cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/cwgbqonhkmfzsz-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C32H28O4/c1-35-31(33)27-15-11-25(12-16-27)29-19-21-3-7-23(29)9-5-22-4-8-24(10-6-21)30(20-22)26-13-17-28(18-14-26)32(34)36-2/h3-4,7-8,11-20H,5-6,9-10H2,1-2H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2918 (m), 1714 (vs), 1604 (s), 1432 (m), 1272 (vs), 1188 (m), 1174 (s), 1115 (s), 1099 (vs), 1017 (s), 984 (m), 966 (m), 914 (s), 901 (m), 857 (vs), 843 (vs), 819 (s), 776 (vs), 731 (vs), 710 (vs), 687 (s), 660 (vs), 649 (s), 632 (m), 567 (s), 526 (m), 486 (vs), 446 (m), 384 (m) cm–1.
DOI: 10.5281/zenodo.7796248
title: R2D Tool Example E10SiteResponse

description: Example files for R2D Example 10: This example presents regional earthquake impact analysis including site response analysis and building damage and loss assessment. Overall 177 sites are included for soil response simulation under bedrock ground motion input with a building inventory including approximately 14,000 buildings in the city of Alameda, CA
description: This material is based upon work supported by the National Science Foundation under Grants No. 1621843 and No. 2131111
DOI: 10.14272/cwgbqonhkmfzsz-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C32H28O4/c1-35-31(33)27-15-11-25(12-16-27)29-19-21-3-7-23(29)9-5-22-4-8-24(10-6-21)30(20-22)26-13-17-28(18-14-26)32(34)36-2/h3-4,7-8,11-20H,5-6,9-10H2,1-2H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, CDCl3, ppm) δ = 167.1 (Cq, 2C, COO), 145.7 (Cq, 2C, CAr), 141.1 (Cq, 2C, CAr), 140.0 (Cq, 2C, CAr), 137.1 (Cq, 2C, CAr), 135.0 (+, 2C, CAr), 132.3 (+, 2C, CAr), 129.9 (+, 4C, CAr), 129.8 (+, 2C, CAr), 129.7 (+, 4C, CAr), 128.6 (+, 2C, CAr), 52.2 (+, 2C, CH3), 34.7 (–, 2C, CH2), 33.8 (–, 2C, CH2).
DOI: 10.6073/pasta/5c3b1a5dff48d9e0e4ad4d7ee186bd58
title: SPW01 Spatial and physical characteristics of bison wallows on Konza Prairie since 2011

description: The objective of this study was to characterize spatial and physical attributes of bison wallows at the Konza Prairie Biological Station in northeastern Kansas. We used aerial imagery from two different years (2011 and 2019) to assess the abundance and spatial distribution of wallows in relation to fire frequency, elevation, and slope. We also recorded physical characteristics for a randomly selected subset of wallows (n = 966) and analyzed these data in relation to the same landscape features. Results indicate that wallows are more abundant on areas characterized by combinations of more frequent burning, higher elevations, and little or no slope. Wallows were smaller in areas burned more often and shallower at higher elevations, particularly when located on grazing lawns. Terrestrial plants were found in approximately 72.1% of the wallows sampled, and their prevalence increased with increasing slope. We found some quantity of aquatic plants in approximately 7.1% of the sampled wallows. The probability of finding aquatic vegetation in wallows was higher on grazing lawns and in areas burned less frequently, particularly every 20 years.
DOI: 10.13140/rg.2.2.29101.97764
title: Quince rust (Gymnosporangium clavariiforme)
DOI: 10.6084/m9.figshare.22525099.v1
title: Main Figures

