coccothera species (Lepidoptera: Tortricidae: Grapholitini)

Figure 4 – C. spissana (Zell.) ♂ P. 3533 KL, Aethiopia, Adis Abeba.

Figure 6 – C in Description of a new species from Africa in genus Coccothera Meyrick, 1914, with additional taxonomic and faunistic information on two other Coccothera species (Lepidoptera: Tortricidae: Grapholitini)

Figure 6 – C. spissana (Zell.) ♀ P. 3528 KL, Cameroun, Faro NP.

Figure 1 – C in Description of a new species from Africa in genus Coccothera Meyrick, 1914, with additional taxonomic and faunistic information on two other Coccothera species (Lepidoptera: Tortricidae: Grapholitini)

Figure 1 – C. spissana (Zell.) ♂ 9 mm. Cameroun. Faro NP.

opencc-by-4.0Jun 2024View details →

Supporting Information: Sedimentary events at the Jurassic–Cretaceous transition in the Slovenian Basin (Alpine Atlantic): further evidences for palaeoenvironmental record in pelagic sediments

Dataset containing raw elemental geochemistry data (ICP-MS, pXRF and GRS measurements), stable carbon and oxygen isotopes, as well as rock magnetic data from the Tithonian-Berriasian of the Petrovo Brdo section (Slovenia). Supplementary information for the research entitled "The Jurassic-Cretaceous transition in the Slovenian Basin (Alpine Atlantic): further evidence for palaeoenvironmental record in pelagic sediments" published in Geochemistry, Geophysics, Geosystems.

opencc-by-4.0Oct 2024View details →

Dataset: Inertial effects in discrete sampling information engines

This record includes the data used for article "Inertial effects in discrete sampling information engines", published in Europhysics Letter (doi: 10.1209/0295-5075/ad8bf0). The preprint is included is this record as "Inertial information engines.pdf" <h3>Long 𝜏 limit (Figs. 4 and 5)</h3> The matlab script LongTauAnalyse.m convert raw time acquisitions of the cantilever deflection for various demon protocols from the data files in zip folders LongTauDataPosh.zip and LongTauDataNegh.zip to create files LongTauDataPosh.mat and LongTauDataNegh.mat. In these two files, for each protocol (set of parameters L and h), 12400 independent readings of the cantilever position, of the initial and final well position, and of the work performed by the demon are recorded. They are used to plot the work distribution for each parameter set (Fig. 4), and the mean value of the work (Fig. 5). The matlab script readbindata.m is a dependency of the main script LongTauAnalyse.m. <h3>Data files</h3> The archive Data.zip contains the data used to plot the figures. These are pre analysed data. From raw data, all the switching events are detected and for each events different quantities are computed and stored using Python in a dictionnary of numpy arrays. Each file can be loaded as a dictionnary containing several fields:  w : work exchanged during a switch of the potential, computed using Stratonovich convention w_el : work computed as a difference of potential energy q : heat exchanged trap_position : value of L during the experiment hysteresis : value of h during the experiment duration :  time to the previous switch event tau_wait : value of 𝜏  <h3>Python files</h3> The archive python.zip contains all the python code used to plot the figures from the pre analysed data. figures_large_tau : figure 4 and 5 figures_variation_tau : figure 6 figures_pdf_tau_inter : figure 7 figures_pdf_short_tau : figure 8 figures_comparaison_maps : figure 9 figures_info_large_tau : figure 11 plot_style_full is the matplotlib configuration file and traitement_stat.py is a dependency of the other python scripts.

Data associated with 'Closing the stellar labels gap: Stellar label independent evidence for [α/M] information in Gaia BP/RP spectra'

