cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/vgpgjdrlibjzar-uhfffaoysa-m/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/2C14H24N4.Cu.HI/c2*1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h2*7-10H,11H2,1-6H3;;1H/q;;+1;/p-1

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C{1H} NMR (101 MHz, Acetonitrile-d3, 25 °C): δ [ppm] = 163.0 (2C, C9), 151.3 (2C, C1), 131.7 (2C, C4), 129.7 (2C, C6), 129.1 (2C, C2), 122.8 (2C, C3), 121.4 (2C, C5), 62.8 (2C, C7), 46.9 (4C, C8), 40.3 (8C, C10).
DOI: 10.14272/vgpgjdrlibjzar-uhfffaoysa-m/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/2C14H24N4.Cu.HI/c2*1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h2*7-10H,11H2,1-6H3;;1H/q;;+1;/p-1

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Acetonitrile-d3, 25 °C): δ [ppm] = 7.18 (d, J = 7.5 Hz, 2H, H6), 7.16–7.11 (m, 2H, H4), 6.88–6.83 (m, 2H, H5), 6.46–6.41 (m, 2H, H3), 3.44 (s, 4H, H7), 2.70 (s, 24H, H10), 2.20 (s, 12H, H8).
DOI: 10.25345/c5p21s
title: MassIVE MSV000086907 - Evaluation of the altered proteome in NPC1 null mice
title: Evaluation of the altered proteome of NPC1 null mice
DOI: 10.14272/dhtdcywqwfegrn-uhfffaoysa-m.1
title: C14H24ClCuN4
title: InChI=1S/C14H24N4.ClH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1

description: This dataset contains experimental data from a sample (InChI=1S/C14H24N4.ClH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) nuclear magnetic resonance method infrared absorption spectroscopy (IR)
DOI: 10.14272/dhtdcywqwfegrn-uhfffaoysa-m/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C14H24N4.ClH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1

description: dataset for infrared absorption spectroscopy (IR) IR (KBr): ṽ [cm-1] = 2961 (m, C–Harom), 2922 (m, C–Harom), 2853 (m, C–Haliph), 2811 (w, C–Haliph), 1538 (m, C=N), 1530 (m), 1483 (w), 1417 (m), 1401 (m), 1393 (m), 1370 (w), 1335 (vw), 1259 (m), 1232 (vw), 1149 (w), 1098 (m), 1062 (m), 1028 (s), 1005 (s), 869 (m), 843 (m), 791 (vs), 757 (s), 734 (vw), 700 (vw), 553 (vw), 476 (vw), 458 (vw).
DOI: 10.14272/dhtdcywqwfegrn-uhfffaoysa-m/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C14H24N4.ClH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C{1H} NMR (101 MHz, Acetonitrile-d3, 25 °C): δ [ppm] = 164.1 (C9), 151.1 (C1), 132.3 (C6), 129.7 (C2), 129.6 (C4), 123.3 (C3), 122.0 (C5), 63.4 (C7), 46.4 (2C, C8), 40.2 (4C, C10).
DOI: 10.14272/dhtdcywqwfegrn-uhfffaoysa-m/chmo0001806
title: nuclear magnetic resonance method ()
title: InChI=1S/C14H24N4.ClH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1

description: dataset for nuclear magnetic resonance method
DOI: 10.14272/dhtdcywqwfegrn-uhfffaoysa-m/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C14H24N4.ClH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Acetonitrile-d3, 25 °C): δ [ppm] = 7.22 – 7.12 (m, 2H, H4+H6), 6.94 – 6.85 (m, 1H, H5), 6.44 (d, J = 7.8 Hz, 1H, H3), 3.48 (s, 2H, H7), 2.74 (s, 12H, H10), 2.24 (s, 6H, H8).
DOI: 10.14272/xjvutlxafqmecw-uhfffaoysa-l.1
title: C14H24Cu2I2N4
title: InChI=1S/C14H24N4.2Cu.2HI/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;;;/h7-10H,11H2,1-6H3;;;2*1H/q;2*+1;;/p-2

description: This dataset contains experimental data from a sample (InChI=1S/C14H24N4.2Cu.2HI/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;;;/h7-10H,11H2,1-6H3;;;2*1H/q;2*+1;;/p-2). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) nuclear magnetic resonance method
DOI: 10.14272/xjvutlxafqmecw-uhfffaoysa-l/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C14H24N4.2Cu.2HI/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;;;/h7-10H,11H2,1-6H3;;;2*1H/q;2*+1;;/p-2