description: Data and code to recreate main figures in Identifying Properties of Pattern Completion Neurons in a Computational Model of the Visual Cortex
DOI: 10.25345/c56w96k4s
title: MassIVE MSV000091619 - Effect of Kinase Inhibitors on Intraerythrocytic Plasmodium falciparum Phosphoproteome
title: Three flasks of asynchronous blood stage P. falciparum Dd2 culture were grown to ~15% parasitemia, 4% hematocrit using the Trager- Jensen method. Per biological replicate, parasites were treated with 5 x EC50 or EC90 of compound depending on the slope of the EC50 curve for 3 hours. Compounds are DC-12717, DC-12834, BI-2536, GSK461364, and DC-12862. All compounds have three biological replicates, except DC-12862 which has one. As a control, culture was treated with an equal volume of DMSO vehicle. Parasites were collected through saponin lysis of erythrocytes, and parasite pellets were resuspended in 8 M urea lysis buffer (8 M urea, 50 mM Tris pH 8.5, 1% SDS), supplemented with a protease and phosphatase inhibitor cocktail. Parasites were lysed by sonication, and protein was quantified with the BCA protein assay. Samples were reduced, alkylated, resuspended, and digested. Resulting peptides were quantified and labelled with isobaric tandem mass tags (TMT). Following a ratio check, phospho-enrichment was performed with a Pierce High-Select Fe-NTA Phosphopeptide Enrichment Kit. The resultant phosphopeptide eluate was analyzed with LC-MS2. The peptide flowthrough was fractionated by basic reverse phase HPLC and similarly analyzed with LC-MS3. Spectra were searched against a composite P. falciparum and human Uniprot database (https://www.uniprot.org/), using the COMET algorithm. Peptide matches were filtered with a 1% false discovery rate (FDR), and peptides with a summed signal-to-noise (SN) threshold of &gt;100 were quantified. For phosphopeptides, a 1% FDR filter was used, and a summed SN threshold of greater than or equal to 100. Channels: 126 - Culture 1 Control 127N - Culture 2 Control 127C - Culture 3 Control 128N - Culture 1 DC-12717 128C - Culture 2 DC-12717 129N - Culture 3 DC-12717 129C - Culture 1 DC-12835 130N - Culture 2 DC-12835 130C - Culture 3 DC-12835 131N - Culture 1 BI-2536 131C - Culture 2 BI-2536 132N - Culture 3 BI-2536 132C - Culture 1 GSK461364 133N - Culture 2 GSK461364 133C - Culture 3 GSK461364 134N - Culture 1 DC-12862
DOI: 10.14272/ghraiodjvumzga-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C23H24N2/c1-25(2)23-16-18-4-8-19-7-3-17(5-9-21(23)10-6-18)15-22(19)20-11-13-24-14-12-20/h3,6-7,10-16H,4-5,8-9H2,1-2H3

description: dataset for mass spectrometry (MS) MS (ESI, 70 eV, 20 °C, %) m/z = 329 (100) [M+H]+.HRMS-ESI (m/z): [M+H]+, calc. for C23H24N2, 329.2013; found: 329.2009.
DOI: 10.14272/ghraiodjvumzga-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C23H24N2/c1-25(2)23-16-18-4-8-19-7-3-17(5-9-21(23)10-6-18)15-22(19)20-11-13-24-14-12-20/h3,6-7,10-16H,4-5,8-9H2,1-2H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3, ppm) δ = 8.64–8.58 (m, 2H, HPy), 7.38–7.32 (m, 2H, HPy), 6.75 (dd, J = 7.7, 1.9 Hz, 1H, HAr), 6.51 (d, J = 1.9 Hz, 1H, HAr), 6.47 (d, J = 7.7 Hz, 1H, HAr), 6.39 (d, J = 7.7 Hz, 1H, HAr), 6.13 (dd, J = 7.7, 1.8 Hz, 1H, HAr), 5.71 (d, J = 1.8 Hz, 1H, HAr), 3.44 (ddd, J = 12.8, 9.3, 2.9 Hz, 1H, HPC), 3.31 (ddd, J = 13.6, 10.1, 3.6 Hz, 1H, HPC), 3.13 (ddd, J = 13.0, 9.4, 6.1 Hz, 1H, HPC), 2.96–2.81 (m, 2H, HPC), 2.81–2.67 (m, 2H, HPC), 2.72 (s, 6H, HPC), 2.52 (ddd, J = 13.1, 10.1, 4.8 Hz, 1H, HPC).
DOI: 10.14272/ghraiodjvumzga-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C23H24N2/c1-25(2)23-16-18-4-8-19-7-3-17(5-9-21(23)10-6-18)15-22(19)20-11-13-24-14-12-20/h3,6-7,10-16H,4-5,8-9H2,1-2H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, CDCl3, ppm) δ = 151.9 (Cq, CPy), 149.6 (+, CH, 2C, CPy), 149.2 (Cq, CAr), 140.6 (Cq, CAr), 140.5 (Cq, CAr), 139.2 (Cq, CAr), 136.5 (Cq, CAr), 135.1 (+, CH, CAr), 134.7 (+, CH, CAr), 132.0 (+, CH, CAr), 130.7 (Cq, CAr), 129.6 (+, CH, CAr), 124.7 (+, CH, 2C, CPy), 123.4 (+, CH, CAr), 121.2 (+, CH, CAr), 43.8 (–, 2C, CH3), 34.8 (–, CH2), 34.6 (–, CH2), 33.7 (–, CH2), 33.5 (–, CH2).
DOI: 10.14272/ghraiodjvumzga-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C23H24N2/c1-25(2)23-16-18-4-8-19-7-3-17(5-9-21(23)10-6-18)15-22(19)20-11-13-24-14-12-20/h3,6-7,10-16H,4-5,8-9H2,1-2H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2932 (s), 2927 (s), 2850 (m), 1592 (vs), 1560 (w), 1540 (m), 1490 (s), 1476 (m), 1452 (s), 1436 (m), 1409 (s), 1402 (vs), 1329 (m), 1302 (m), 1213 (w), 1193 (m), 1166 (w), 1156 (w), 1137 (m), 1116 (m), 1092 (m), 1067 (m), 1050 (m), 992 (vs), 956 (m), 908 (s), 891 (m), 863 (m), 827 (s), 790 (m), 761 (m), 732 (vs), 711 (s), 694 (m), 687 (m), 663 (s), 643 (m), 633 (s), 622 (s), 602 (m), 594 (m), 571 (s), 550 (m), 527 (m), 501 (s), 482 (s), 455 (m), 443 (m), 436 (m), 429 (m), 412 (m), 399 (m), 392 (m), 381 (m) cm–1.
DOI: 10.14272/ghraiodjvumzga-uhfffaoysa-n.1
title: 4-Pyridyl-16-(N,N-dimethylamino)[2.2]paracyclophane
title: InChI=1S/C23H24N2/c1-25(2)23-16-18-4-8-19-7-3-17(5-9-21(23)10-6-18)15-22(19)20-11-13-24-14-12-20/h3,6-7,10-16H,4-5,8-9H2,1-2H3