We present a stellar label independent model for Gaia BP/RP (XP) spectra, which does not rely on stellar labels to simulate XP spectra. Here, we provide all of the data required to reproduce <a href="https://github.com/AlexLaroche7/xp_vae">our results</a>. All of the data files should be placed in the data folder. Below we brieflty summarize the contents of each: APOGEE/GAIA XP DATA: <ul> <li>xp_apogee_cat.h5: Contains all Gaia XP data and APOGEE stellar labels used in this work.</li> <li>xp_corrs.npz: Contains Gaia XP covariance matrices for a subset of the test data to compute reconstruction errors.</li> </ul> INTERMEDIATE DATA PRODUCTS: (All files below can be reproduced with our codebase. We simply include them to speed up runtime for reproducing our results.) <ul> <li>lb23_xp_est_labels.npy: <a href="https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.1494L/abstract">Leung & Bovy (2023)</a> XP coeffficient spectra reconstructions (stellar label dependent implementaton)</li> <li>lb23_xp_est_no_labels.npy: Leung & Bovy (2023) XP coeffficient spectra reconstructions (stellar label independent implementaton)</li> <li>xp_wavelength_space.npy: Observed Gaia XP spectra in wavelength space</li> <li>err_wavelength_space.npy: Observed Gaia XP uncertainties in wavelength space</li> <li>apogee_norm.npz: Means and standard deviations which are used to pre-process Gaia XP data before inputting into our model</li> <li>zhang_stellar_params_xmatch.npz: <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.524.1855Z/abstract">Zhang, Green, Rix (2023)</a> stellar parameter estimates for subset of XP spectra (to compute reconstructions)</li> <li>vae_wavelength_space.npy: Our model XP coefficient reconstructions in wavelength space</li> </ul> If you have any questions please reach out via email: alex.laroche@mail.utoronto.ca

opencc-by-4.0Oct 2024View details →

OctocoralTraits: a global database of trait information for octocoral species

The OctocoralTraits_v2_2 ZIP file contains structured data and code to create all figures from the manuscript: “The Octocoral Trait Database: a global database of trait information for octocoral species”. The folder also contains the code used to validate the data descriptor. Specifically, the folder contains the following files: ·       Code.R – This is the annotated code used to create all figures in R.  (v R studio; 2023.06.0+421) ·       OctocoralTraits_v2_2.csv – This is the first data realase of the octocoral trait database, used to create all figures of the manuscript. ·       sp_id.csv – This is a file containing the list of accepted octocoral species contained in the database ·       iqtree.tre – This is a tree object used to create a family-resolved tree of octocorals. It has been downloaded from McFadden et al. 2022. Revisionary systematics of Octocorallia (Cnidaria: Anthozoa) guided by phylogenomics ·       MEOW folder – A folder containing regional information from Spalding et al. 2007. Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas ·       Provinces_coord.csv: A file containing the coordinates of marine provinces, also derived from Spalding et al. 2007 ·       Table_ids folder - A folder containing tables with information of the corresponding ids that appear in the file database. ·      Validation folder: A folder containing a Technical_validation.R file that was used to flag outliers and extreme outliers, duplicated rows and potential structural errors in the data (e.g., a given observation_id linked to more than one resource, species or location).

Supplemental Information on the Weighted Gene Co-expression Network Analysis performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"

Supplemental Information on the Weighted Gene Co-expression Network Analysis (WGNCA) performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide" The results are sorted by the three species analysed: the two algae Zygnema circumcarinatum SAG 698-1b (Zygnema) and Mesotaenium endlicherianum SAG 12.97 (Mesotaenium); and the bryophyte Physcomitrium patens strain Gransden 2004 (Physcomitrium).

Supporting Information for "The Pitch Angle Measurement Method based on Detector Counts Distribution. -I. Basic conception"

Data Description We have uploaded 2 data files. These are: <ol> <li>The CPD data of tn240719_200130. The data include: a) the relative time to reference time (UT 2024-07-19T20:01:30), b) the deposited energy (keV), c) the detector ID (i.e. CPD1 to CPD2). </li> <li>The GRD data of tn240719_200130. The data include: a) the relative time to reference time (UT 2024-07-19T20:01:30), b) the deposited energy (keV), c) the detector ID (i.e. GRD1 to GRD12). </li> </ol>

data for article "Theory informed, experiment based, constraint on the rate of autoxidation chemistry – An analytical approach"

The files contain the data from simulations shown in plots for main manuscript figures and SI figures. Further, a preliminary autoCONSTRAINT verion to reproduce the results shown is attached and the PyCHAM model used to create CIMS-like inputs to autoCONSTRAINT referenced (model inputs are attached). For any questions on the data contact: office@pi-numerics.com Detailed information on the file content is provided in file "SI_data_description"

Supplementary information for the report "Fracture Mapping on the Reykjanes Peninsula"

Supplementary material for the University of Iceland's Institute of Earth Sciences (Jarðvísindastofnun Háskólans) research report RH-06-24: "Fracture Mapping on the Reykjanes Peninsula". These are fractures mapped with TerraSAR-X data following the methods of Ducrocq et al. (2024), published in Bulletin of Volcanology (<a href="https://doi.org/10.1007/s00445-023-01699-0" target="_blank" rel="noopener">https://doi.org/10.1007/s00445-023-01699-0</a>), for September 2021 to July 2024.