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C{1H} NMR (101 MHz, Acetonitrile-d3, 25 °C) δ [ppm] = 165.1 (C9), 151.7 (C1), 133.1 (C4), 130.0 (C6), 129.0 (C2), 123.3 (C3), 121.9 (C5), 65.1 (C7), 48.7 (2C, C8), 40.6 (4C, C10).
DOI: 10.14272/xjvutlxafqmecw-uhfffaoysa-l/chmo0001806
title: nuclear magnetic resonance method ()
title: InChI=1S/C14H24N4.2Cu.2HI/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;;;/h7-10H,11H2,1-6H3;;;2*1H/q;2*+1;;/p-2

description: dataset for nuclear magnetic resonance method
DOI: 10.14272/xjvutlxafqmecw-uhfffaoysa-l/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C14H24N4.2Cu.2HI/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;;;/h7-10H,11H2,1-6H3;;;2*1H/q;2*+1;;/p-2

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Acetonitrile-d3, 25 °C): δ [ppm] = 7.23–7.17 (m, 1H, H6), 7.15–7.11 (m, 1H, H4), 6.89 (td, J = 7.4, 1.3 Hz, 1H, H5), 6.41 (dd, J = 7.9, 1.2 Hz, 1H, H3), 3.52 (s, 2H, H7), 2.75 (s, 12H, H10), 2.32 (s, 6H, H8).
DOI: 10.14272/ggyhsqsqrivbso-uhfffaoysa-m.1
title: C28H48BrCuN8
title: InChI=1S/2C14H24N4.BrH.Cu/c2*1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h2*7-10H,11H2,1-6H3;1H;/q;;;+1/p-1

description: This dataset contains experimental data from a sample (InChI=1S/2C14H24N4.BrH.Cu/c2*1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h2*7-10H,11H2,1-6H3;1H;/q;;;+1/p-1). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) nuclear magnetic resonance method
DOI: 10.14272/ggyhsqsqrivbso-uhfffaoysa-m/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/2C14H24N4.BrH.Cu/c2*1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h2*7-10H,11H2,1-6H3;1H;/q;;;+1/p-1

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Acetonitrile-d3, 25 °C): δ [ppm] = 7.21 – 7.16 (m, 2H, H6), 7.13 (td, J = 7.7, 1.5 Hz, 2H, H4), 6.85 (td, J = 7.4, 1.1 Hz, 2H, H5), 6.44 (dd, J = 7.9, 1.0 Hz, 2H, H3), 3.41 (s, 4H, H7), 2.68 (s, 24H, H10), 2.20 (s, 12H, H8).
DOI: 10.14272/ggyhsqsqrivbso-uhfffaoysa-m/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/2C14H24N4.BrH.Cu/c2*1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h2*7-10H,11H2,1-6H3;1H;/q;;;+1/p-1

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C{1H} NMR (101 MHz, Acetonitrile-d3, 25 °C): δ [ppm] = 162.6 (2C, C9), 151.4 (2C, C1), 131.5 (2C, C6), 129.9 (2C, C2), 129.0 (2C, C4), 122.8 (2C, C3), 121.4 (2C, C5), 62.6 (2C, C7), 46.5 (4C, C8), 40.1 (8C, C10).
DOI: 10.14272/ggyhsqsqrivbso-uhfffaoysa-m/chmo0001806
title: nuclear magnetic resonance method ()
title: InChI=1S/2C14H24N4.BrH.Cu/c2*1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h2*7-10H,11H2,1-6H3;1H;/q;;;+1/p-1

description: dataset for nuclear magnetic resonance method
DOI: 10.14272/xybuosnkfwkmii-uhfffaoysa-m.1
title: C14H24BrCuN4
title: InChI=1S/C14H24N4.BrH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1

description: This dataset contains experimental data from a sample (InChI=1S/C14H24N4.BrH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) nuclear magnetic resonance method infrared absorption spectroscopy (IR)
DOI: 10.14272/xybuosnkfwkmii-uhfffaoysa-m/chmo0000630
title: infrared absorption spectroscopy (IR) ()
title: InChI=1S/C14H24N4.BrH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1

