Find research datasets worth reusing

Data from Environmental Outcomes of the U.S. Renewable Fuel Standard

Data and figures associated with the paper "Environmental Outcomes of the U.S. Renewable Fuel Standard" by Lark et al.

Non-embeddability of standard contexts of lattices of integer partitions

We consider lattices of integer partitions ordered by dominance, and their standard contexts as studied in formal concept analysis. The present dataset provides evidence that the standard context of the partition lattice for $n = 9$ cannot be embedded into the one for $n = 10$. Further context and a detailed description of the files in the dataset can be obtained from the file context_embeddings.pdf (the code source for this file is given in context_embeddings.tex).

Symmetric embeddings between standard contexts of lattices of integer partitions

We consider lattices of integer partitions ordered by dominance, and their standard contexts as studied in formal concept analysis. The present dataset contains all 29 symmetric context embeddings of the standard context of the lattice of partitions of the integer 8 into the standard context of the lattice of 10. Further information and a detailed description of the files in the dataset can be obtained from the file context_embeddings.pdf (the code source for this file is given in context_embeddings.tex).

opencc-by-4.0Jan 2022View details →

Sampled ΔH/Δλ and ΔH data from ABFE calculations (using standard atomic masses) of 10 ligands bound to Cyclophilin D

Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process" Included are the ABFE raw free energy samples for multiple replicates (labelled by `run` number) of 10 ligands to bound Cyclophilin D. These ligands are originally detailed by Grädler et al. (https://doi.org/10.1016/j.bmcl.2019.126717). Unlike other datasets in this work, which employed hydrogen mass repartitioning, the ligands here were calculated using standard atomic masses. All samples are provided as a set of `.xvg` files as generated by GROMACS 2021 (https://doi.org/10.5281/zenodo.5849961). The `.xvg` files are labelled as dhdl.N.xvg where N represents the λ state the free energy values were sampled from. The `.xvg` files contain both ΔH/Δλ and ΔH values, please see the header of each files for more information. Samples detailing the partial decoupling of the ligand from the protein-ligand complex are contained within the `complex` folder. These consist of an orientational restraint addition step (found within the `restraints-xvg` folders), charge annihilation step (found within the `coul-xvg` folders), and Van der Waals decoupling step (found within the `vdw-xvg` folders). Samples detailing the partial decoupling of the ligand from solvent are contained within the `ligand` folder and consist of a charge annihilation step (found within the individual `coul-xvg` folders) and a Van der Waals decoupling step (found within the individual `vdw-xvg` folders).

opencc-by-4.0Feb 2022View details →

Data of "Durability of self-healing cementitious systems with encapsulated polyurethane evaluated with a new pre-standard test method"

The dataset found here is related to the crack width measurements and water permeability tests performed in a study investigating the durability of self-healing cementitious systems with encapsulated polyurethane evaluated with a new pre-standard test method.

opencc-by-4.0Sep 2021View details →

Standardization of Methodology of Light-to-Heat Conversion Efficiency Determination for Colloidal Nanoheaters

Localized photothermal therapy (PTT) has been demonstrated to be a promising method of combating cancer, that additionally synergistically enhances other treatment modalities such as photodynamic therapy or chemotherapy. PTT exploits nanoparticles (called nanoheaters), that upon proper biofunctionalization may target cancerous tissues, and under light stimulation may convert the energy of photons to heat, leading to local overheating and treatment of cancerous cells. Despite extensive work, there is, however, no agreement on how to accurately and quantitatively compare light-to-heat conversion efficiency (ηQ) and rank the nanoheating performances of various groups of nanomaterials. This disagreement is highly problematic because the obtained ηQ values, measured with various methods, differ significantly for similar nanomaterials. In this work, we experimentally review existing optical setups, methods, and physical models used to evaluate ηQ. In order to draw a binding conclusion, we cross-check and critically evaluate the same Au@SiO2 sample in various experimental conditions. This critical study let us additionally compare and understand the influence of the other experimental factors, such as stirring, data recording and analysis, and assumptions on the effective mass of the system, in order to determine ηQ in a most straightforward and reproducible way. Our goal is therefore to contribute to the understanding, standardization, and reliable evaluation of ηQ measurements, aiming to accurately rank various nanoheater platforms.