description: This dataset contains experimental data from a sample (InChI=1S/C23H24N2/c1-25(2)23-16-18-4-8-19-7-3-17(5-9-21(23)10-6-18)15-22(19)20-11-13-24-14-12-20/h3,6-7,10-16H,4-5,8-9H2,1-2H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.14272/pgedkpihulcarl-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C38H30N2O8/c41-33-17-18-34(42)39(33)47-37(45)29-13-9-27(10-14-29)31-21-23-1-5-25(31)8-4-24-2-6-26(7-3-23)32(22-24)28-11-15-30(16-12-28)38(46)48-40-35(43)19-20-36(40)44/h1-2,5-6,9-16,21-22H,3-4,7-8,17-20H2

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2931 (w), 1772 (s), 1735 (vs), 1667 (w), 1604 (m), 1565 (w), 1425 (w), 1395 (w), 1364 (w), 1251 (w), 1232 (s), 1207 (vs), 1069 (s), 1047 (m), 1023 (w), 990 (vs), 956 (w), 914 (w), 856 (m), 836 (m), 810 (w), 765 (w), 732 (w), 720 (w), 701 (m), 647 (s), 605 (m), 575 (w), 564 (w), 524 (w), 504 (w), 480 (w), 426 (w), 398 (w), 387 (w) cm–1.
DOI: 10.5281/zenodo.7796170
title: Data accompanying Using Neural Networks to Learn the Forced Response of the Jet-Stream to Tropospheric Temperature Tendencies

description: Data used to train and evaluate a CNN. Details about data and the preprocessing can be found in the citation given below Charlotte Connolly, Elizabeth A. Barnes, Pedram Hassanzadeh, and Mike Pritchard: Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability, accepted to Artificial Intelligence for the Earth Systems 03/2023. Preprint available at https://arxiv.org/abs/2301.00496. Code found at https://doi.org/10.5281/zenodo.7796266.
DOI: 10.14272/pgedkpihulcarl-uhfffaoysa-n.1
title: Bis(2,5-dioxopyrrolidin-1-yl)-4.4'-(4,16-[2.2]paracyclophanyl) dibenzoate
title: InChI=1S/C38H30N2O8/c41-33-17-18-34(42)39(33)47-37(45)29-13-9-27(10-14-29)31-21-23-1-5-25(31)8-4-24-2-6-26(7-3-23)32(22-24)28-11-15-30(16-12-28)38(46)48-40-35(43)19-20-36(40)44/h1-2,5-6,9-16,21-22H,3-4,7-8,17-20H2