Tree census data associated to 'Large-scale informative priors to better predict the local occurrence rate of a rare tree-related microhabitat'

This repository contains two excel spreadsheets providing data about the trees studied in Cottais et al. study (doi :  10.1101/2024.11.28.625900): <ul> <li>df_TreM.xlsx has 1462 lines (headers not included); each line corresponds to one oak tree sampled in 2021 survey of Cottais et al. study. Fields are: <ul> <li>Plot_Id : the id of the sampling plot where the tree is located</li> <li>Tree_Id : a unique id of the tree used in the BloBiForM project</li> <li>Species_Latin_Name : the latin name of the tree species ('Quercus_sp' for all trees given than only oaks are reported here)</li> <li>DBH_cm : the diameter at breast height of the tree in cm</li> <li>TreM : whether the tree harbours a basal rot hole (1) or not (0)</li> <li>Life_Stage : whether the tree is a living tree or a snag</li> <li>Last_Logging_Year : the year of the last logging event in the stand where the tree is located</li> <li>conv : whether the last logging event occured less that 120 years before 2024 (conv=0; the stand is now classic high forest) or is more ancient (conv=1, the stand is being converted to high forest through sprout thinning)</li> </ul> </li> <li>DMH_2022.xlsx has 1190 lines (headers not included); each line corresponds to one tree sampled in 2022 survey of Cottais et al. study. Fields are: <ul> <li>Plot_Id: the id of the sampling plot where the tree is located</li> <li>Species_Latin_Name: the latin name of the tree species, obtained as a translation from french using the dictionnary in page 2; oaks are not the only species reported, but only oaks are used in Cottais et al. study;</li> <li>Species_French_Name : the french name of the tree species;</li> <li>DBH_cm: the diameter at breast height of the tree in cm</li> <li>TreM: whether the tree harbours a basal rot hole (1) or not (0)</li> <li>Tree_Id: when the tree belongs to the cohort of the BloBiForM project, the tree id is reported; left empty otherwise</li> <li>Nb_brin: number of stems on the tree stump;</li> <li>Commentaire: any comments during fieldwork</li> </ul> </li> </ul>

Location and information files

These are the files containing other information used for this thesis from which the various maps showing locations of quarries, samples, and artefacts were created. Each is in .csv format. The files are: <ul> <li>Artefact data file.csv</li> <li>Quarries.csv</li> <li>Trace element map sample locations.csv</li> </ul> Artefact data file.csv: On line databases for 10 Swedish museums were searched for their steatite artefacts, and the information was compiled to this file. For those artefacts where the location was given only in the form of a place name I consulted online maps, including the Swedish National Map service, https://minkarta.lantmateriet.se/ and https://www.google.com/maps to determine a latitude and longitude for the address/location described. If the exact location could not be determined, or was not listed, but the database listed the parish in which it was found, then the location of the parish was determined from https://socknar.se/, and the location of the parish church was recorded. If there was not even a parish listed, then the approximate centre of the province was recorded. These three levels of accuracy for the location are recorded under the variable name “accuracy”. Quarries.csv: The data for these steatite quarry, outcrop, and prospect locations was primarily obtained from the Norwegian Geological Survey (https://geo.ngu.no/kart/mineralressurser_mobil/) for the quarries located in Norway, and the Swedish Geological Survey (https://apps.sgu.se/kartvisare/kartvisare-malm-mineral.html) for those located in Sweden. This information was supplemented with some data from my own field work when collecting samples, and from the Swedish national archaeological database (https://app.raa.se/open/fornsok/).  Trace element map sample locations.csv: This file contains information specific to the samples analysed with LA-ICP-MS for this thesis, with location and geologic information coming from the quarry locations file (see above), and the information pertaining to the sample and trace-element composition map number, which epoxy mount it is in, and the date of analysis is recorded from working notes. Note: It was discovered that version 1 of this data set didn't have all three files, so version 2 was uploaded.

opencc-zeroJan 2023View details →

Canadian publications in Library and Information Science / Publications canadiennes en bibliothéconomie et sciences de l'information