description: dataset for infrared absorption spectroscopy (IR) IR (KBr): ṽ [cm-1] = 2961 (w, C–Harom), 2924 (m, C–Harom), 2852 (w, C–Haliph), 1543 (m, C=N), 1518 (s), 1481 (m), 1420 (m), 1406 (m), 1391 (m), 1373 (m), 1331 (w), 1261 (m), 1230 (vw), 1154 (m), 1097 (m), 1065 (m), 1029 (s), 1006 (s), 870 (m), 842 (m), 789 (vs), 756 (s), 730 (m), 694 (m), 630 (vw), 554 (w), 484 (m).
DOI: 10.14272/xybuosnkfwkmii-uhfffaoysa-m/chmo0001806
title: nuclear magnetic resonance method ()
title: InChI=1S/C14H24N4.BrH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1

description: dataset for nuclear magnetic resonance method
DOI: 10.14272/xybuosnkfwkmii-uhfffaoysa-m/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) ()
title: InChI=1S/C14H24N4.BrH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Acetonitrile-d3, 25 °C): δ [ppm] = 7.21–7.17 (m, 1H, H6), 7.13 (td, J = 7.7, 1.6 Hz, 1H, H4), 6.86 (td, J = 7.4, 1.2 Hz, 1H, H5), 6.44 (dd, J = 8.0, 1.1 Hz, 1H, H3), 3.42 (s, 2H, H7), 2.69 (s, 12H, H10), 2.20 (s, 6H, H8).
DOI: 10.14272/xybuosnkfwkmii-uhfffaoysa-m/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) ()
title: InChI=1S/C14H24N4.BrH.Cu/c1-16(2)11-12-9-7-8-10-13(12)15-14(17(3)4)18(5)6;;/h7-10H,11H2,1-6H3;1H;/q;;+1/p-1

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C{1H} NMR (101 MHz, Acetonitrile-d3, 25 °C): δ [ppm] = 162.8 (C9), 151.4 (C1), 131.7 (C6), 129.8 (C2), 129.1 (C4), 122.9 (C3), 121.5 (C5), 62.8 (C7), 46.6 (2C, C8), 40.2 (4C, C10).
DOI: 10.5281/zenodo.4549501
title: Dataset related to article "PSMA-PET and micro-ultrasound potential in the diagnostic pathway of prostate cancer"

description: This record contains raw data related to article “PSMA-PET and micro-ultrasound potential in the diagnostic pathway of prostate cancer" <strong>Purpose: </strong> To compare the diagnostic performance of <sup>68</sup>Ga-PSMA PET/TC with PRI-MUS (prostate risk identification using micro-ultrasound) in the primary diagnosis of prostate cancer (PCa). <strong>Methods: </strong> From September till December 2018, we prospectively enrolled 25 candidates to <sup>68</sup>Ga-PSMA PET/TRUS (transrectal ultrasound) fusion biopsy and compared them with PRI-MUS. This included patients with persistently elevated PSA and/or PHI (prostate health index) suspicious for PCa, negative digital rectal examination, with either negative or contraindication to mpMRI, and at least one negative biopsy. The diagnostic performance of the two modalities was calculated based on pathology results. <strong>Results: </strong> Overall, 20 patients were addressed to <sup>68</sup>Ga-PSMA PET/TRUS fusion biopsy. Mean SUVmax and SUVratio for PCa lesions resulted significantly higher than in benign lesions (p = 0.041 and 0.011, respectively). Using optimal cut-off points, <sup>68</sup>Ga-PSMA PET/CT demonstrated an overall accuracy of 83% for SUVmax ≥ 5.4 and 94% for SUVratio ≥ 2.2 in the detection of clinically significant PCa (GS ≥ 7). On counterpart, PRI-MUS results were: score 3 in nine patients (45%), score 4 in ten patients (50%), and one patient with score 5. PRI-MUS score 4 and 5 demonstrated an overall accuracy of 61% in detecting clinically significant PCa. <strong>Conclusion: </strong> In this highly-selected patient population, in comparison to PRI-MUS, <sup>68</sup>Ga-PSMA PET/CT shows a higher diagnostic performance.
DOI: 10.5281/zenodo.4549500
title: Dataset related to article "PSMA-PET and micro-ultrasound potential in the diagnostic pathway of prostate cancer"