description: This dataset contains experimental data from a sample (InChI=1S/C38H30N2O8/c41-33-17-18-34(42)39(33)47-37(45)29-13-9-27(10-14-29)31-21-23-1-5-25(31)8-4-24-2-6-26(7-3-23)32(22-24)28-11-15-30(16-12-28)38(46)48-40-35(43)19-20-36(40)44/h1-2,5-6,9-16,21-22H,3-4,7-8,17-20H2). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.5281/zenodo.7796169
title: Data accompanying Using Neural Networks to Learn the Forced Response of the Jet-Stream to Tropospheric Temperature Tendencies

description: Data used to train and evaluate a CNN. Details about data and the preprocessing can be found in the citation given below Charlotte Connolly, Elizabeth A. Barnes, Pedram Hassanzadeh, and Mike Pritchard: Using Neural Networks to Learn the Jet Stream Forced Response from Natural Variability, accepted to Artificial Intelligence for the Earth Systems 03/2023. Preprint available at https://arxiv.org/abs/2301.00496. Code found at https://doi.org/10.5281/zenodo.7796266.
DOI: 10.14272/pgedkpihulcarl-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C38H30N2O8/c41-33-17-18-34(42)39(33)47-37(45)29-13-9-27(10-14-29)31-21-23-1-5-25(31)8-4-24-2-6-26(7-3-23)32(22-24)28-11-15-30(16-12-28)38(46)48-40-35(43)19-20-36(40)44/h1-2,5-6,9-16,21-22H,3-4,7-8,17-20H2

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, DMSO-d6, ppm) δ = 170.4 (Cq, 4C, COO), 161.8 (Cq, 2C, COO), 147.5 (Cq, 2C, CAr), 140.1 (Cq, 2C, CAr), 139.8 (Cq, 2C, CAr), 137.1 (Cq, 2C, CAr), 135.2 (+, 2C, CAr), 132.1 (+, 2C, CAr), 130.6 (+, 4C, CAr), 130.4 (+, 4C, CAr), 130.0 (+, 2C, CAr), 122.7 (Cq, 2C, CAr), 34.0 (–, 2C, CH2), 33.2 (–, 2C, CH2), 25.6 (–, 4C, CH2).This spectrum contains solvent peaks DMF at 30.7 ppm, 35.7 ppm, 162.3 ppm,
DOI: 10.14272/pgedkpihulcarl-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) ()
title: InChI=1S/C38H30N2O8/c41-33-17-18-34(42)39(33)47-37(45)29-13-9-27(10-14-29)31-21-23-1-5-25(31)8-4-24-2-6-26(7-3-23)32(22-24)28-11-15-30(16-12-28)38(46)48-40-35(43)19-20-36(40)44/h1-2,5-6,9-16,21-22H,3-4,7-8,17-20H2

description: dataset for mass spectrometry (MS) MS (ESI, 70 eV, 20 °C, %) m/z = 681(44) [M+K]+, 291(100) [C17H9NO4]+, 293(98) [C17H11NO4]+.HRMS-ESI (m/z): [M+H]+, calc. for C38H30KN2O8, 681.1634; found: 681.4193.
DOI: 10.14272/pgedkpihulcarl-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C38H30N2O8/c41-33-17-18-34(42)39(33)47-37(45)29-13-9-27(10-14-29)31-21-23-1-5-25(31)8-4-24-2-6-26(7-3-23)32(22-24)28-11-15-30(16-12-28)38(46)48-40-35(43)19-20-36(40)44/h1-2,5-6,9-16,21-22H,3-4,7-8,17-20H2

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, DMSO-d6, ppm) δ = 8.26–8.21 (m, 4H, HAr), 7.88–7.82 (m, 4H, HAr), 6.90 (d, J = 7.8 Hz, 2H, HAr), 6.82 (d, J = 1.8 Hz, 2H, HAr), 6.55 (dd, J = 7.7, 1.8 Hz, 2H, HAr), 3.39–3.32 (m, 2H, HPC), 3.11 (ddd, J = 13.8, 10.0, 4.6 Hz, 2H, HPC), 2.95–2.88 (m, J = 16.4 Hz, 10H, HPC, CH2), 2.70–2.61 (m, 2H, HPC).This spectrum contains solvent peaks DMF at 2.86 ppm, 2.96 ppm, 7.96 ppm,
DOI: 10.5281/zenodo.7796264
title: Dataset for Manuscript: Comparing Urban Anthropogenic NMVOC Measurements with Representation in Emission Inventories - A Global Perspective