opencc-by-4.0Apr 2022View details →

DLCC Gold Standard

Corresponding GitHub repository: <a href="https://github.com/janothan/DL-TC-Generator">DL-TC-Generator on GitHub</a>   Abstract Knowledge graph embedding is a representation learning technique which projects entities and relations in a knowledge graph to continuous vector spaces. Embeddings have gained a lot of uptake and have been heavily used in link prediction and other downstream prediction tasks. Most approaches are evaluated on a single task or a single group of tasks to determine their overall performance. The evaluation is then assessed in terms of how well the embedding approach performs on the task at hand, but it is hardly evaluated (and often not even deeply understood) what information the embedding approaches are actually learning to represent. To fill this gap, we present the DLCC (Description Logic Class Constructors) benchmark, a resource to analyze embedding approaches in terms of which kinds of classes they can represent. Two gold standards are presented, one based on the real world knowledge graph DBpedia, and one synthetic gold standard.

opencc-by-4.0May 2022View details →

Development of standardized definitions for Combined and Composite High Voltage Tests

Driven by the increasing demands on traceable measurement systems and calibration services, being able to fully cover the required needs, a large number of NMIs have put a great effort on the development of standardized wave shapes definitions, low voltage calibrators, measuring instruments and software for combined and composite high voltage tests. The European Metrology Programme for Innovation and Research (EMPIR) supports this work through the normative Joint Research Project 19NRM07 HV-com², which started in 2020 and has received funding from the EMPIR co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program.

opencc-by-4.0May 2022View details →

Mean, standard deviation, and percentiles of the schema and domain scores of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3) in a German opportunity sample (n=1,150)

Mean, standard deviation, and percentiles of the schema and domain scores of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3) in a German opportunity sample (n=1,150). Details are reported in: Kriston L, Schäfer J, Jacob GA, Härter M, Hölzel LP. Reliability and validity of the German version of the Young Schema Questionnaire - Short Form 3 (YSQ-S3). Eur J Psychol Assess 2013; 29: 205-212. IMPORTANT: This is an opportunity sample that is not representative of any well-defined population. Accordingly, the values should not be used as reference or norm values for the German version of the YSQ-S3.

opencc-by-4.0Jan 2022View details →

EvaNIL: silver standard dataset for large-scale NIL entity linking evaluation

The EvaNIL dataset can be used to train or evaluate approaches developed for NIL entity linking. It was built from several Biomedical and Life Sciences corpora: <ul> <li>PubMed DS</li> <li>CRAFT corpus</li> <li>MedMentions</li> </ul> These corpora contain entities associated with knowledge base concepts. To build the EvaNIL dataset, we assumed that those knowledge base concepts did not exist in the respective knowledge bases, so each entity is associated instead with the direct ancestors of those original concepts. The EvaNIL dataset is divided into 6 partitions including annotations from several knowledge bases: <ul> <li>"medic" (CTD-MEDIC)</li> <li>"ctd_anatomy" (CTD-Anatomy)</li> <li>"ctd_chemicals" (CTD-Chemicals)</li> <li>"chebi" (ChEBI)</li> <li>"go_bp" (GO-Biological Process)</li> <li>"hp" (HPO)</li> </ul>

dryad40/100

Standardized nuclear markers improve and homogenize species delimitation in Metazoa

Species are the fundamental units of life and evolution. Their recognition is essential for science and society. Molecular methods have been increasingly employed for the identification of animal species, despite several challenges. Here, we explore with genomic data from nine animal lineages a set of nuclear markers, namely metazoan-level universal single-copy orthologs (metazoan USCOs), for their use in species delimitation. Our data sets include arthropods and vertebrates. We use various data assembly strategies and employ coalescent-based species inference as well as population admixture analyses and phenetic methods. We demonstrate that metazoan USCOs well distinguish closely related morphospecies and consistently outperform classical mitochondrial DNA barcoding in discriminating closely related species in different animal taxa. USCOs overcome the general shortcomings of mitochondrial DNA barcodes, and due to standardization across Metazoa, also those of other approaches. They accurately assign samples not only to lower but also to higher taxonomic levels. Metazoan USCOs provide a powerful and unifying framework for DNA-based species delimitation and taxonomy in animals and their employment could result in a more efficient use of research data and resources.