Overview of Dataset  This dataset was developed through a collaboration between Dalhousie University and the University of Montréal. This project aims to help break down the silos in which the two primary target audiences- information science researchers and academic librarians- conduct their research. The Canadian Publications in Library and Information Science dataset makes visible the work that librarians do and allows other Canadian researchers to discover the research of their colleagues.  The dataset contains 1,326 distinct authors, 850 of which were classified as practitioners and 476 as academics. It has a total of 13,775 records out of which 8,230 are authored by at least one academic and 5,740 are authored by at least one practitioner. File descriptions   Table 1. Canadian LIS authors table (authors) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> author_id </td> <td> Unique identifier for the publication in the LIS database </td> </tr> <tr> <td> first_name </td> <td> First name of author </td> </tr> <tr> <td> last_name </td> <td> Last name of author </td> </tr> <tr> <td> full_name </td> <td> Full name of author </td> </tr> <tr> <td> status </td> <td> Academic (Ph.D. student, a postdoctoral fellow, or a professor (assistant, associate, full, emeritus) in an organizational unit offering an ALA accredited degree) or practitioner (librarian position in a Canadian university) </td> </tr> </tbody> </table>   Table 2. Works table (publications) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> pub_id </td> <td> Unique identifier for the publication in the LIS database </td> </tr> <tr> <td> doi </td> <td> Digital object identifiers </td> </tr> <tr> <td> openalex_work_id </td> <td> Identifier of the work in the OpenAlex database (URL format) </td> </tr> <tr> <td> isbn </td> <td> International standard book number (ISBN). </td> </tr> <tr> <td> doc_type </td> <td> Document type. Can take one of the following values: article; review; conference paper, book; edited book; book chapter. </td> </tr> <tr> <td> publication_year </td> <td> Year of publication </td> </tr> <tr> <td> title </td> <td> Title of the document </td> </tr> <tr> <td> source_name </td> <td> Title of the source (journal, conference, or book title for book chapters) </td> </tr> <tr> <td> author_list_full </td> <td> Full text listing of author names </td> </tr> <tr> <td> volume </td> <td> Volume number </td> </tr> <tr> <td> issue </td> <td> Issue number </td> </tr> <tr> <td> pages </td> <td> First and last pages separated by a hyphen. </td> </tr> <tr> <td> bk_edition </td> <td> Book edition </td> </tr> <tr> <td> bk_editor </td> <td> Name of book editor (for book chapters) </td> </tr> <tr> <td> publisher </td> <td> Publisher of the book/journal </td> </tr> <tr> <td> source_id </td> <td> Foreign key to the sources table </td> </tr> <tr> <td> url </td> <td> URL for the publication </td> </tr> </tbody> </table>   Table 3. Author publications table (authors_publications) <table> <tbody> <tr> <td> <a name="_Hlk155194541"></a>Field </td> <td> Description </td> </tr> <tr> <td> author_id </td> <td> Unique identifier for the author in the authors table </td> </tr> <tr> <td> pub_id </td> <td> Unique identifier for the work in the publications table </td> </tr> <tr> <td> author_position </td> <td> Position on the byline. </td> </tr> <tr> <td> role </td> <td> Role of the author on the work (author/editor) </td> </tr> </tbody> </table> Table 4. Author IDs table (authors_ids) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> author_id </td> <td> Unique identifier for the author in the authors table </td> </tr> <tr> <td> source </td> <td> Source for the identifier (e.g., OpenAlex, Scopus, Google Scholar, ORCID) </td> </tr> <tr> <td> identifier </td> <td> Identifier for the author in the source database </td> </tr> </tbody> </table>   Table 5. Publication source table (sources) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> source_id </td> <td> Unique identifier for the source </td> </tr> <tr> <td> source_name </td> <td> Name of the source </td> </tr> <tr> <td> publisher </td> <td> Publisher name for the source </td> </tr> <tr> <td> issn </td> <td> ISSN for the source </td> </tr> <tr> <td> source_type </td> <td> OpenAlex source type (e.g., journal, conference) </td> </tr> </tbody> </table>   Table 6. Institutions table (institutions) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> institution_id </td> <td> Unique identifier for the institution </td> </tr> <tr> <td> institution_name </td> <td> Name of the Canadian academic institution </td> </tr> <tr> <td> city </td> <td> Name of the city in which the institution is primarily located </td> </tr> <tr> <td> province </td> <td> Two-letter code of the province in which the institution is located </td> </tr> </tbody> </table>   Table 7. Institution IDs table (institutions_ids) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> institution_id </td> <td> Unique identifier for the institution in the institutions table </td> </tr> <tr> <td> id_source </td> <td> Source database for the identifier (e.g., OpenAlex) </td> </tr> <tr> <td> identifier </td> <td> Identifier linked to the institution in the source database </td> </tr> </tbody> </table>   Table 8. Authorship institutional affiliation table (authors_publications_institutions) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> author_id </td> <td> Author component of the authorship information in the authors_publications table </td> </tr> <tr> <td> pub_id </td> <td> Publication component of the authorship information in the authors_publications table </td> </tr> <tr> <td> institution_id </td> <td> Unique identifier for the affiliated institution in the institutions table </td> </tr> </tbody> </table>   Table 9. Citations table (citations) <table> <tbody> <tr> <td> Field </td> <td> Description </td> </tr> <tr> <td> citing_pub_id </td> <td> Unique identifier for the citing work in the publications table </td> </tr> <tr> <td> cited_pub_id </td> <td> Unique identifier for the cited work in the publications table </td> </tr> </tbody> </table>   To submit updates For those interested in submitting updates to this dataset, you may send them by email to Philippe Mongeon (PMongeon@dal.ca). Please specify whether you want to modify, add, or delete existing data entries. Files in any format (e.g., XLS, BIB, Word, or a list of DOIs) are accepted. Data paper Find the corresponding data paper that describes the objectives of this dataset and the steps of its creation here: <a href="https://arxiv.org/abs/6053305">https://arxiv.org/abs/6053305</a>. How to cite this dataset Sauvé, J.-S., Hare, M., Krause, G., Poitras, C., Riddle, P., & Mongeon, P. (2024). Canadian publications in Library and Information Science / Publications canadiennes en bibliothéconomie et sciences de l'information [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14302591" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14302591</a>