description: Urban observations of individual NMVOCs and the calculated or reported emission ratios used for comparison to emission inventories.
DOI: 10.5281/zenodo.7796263
title: Dataset for Manuscript: Comparing Urban Anthropogenic NMVOC Measurements with Representation in Emission Inventories - A Global Perspective

description: Urban observations of individual NMVOCs and the calculated or reported emission ratios used for comparison to emission inventories.
DOI: 10.6084/m9.figshare.22527298
title: Metadata

description: Metadata explaining the field codes for the 'data' used to support 'Landscape-scale benefits of protected areas for tropical biodiversity'
DOI: 10.13140/rg.2.2.13163.62248
title: Heloderma suspectum
DOI: 10.13140/rg.2.2.26585.39521
title: Heloderma suspectum
DOI: 10.14278/rodare.2245
title: Data publication: ESFR-SMART T1.2.4

description: Coupled Neutronic/Thermal-Hydraulic calculations of the European Sodium cooled Fast Reactor core with reactor simulator DYN3D in the framework of the H2020 ESFR-SMART project, Task 1.2.4.
DOI: 10.14278/rodare.2246
title: Data publication: ESFR-SMART T1.2.4

description: Coupled Neutronic/Thermal-Hydraulic calculations of the European Sodium cooled Fast Reactor core with reactor simulator DYN3D in the framework of the H2020 ESFR-SMART project, Task 1.2.4.
DOI: 10.6084/m9.figshare.22203629
title: Hypotheses Testing from Complex Survey Data Using Bootstrap Weights: A Unified Approach

description: Standard statistical methods without taking proper account of the complexity of a survey design can lead to erroneous inferences when applied to survey data due to unequal selection probabilities, clustering, and other design features. In particular, the type I error rates of hypotheses tests using standard methods can be much larger than the nominal significance level. Methods incorporating design features in testing hypotheses have been proposed, including Wald tests and quasi-score tests that involve estimated covariance matrices of parameter estimates. In this paper, we present a unified approach to hypothesis testing without requiring estimated covariance matrices or design effects, by constructing bootstrap approximations to quasi-likelihood ratio statistics and quasi-score statistics and establishing its asymptotic validity. The proposed method can be easily implemented without a specific software designed for complex survey sampling. We also consider hypothesis testing for categorical data and present a bootstrap procedure for testing simple goodness of fit and independence in a two-way table. In simulation studies, the type I error rates of the proposed approach are much closer to their nominal significance level compared with the naive likelihood ratio test and quasi-score test. An application to an educational survey under a logistic regression model is also presented.
DOI: 10.6084/m9.figshare.22203629.v2
title: Hypotheses Testing from Complex Survey Data Using Bootstrap Weights: A Unified Approach

description: Standard statistical methods without taking proper account of the complexity of a survey design can lead to erroneous inferences when applied to survey data due to unequal selection probabilities, clustering, and other design features. In particular, the Type I error rates of hypotheses tests using standard methods can be much larger than the nominal significance level. Methods incorporating design features in testing hypotheses have been proposed, including Wald tests and quasi-score tests that involve estimated covariance matrices of parameter estimates. In this article, we present a unified approach to hypothesis testing without requiring estimated covariance matrices or design effects, by constructing bootstrap approximations to quasi-likelihood ratio statistics and quasi-score statistics and establishing its asymptotic validity. The proposed method can be easily implemented without a specific software designed for complex survey sampling. We also consider hypothesis testing for categorical data and present a bootstrap procedure for testing simple goodness of fit and independence in a two-way table. In simulation studies, the Type I error rates of the proposed approach are much closer to their nominal significance level compared with the naive likelihood ratio test and quasi-score test. An application to an educational survey under a logistic regression model is also presented. Supplementary materials for this article are available online.
DOI: 10.48577/jpl.34jch1
title: Watching the Earth Breathe: Space-Based Monitoring of Carbon Dioxide and Solar-Induced Fluorescence with OCO-2 and OCO-3

description: No abstract available.
DOI: 10.25345/c5348gs1f
title: MassIVE MSV000091620 - Effect of salinity stress on the exoproteome profile of Bacillus amyloliquefaciens EB2003A
title: The study evaluated the effect of 200 mM NaCl on the exoproteome profile of Bacillus amyloliquefaciens. 0 mM NaCl was the control
DOI: 10.5281/zenodo.7795947
title: Upper thermal limits of Hediste diversicolor under global and local change scenarios