opencc-zeroNov 2022View details →

opencc-by-4.0May 2022View details →

MOSBRI survey - Biophysical Data Standards and Accessibility

The needs for data standards and formats in molecular biophysics were analysed mainly via a survey focused on data producers and users in the field: Biophysical Data Standards and Accessibility. The questions were focused on identifying the expertise and scientific interests of the respondents, their use of techniques of molecular biophysics, views on the current situation and needs in data formats standardisation and needs for repositories or databases. The data were collected using LimeSurvey technology. Anonymized raw dat, their processing and interpretation included in this dataset. The work was performed as part of the project MOlecular Scale Biophysics Research Infrastructure (MOSBRI).

opencc-by-4.0Jun 2022View details →

Stress placement in Stress-standard Serbo-Croatian (speakers from Bor, Niš, Zaječar)

In order to obtain a good overview of the stress standard SC situation, we assembled a database of the 3000 most frequent verbs in the Serbian variety of standard Serbo-Croatian. For this purpose we used the web corpus srWac (Ljube&scaron;ić & Klubička 2014). We asked four stress standard SC speakers (two from Ni&scaron;, one from Bor and one from Zaječar) to pronounce one finite and one non-finite form of each verb. The speakers were asked to use the variety they would speak when talking to a stranger from their own hometown in a relatively formal setting (e.g. at a doctor’s or a government office).  Explanation of columns: INF: Citation form of the verb Theme type: Theme vowel class for transparent verbs, example verb used in the paper for intrasparent ones Transparency: "tra" for transparent verbs, "intra" for nontransparent verbs. N1_form_NF: Non-finite (infinitive-based) form produced by speaker 1 from Ni&scaron;. N1_form_NF_Stress_from_INFL: Location of stress in the non-finite (infinitive-based) form produced by speaker 1 from Ni&scaron;, counting from the INFL (i.e. from the +). N1_form_F: Finite (present-tense-based) form produced by speaker 1 from Ni&scaron;. N1_form_F_Stress_from_INFL: Location of stress in the the finite (present-tense-based) form produced by speaker 1 from Ni&scaron;, counting from the INFL (i.e. from the +).   N2_form_NF: Non-finite (infinitive-based) form produced by speaker 2 from Ni&scaron;. N2_form_NF_Stress_from_INFL: Location of stress in the non-finite (infinitive-based) form produced by speaker 2 from Ni&scaron;, counting from the INFL (i.e. from the +). N2_form_F: Finite (present-tense-based) form produced by speaker 2 from Ni&scaron;. N2_form_F_Stress_from_INFL: Location of stress in the the finite (present-tense-based) form produced by speaker 2 from Ni&scaron;, counting from the INFL (i.e. from the +). B_form_NF: Non-finite (infinitive-based) form produced by the speaker from Bor. B_form_NF_Stress_from_INFL: Location of stress in the non-finite (infinitive-based) form produced by the speaker from Bor, counting from the INFL (i.e. from the +). B_form_F: Finite (present-tense-based) form produced by the speaker from Bor. B_form_F_Stress_from_INFL: Location of stress in the the finite (present-tense-based) form produced by the speaker from Bor, counting from the INFL (i.e. from the +). Z_form_NF: Non-finite (infinitive-based) form produced by the speaker from Zaječar. Z_form_NF_Stress_from_INFL: Location of stress in the non-finite (infinitive-based) form produced by the speaker from Zaječar, counting from the INFL (i.e. from the +). Z_form_F: Finite (present-tense-based) form produced by the speaker from Zaječar. Z_form_F_Stress_from_INFL: Location of stress in the the finite (present-tense-based) form produced by the speaker from Zaječar, counting from the INFL (i.e. from the +).

Players' Profiles and Preferences in Standard and Adapted Levels: Dataset

Dataset produced in a study to measure the impact of levels' generation and adaptation to the players' preferences. The study was conducted in the context of a Master's thesis on Game Adaptivity.

opencc-by-4.0Mar 2022View details →

SocialDisNER corpus: gold standard annotations for detection of disease mentions in Spanish tweets