opencc-zeroJan 2024View details →

Ethanol consumption for an individual known as "hexylena": an informal look

<h2>Background</h2> This dataset comprises the self-reported ethanol consumption for hexylena starting in 2023. I generally believe more individuals should collect data and publish on it, as that makes the world more colourful and interesting, they'll potentially be of interest to future researchers or historians. I was inspired by a video on <a href="https://en.m.wikipedia.org/wiki/John_Trinkaus">John Trinkaus</a> who collected a number of informal datasets (<a href="https://pubmed.ncbi.nlm.nih.gov/18232433/">children's emotions upon interacting with a mall santa</a>, <a href="https://pubmed.ncbi.nlm.nih.gov/15362428/">honesty in paying for votive candles</a>, <a href="https://journals.sagepub.com/doi/abs/10.2466/pms.1991.73.2.613">shoe color preference</a>). All fascinating small studies! I wish I could stuff my CV with such papers. <h2>Data Collection</h2> Data was collected with consent of the subject, via a google form which they used to enter the data. In 2023 this consisted of only volume and ABV, in 2024 this was expanded to include a beverage type, and a reason the individual chose to consume a beverage at that moment. Both of these new fields have been excluded from final publication due to privacy concerns. The 'reason' field has been manually coded into a number of categories, and that coding as been supplied instead. <h2>Source of Error</h2> I estimate that this is an approximately 98% complete dataset, I was very fastidious but nevertheless there is room for collection error due to a number of potential factors: mislabelling, lack of labelling for low-percentage consumption (e.g. an overly ripe banana), or a handful of cases where I was given a glass with unclear measurements. However, as is clear from the dataset, I was rarely so impaired that data was ever lost for that reason. <h2>Changelog</h2> <table> <tbody> <tr> <td>Version</td> <td>Date</td> <td>Changes</td> </tr> <tr> <td>2.1</td> <td>2025-01-02</td> <td>Improved data consistency across years, fixed missing values</td> </tr> <tr> <td>2</td> <td>2025-01-02</td> <td>2024 data release, MeSH terms added, changed license to CC0, improved description, included relevant references.</td> </tr> <tr> <td>1</td> <td>2024-01-05</td> <td>Initial publication</td> </tr> </tbody> </table>

opencc-by-4.0Dec 2023View details →

Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 2001-2023

Usage Notes: This is the updated LCSPP dataset (v3.2), generated using the LCREF-AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals. Key updates in version 3.2 include: <ul> <li>Improved Calibration: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li>Quality Flags: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li>Extension to include observations from the year of 2023.</li> </ul> Other LCSPP repositories can be accessed via the following links: <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test.  In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links: <ul> <li>LCREF-AVHRR v3.2 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that LCSPP is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield. All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1st day to the 15th day of a month, and the second file “b” representative of the 16th day to the last day of a month. Abstract: Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.