description: Raw data on wet weight and upper thermal tolerance limits (CTMax) of the ragworm <em>Hediste diversicolor</em> collected at Ria de Aveiro (Portugal) and subjected to a combination of different temperatures (24, 27 and 30 ºC) and salinities (20 and 30) after 29 days of acclimation. Wet weight data was obtained post-CTMax assay.
description: We would like to acknowledge project AquaMMIn (MAR-02.01.01- FEAMP-0038), co-funded by Portugal (2020) and the European Union through Mar2020, the Operational Programme (OP) for the European Maritime and Fisheries Fund (EMFF) in Portugal. We also acknowledge financial support to CESAM by FCT/MCTES (UIDP/50017/2020+UIDB/50017/2020+LA/P/0094/2020), through national funds, to L'Oreal/FCT/UNESCO for the prize L'Oreal Medals for Women in Science Portugal awarded to D.M., to FCT/ MEC through Scientific Employment Stimulus – Individual Call 2018 (CEECIND/01250/2018 to DM) and PhD scholarship call 2021 (2021.04675.BD to JFF), to Do*MAR Doctoral Programme [PD/BD/127989/2016 to DJ] and to the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020.
DOI: 10.5281/zenodo.7795946
title: Upper thermal limits of Hediste diversicolor under global and local change scenarios

description: Raw data on wet weight and upper thermal tolerance limits (CTMax) of the ragworm <em>Hediste diversicolor</em> collected at Ria de Aveiro (Portugal) and subjected to a combination of different temperatures (24, 27 and 30 ºC) and salinities (20 and 30) after 29 days of acclimation. Wet weight data was obtained post-CTMax assay.
description: We would like to acknowledge project AquaMMIn (MAR-02.01.01- FEAMP-0038), co-funded by Portugal (2020) and the European Union through Mar2020, the Operational Programme (OP) for the European Maritime and Fisheries Fund (EMFF) in Portugal. We also acknowledge financial support to CESAM by FCT/MCTES (UIDP/50017/2020+UIDB/50017/2020+LA/P/0094/2020), through national funds, to L'Oreal/FCT/UNESCO for the prize L'Oreal Medals for Women in Science Portugal awarded to D.M., to FCT/ MEC through Scientific Employment Stimulus – Individual Call 2018 (CEECIND/01250/2018 to DM) and PhD scholarship call 2021 (2021.04675.BD to JFF), to Do*MAR Doctoral Programme [PD/BD/127989/2016 to DJ] and to the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020.
DOI: 10.13140/rg.2.2.36651.72484
title: Heloderma suspectum
DOI: 10.13140/rg.2.2.30255.41123
title: Heloderma suspectum
DOI: 10.22003/xfel.eu-data-003449-00
title: Diagnosing relativistic surface plasmon propagation using GISAXS
DOI: 10.6084/m9.figshare.22246198
title: Supplementary Material – Traditional botanical knowledge: food plants from the Huni Kuĩ indigenous people, Acre, western Brazilian Amazon

description: Table S1 – Survey of food species in villages in the <em>Kaxinawá do Baixo Rio Jordão </em>Indigenous Land. Characterization of species by: botanical family, scientific name, indigenous name, growth form, habitat, eaten part, form of consumption, likely origin and frequency with which the particular species appeared in the interviews. Table S2 – Overview of scientifical studies on traditional food species: ethnobotanical food uses and bromatological composition in villages in the <em>Kaxinawá do Baixo Rio Jordão </em>Indigenous Land. Part of the Original Paper published in Rodriguesia – DOI: http://dx.doi.org/10.1590/2175-7860202374016
DOI: 10.6084/m9.figshare.22246198.v2
title: Supplementary Material – Traditional botanical knowledge: food plants from the Huni Kuĩ indigenous people, Acre, western Brazilian Amazon

description: Table S1 – Survey of food species in villages in the <em>Kaxinawá do Baixo Rio Jordão </em>Indigenous Land. Characterization of species by: botanical family, scientific name, indigenous name, growth form, habitat, eaten part, form of consumption, likely origin and frequency with which the particular species appeared in the interviews. Table S2 – Overview of scientifical studies on traditional food species: ethnobotanical food uses and bromatological composition in villages in the <em>Kaxinawá do Baixo Rio Jordão </em>Indigenous Land. Part of the Original Paper published in Rodriguesia – DOI: http://dx.doi.org/10.1590/2175-7860202374016