description: This record contains raw data related to article “PSMA-PET and micro-ultrasound potential in the diagnostic pathway of prostate cancer" <strong>Purpose: </strong> To compare the diagnostic performance of <sup>68</sup>Ga-PSMA PET/TC with PRI-MUS (prostate risk identification using micro-ultrasound) in the primary diagnosis of prostate cancer (PCa). <strong>Methods: </strong> From September till December 2018, we prospectively enrolled 25 candidates to <sup>68</sup>Ga-PSMA PET/TRUS (transrectal ultrasound) fusion biopsy and compared them with PRI-MUS. This included patients with persistently elevated PSA and/or PHI (prostate health index) suspicious for PCa, negative digital rectal examination, with either negative or contraindication to mpMRI, and at least one negative biopsy. The diagnostic performance of the two modalities was calculated based on pathology results. <strong>Results: </strong> Overall, 20 patients were addressed to <sup>68</sup>Ga-PSMA PET/TRUS fusion biopsy. Mean SUVmax and SUVratio for PCa lesions resulted significantly higher than in benign lesions (p = 0.041 and 0.011, respectively). Using optimal cut-off points, <sup>68</sup>Ga-PSMA PET/CT demonstrated an overall accuracy of 83% for SUVmax ≥ 5.4 and 94% for SUVratio ≥ 2.2 in the detection of clinically significant PCa (GS ≥ 7). On counterpart, PRI-MUS results were: score 3 in nine patients (45%), score 4 in ten patients (50%), and one patient with score 5. PRI-MUS score 4 and 5 demonstrated an overall accuracy of 61% in detecting clinically significant PCa. <strong>Conclusion: </strong> In this highly-selected patient population, in comparison to PRI-MUS, <sup>68</sup>Ga-PSMA PET/CT shows a higher diagnostic performance.
DOI: 10.5281/zenodo.4549508
title: Supplemental Material for "Estimating abundance based on time-to-detection data"

description: 1. Many studies in ecology and management aim at quantifying absolute abundance based on counts at a set of surveyed sites. As time for data collection is typically limited, methods for reliable estimation of occupancy or abundance from low-cost data are desirable. Time-to-detection (TTD) models have shown promise for the estimation of occupancy. However, they remain heavily underutilized, and restricted to inference about occupancy, rather than abundance. 2. We developed a binomial N-mixture model for species-level TTD protocols that allows estimation of abundance with multiple- or single-visit data. An extension of the multi-visit version allows estimating availability per visit, given temporary emigration is random. We provide JAGS code and a new function (nmixTTD) in the R package unmarked for fitting a variety of such models. 3. Simulations showed accurate parameter estimation from single-visit species-level TTD data if individual detection probability is high (≥ ~0.7) and the number of visited sites is in the hundreds (≥ ~300). Additional visits improved the accuracy of estimates considerably. A comparison with the Royle-Nichols- and the classic binomial N-mixture-model revealed that the performance of our model is between these two, but require data that are less expensive and less error-prone than count data required for binomial N-mixture-models. In a case study, we found similar results when analysing data with the Royle-Nichols-, the binomial N-mixture-model or the multi-visit version of our TTD model. Analysing single-visit data with our model yielded lower abundance and higher detectability estimates. Presumably these differences are due to temporary emigration, as the single visit-method estimates the abundance of individuals available at one sampling occasion, whereas the multi-visit methods refer to the superpopulation, i.e. the number of individuals present over the study period. 4. Our new TTD-N-mixture model shows promise because it enables estimation of abundance, corrected for imperfect detection, for single- and multiple-visit data, based on data that is less expensive and that will be available in large quantities in the near future thanks to technical advances like autonomous recording units. The effects of unmodelled heterogeneity in detection rate and imperfect availability require further study.
DOI: 10.5281/zenodo.4459507
title: Supplemental Material for "Estimating abundance based on time-to-detection data"

description: 1. Many studies in ecology and management aim at quantifying absolute abundance based on counts at a set of surveyed sites. As time for data collection is typically limited, methods for reliable estimation of occupancy or abundance from low-cost data are desirable. Time-to-detection (TTD) models have shown promise for the estimation of occupancy. However, they remain heavily underutilized, and restricted to inference about occupancy, rather than abundance. 2. We developed a binomial N-mixture model for species-level TTD protocols that allows estimation of abundance with multiple- or single-visit data. An extension of the multi-visit version allows estimating availability per visit, given temporary emigration is random. We provide JAGS code and a new function (nmixTTD) in the R package unmarked for fitting a variety of such models. 3. Simulations showed accurate parameter estimation from single-visit species-level TTD data if individual detection probability is high (≥ ~0.7) and the number of visited sites is in the hundreds (≥ ~300). Additional visits improved the accuracy of estimates considerably. A comparison with the Royle-Nichols- and the classic binomial N-mixture-model revealed that the performance of our model is between these two, but require data that are less expensive and less error-prone than count data required for binomial N-mixture-models. In a case study, we found similar results when analysing data with the Royle-Nichols-, the binomial N-mixture-model or the multi-visit version of our TTD model. Analysing single-visit data with our model yielded lower abundance and higher detectability estimates. Presumably these differences are due to temporary emigration, as the single visit-method estimates the abundance of individuals available at one sampling occasion, whereas the multi-visit methods refer to the superpopulation, i.e. the number of individuals present over the study period. 4. Our new TTD-N-mixture model shows promise because it enables estimation of abundance, corrected for imperfect detection, for single- and multiple-visit data, based on data that is less expensive and that will be available in large quantities in the near future thanks to technical advances like autonomous recording units. The effects of unmodelled heterogeneity in detection rate and imperfect availability require further study.
DOI: 10.5281/zenodo.4549516
title: How to dismantle modern stressors: does a short trip to simulated Paleolithic conditions in the wild reduce cortisol levels? (Study data)