If you use any data from this repository, please cite our scientific paper instead of the Zenodo repo:  Luis Gasco Sánchez, Darryl Estrada Zavala, Eulàlia Farré-Maduell, Salvador Lima-López, Antonio Miranda-Escalada, and Martin Krallinger. 2022. <a href="https://aclanthology.org/2022.smm4h-1.48">The SocialDisNER shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora</a>. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 182–189, Gyeongju, Republic of Korea. Association for Computational Linguistics. <pre><code class="language-json">@inproceedings{gasco2022socialdisner, title = "The {S}ocial{D}is{NER} shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora", author = "Gasco S{\'a}nchez, Luis and Estrada Zavala, Darryl and Farr{\'e}-Maduell, Eul{\`a}lia and Lima-L{\'o}pez, Salvador and Miranda-Escalada, Antonio and Krallinger, Martin", booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.smm4h-1.48", pages = "182--189" }</code></pre>   Introduction: The SocialDisNER corpus of the SMM4H 2022 – Task 10 task focus on the recognition of disease mentions in tweets written in Spanish after selecting primarily first-hand experience of diseases and other health-relevant content (from patient associations, professional healthcare institutions, and through followers of patient association accounts of a diversity of pathologies including rare diseases, mental health, cancer, etc..). SocialDisNER Gold Standard The Gold Standard corpus was manually annotated by medical experts following the <a href="https://doi.org/10.5281/zenodo.6983041">SMM4H-SocialDisNER guidelines</a>. These guidelines were adapted from previous efforts used to annotate patient clinical records and medical literature. It covers rules for annotating mentions of diseases in health-related tweets in Spanish, The training set consists of 5000 tweets written in Spanish and the validation set consists of 2500 tweets written in Spanish. Both sets have been manually annotated by healthcare professionals. The test dataset contains 23430 tweets, although only 2000 will be used to evaluate the systems participating in the task (the rest is background set). We don't plan to publish the test set, but if you want you can test your system from <a href="https://codalab.lisn.upsaclay.fr/competitions/3531">SocialDisNER Codalab</a>. SocialDisNER Large Scale Corpus The large-scale data contains mentions automatically extracted from a set of 85000 tweets. Separate datasets are shown for each entity including diseases, drugs, symptoms, professions, procedures, species, morphology neoplasm, and persons. SocialDisNER co-mention networks We have computed a co-occurrence matrix of the extracted diseases, as well as several co-mention matrices between the disease mentions and the rest of the entities in the large-scale corpora.   File structure: The structure of the corpus is:  <ul> <li>SocialDisNER_Data: <ul> <li>training-validation-data folder <ul> <li>train-valid-txt-files:  folder with training and validation text files. One text file per tweet, the file name corresponds to the tweet id. One sub-directory per corpus split (train and valid). The files named ids_dev_set.txt and ids_train_set.txt contain the list of file identifiers for each of the data splits (validation and train).</li> <li>mentions.tsv: This file contains the manually annotated disease mentions. The file has the following fields: <ul> <li>tweets_id: This is the id of the tweet, using Twitter API you can query the content of the tweet.</li> <li>Begin: This is the position in the tweet where the annotation was found.</li> <li>End: This is the position of the last character of the annotation in the tweet.</li> <li>Type: This is the type of entity found, in our case "ENFERMEDAD".</li> <li>Extraction: This is the literal extraction, in other words, the fragment of text which refers to the annotation. </li> </ul> </li> </ul> </li> <li>test-data folder: <ul> <li>test-data-txt-files: folder with test text files. One file per tweet, the file name corresponds to the tweet id. The folder contains 23430 tweets to be used as test set of the task. Of them, 2000 will be used to evaluate the participating systems.</li> </ul> </li> </ul> </li> </ul>   <ul> <li>SocialDisNER_LargeScale_additionaldata: <ul> <li>socialdisner_diseases: <ul> <li>tweets_txt: Folder with large-scale tweet database. One text file per tweet, the file name corresponds to the tweet id.</li> <li>diseases_mentions.tsv: This file contains the automatically annotated disease mentions from the large-scale SocialDisNER corpus (Silver Standard). The structure is the same than the Golden Standard annotations.</li> </ul> </li> <li>socialdisner_ENTITY: Each folder with this naming convention contains the following data structure. Corpora have been generated with mentions of diseases, drugs, symptoms, professions, procedures, species, morphology neoplasm and persons <ul> <li>tweets_txt: Folder with large-scale tweet database. One text file per tweet, the file name corresponds to the tweet id.</li> <li>ENTITY_mentions.tsv: This file contains the automatically annotated mentions of type “ENTITY” from the large-scale SocialDisNER corpus (Silver Standard). The structure is the same than the Golden Standard annotations.</li> </ul> </li> <li>socialdisner_networks: This folder contains tsv files containing the co-mention matrices between the diseases and the rest of the entities of the large-scale socialdisner data. Each file follows the following naming convention: <ul> <li>socialdisner_disease-ENTITY_net.tsv:  The tsv file contains a series of columns and rows corresponding to the mentions used for building the matrix. Each column is separated by “;”. The type of each mention is identified by the label in parentheses of each title. The count represents the number of times that mention x and mention y were found in the same tweet of the large-scale dataset.</li> <li>socialdiser_disease_net.tsv: This tsv file contains the array of socialdisner-disease large-scale corpus co-mentions separated by ";". This file can be loaded into NetworkX to perform disease co-morbidity analysis on the socialdisner-disease large-scale data.</li> </ul> </li> </ul> </li> </ul> Note: In previous versions of the dataset the order of the columns in the mentions.tsv file was not in the correct order. From this version onwards the order is correct and adequate to send the predictions of the task.   For further information, please visit <a href="https://temu.bsc.es/socialdisner/">https://temu.bsc.es/socialdisner/</a> Summary statistics: <table> <caption>Manually annotated data</caption> <thead> <tr> <th scope="row"> </th> <th scope="col">Training set</th> <th scope="col">Development set</th> </tr> </thead> <tbody> <tr> <th scope="row"># tweets</th> <td>5000</td> <td>2500</td> </tr> <tr> <th scope="row"># characters</th> <td>1253431</td> <td>516768</td> </tr> <tr> <th scope="row"># tokens</th> <td>211555</td> <td>84478</td> </tr> <tr> <th scope="row">Avg. char / tweet</th> <td>250.69</td> <td>206.71</td> </tr> <tr> <th scope="row">Avg. tok. / tweet</th> <td>42.31</td> <td>33.79</td> </tr> <tr> <th scope="row"># mentions</th> <td>15173</td> <td>4252</td> </tr> <tr> <th scope="row"># unique mentions</th> <td>4407</td> <td>1413</td> </tr> </tbody> </table>   <table> <caption>Large-scale annotated data (Silver Standard)</caption> <tbody> <tr> <td> </td> <td>Socialdisner-diseases</td> <td>Socialdisner-pharma</td> <td>Socialdisner-morphology_neoplasms</td> <td>Socialdisner-symptoms</td> <td>Socialdisner-professions</td> <td>Socialdisner-Procedures</td> <td>Socialdisnerv-Person</td> <td>Socialdisner-Species</td> </tr> <tr> <td># tweets</td> <td>85077</td> <td>1759</td> <td>8518</td> <td>12624</td> <td>15831</td> <td>11462</td> <td>41033</td> <td>12118</td> </tr> <tr> <td># characters</td> <td>19920670</td> <td>435141</td> <td>2082574</td> <td>3023784</td> <td>4063114</td> <td>2873791</td> <td>10273278</td> <td>2933925</td> </tr> <tr> <td># tokens</td> <td>3236411</td> <td>68269</td> <td>332539</td> <td>521503</td> <td>660071</td> <td>467059</td> <td>1689479</td> <td>486249</td> </tr> <tr> <td>Avg. char / tweet</td> <td>234.15</td> <td>247.38</td> <td>244.49</td> <td>239.53</td> <td>256.66</td> <td>250.72</td> <td>250.37</td> <td>242.11</td> </tr> <tr> <td>Avg. tok. / tweet</td> <td>38.04</td> <td>38.81</td> <td>39.04</td> <td>41.31</td> <td>41.69</td> <td>40.75</td> <td>41.17</td> <td>40.13</td> </tr> <tr> <td># mentions</td> <td>116260</td> <td>1029</td> <td>8943</td> <td>12896</td> <td>18590</td> <td>10080</td> <td>58007</td> <td>14014</td> </tr> <tr> <td># unique mentions</td> <td>16034</td> <td>530</td> <td>541</td> <td>6991</td> <td>3667</td> <td>3841</td> <td>3446</td> <td>1676</td> </tr> </tbody> </table>       Do not share the data with other individuals/teams without permission from the task organizer. Tweets IDs are the primary source of information. Tweet texts are provided as support material. By downloading this resource, you agree to the Twitter <a href="https://twitter.com/en/tos">Terms of Service</a>, <a href="https://twitter.com/en/privacy">Privacy Policy</a>, <a href="https://developer.twitter.com/en/developer-terms/agreement">Developer Agreement</a>, and <a href="https://developer.twitter.com/en/developer-terms/policy">Developer Policy</a>.

Hunting for vampires and other unlikely forms of parity violation at the Large Hadron Collider: calo-image datasets for the standard model

An example calo-image dataset used in the <a href="https://arxiv.org/abs/2205.09876">paper</a>: standard model. Each shard is named `calo-image_sm_${DATASET}_${INDEX}.tar.gz`. Each contains one data file. DATASET is in {train,test,private_test} to label the three independent splits for training, validation, and testing respectively. INDEX labels separate batches which should be trivially combined. Each data file is in <a href="https://www.h5py.org/">h5</a> format. Its data are under the key "entries" as an array with shape (n, 32, 32) corresponding to (event_index, eta, phi) for the n calorimeter images in an unrolled eta--phi surface.

The Story Map of the Royal Netherlands Standardization Institute (NEN) on Institutionalizing Responsibility

The Royal Netherlands Standardization Institute (NEN) has started various research projects to explore how standardization can be made more socially responsible. The Delft University of Technology has explored what factors motivate, obstruct, and facilitate social responsibility in standardisation processes. As such, it supports NEN in understanding how it can better align its activities with the values, needs, and expectations of society.

Replication package for "Teams and Individuals in Standard Auction Formats: Decision and Emotions"

This is the replication package for the paper "Teams and Individuals in Standard Auction Formats: Decision and Emotions" by Maria Karmeliuk, Martin Kocher and Georg Schmidt, forthcoming in Experimental Economics. The package contains instructions and zTree code for running the laboratory experiment, as well as raw data from the experiment including the data from face recognition software FaceReader. It also contains Stata and R code for data cleaning, cleaned datasets and Stata code for replicating data analysis and statistics reported in the paper.

opencc-by-4.0Aug 2022View details →

Weekly Standardized Counts of Influenza Positive Isolates in 4 Region in China Mainland

The data include weekly standardized counts of influenza positive isolates in 4 region in China mainland that covers 30 provinces.More details about the method of standardization and division of regions can be seen in "Characterization of influenza seasonality in China, 2010-2018: implications for seasonal influenza vaccination timing".

Standardized Dataset of the Ecosystem's Water Use Efficiency, Gross Primary Productivity and the Evapotranspiration Deficit Index for 1982–2017 over the Middle East

This data aimed to investigate the spatial-temporal variability of Standardized Actual Evapotranspiration (sAET), Gross Primary Productivity (sGPP) and Water Use Efficiency (WUE) anomalies series, and the Standardized Evapotranspiration Deficit Index (SEDI). The Middle East (ME), was selected as a case study to monitoring  drought events as one of the major natural disasters for the ecosystem. To this end, the yearly gross primary production of GLASS, GIMMS,  FloxCom, and VPM datasets for the study area spanning 1982–2017 was used to develop the sGPPR data. On the other hand, the Global Land Evaporation Amsterdam Model (GLEAM-version (v3.3a)), which estimated the several components of terrestrial evaporation (annual actual and potential evaporation (AET, PET)) was used for the same period this aimed to detect the variability of the SEDI. This version of the yearly GLASS-sGPPR dataset (1982–2017) is available for the ME at 0.05° spatial resolution, as the original data of  the GPP-GLASS products, While, sGPPR dataset of GIMMS,  FloxCom, and VPM are also at annual temporal resolution, and at 0.5 degree spatial resolution spanning 1982–2016 for GIMMS,  FloxCom, and 2000-2016 for VPM (Excel wrokbook .xlsx). The SEDI data are also available at 0.25 degree spatial resolution for 1980–2018 ( Raster files (TIFF)). For more details about Standardization of the GPP and evapotranspiration deficit  data see: Alsafadi, K., Al-Ansari, N., Mokhtar, A., Mohammed, S., Elbeltagi, A., Sammen, S. S., & Bi, S. (2021). An evapotranspiration deficit-based drought index to detect variability of terrestrial carbon productivity in the Middle East. Environmental Research Letters. <a href="http://dx.doi.org/10.1088/1748-9326/ac4765">10.1088/1748-9326/ac4765</a>