description: This is the dataset underlying the study "How to dismantle modern stressors: does a short trip to simulated Paleolithic conditions in the wild reduce cortisol levels?" The abstract of the study is as follows: <strong>Background:</strong> Chronic stress has become a central problem of our modern society, while it appears plausible that our Paleolithic ancestors were more exposed to acutely life-threatening stress stimuli than chronically enduring psychosocial stressors. The aim of this study was to test whether it is possible to reduce physiological and psychological stress parameters by returning to simulated Paleolithic conditions. <strong>Methods:</strong> In total, 15 volunteers undertook a four-day hiking tour simulating hunter-gatherer lifestyle conditions in a National Park. Saliva samples were taken at 5, 6 and 7 pm prior to, during and after the intervention and evaluated for cortisol concentration. Perceived stress was measured by the Perceived Stress Questionnaire. <strong>Results:</strong> The study was completed by 11 of the subjects. Body weight and fat mass decreased significantly during the intervention by 2.3 and 2.2 kg, respectively. There was a marked increase in cortisol levels on the first day of the intervention compared to baseline with a gradual return to baseline levels on the subsequent days. Individual cortisol responses were heterogeneous; in threes subjects, cortisol concentrations generally decreased from 5 to 7 pm, while in four subjects the opposite trend was observed. <strong>Conclusions:</strong> During this four-day outdoor intervention under simulated Paleolithic conditions, significant changes occurred almost exclusively at the first day of the study. The increase in cortisol levels at this point supports the hypothesis that some individuals respond by an increased release of cortisol as a result of adaptation to new environmental conditions.
DOI: 10.5281/zenodo.4549515
title: How to dismantle modern stressors: does a short trip to simulated Paleolithic conditions in the wild reduce cortisol levels? (Study data)

description: This is the dataset underlying the study "How to dismantle modern stressors: does a short trip to simulated Paleolithic conditions in the wild reduce cortisol levels?" The abstract of the study is as follows: <strong>Background:</strong> Chronic stress has become a central problem of our modern society, while it appears plausible that our Paleolithic ancestors were more exposed to acutely life-threatening stress stimuli than chronically enduring psychosocial stressors. The aim of this study was to test whether it is possible to reduce physiological and psychological stress parameters by returning to simulated Paleolithic conditions. <strong>Methods:</strong> In total, 15 volunteers undertook a four-day hiking tour simulating hunter-gatherer lifestyle conditions in a National Park. Saliva samples were taken at 5, 6 and 7 pm prior to, during and after the intervention and evaluated for cortisol concentration. Perceived stress was measured by the Perceived Stress Questionnaire. <strong>Results:</strong> The study was completed by 11 of the subjects. Body weight and fat mass decreased significantly during the intervention by 2.3 and 2.2 kg, respectively. There was a marked increase in cortisol levels on the first day of the intervention compared to baseline with a gradual return to baseline levels on the subsequent days. Individual cortisol responses were heterogeneous; in threes subjects, cortisol concentrations generally decreased from 5 to 7 pm, while in four subjects the opposite trend was observed. <strong>Conclusions:</strong> During this four-day outdoor intervention under simulated Paleolithic conditions, significant changes occurred almost exclusively at the first day of the study. The increase in cortisol levels at this point supports the hypothesis that some individuals respond by an increased release of cortisol as a result of adaptation to new environmental conditions.
DOI: 10.6084/m9.figshare.14055389
title: Dataset

description: Dataset
DOI: 10.15468/3qhjdc
title: Binhon atrum Pic - a junior synonym of Plastocerus thoracicus, with nomenclatural notes on Plastocerus (Coleoptera: Elateridae: Plastocerinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Hájek, Jiří, Ivie, Michael A., Lawrence, John F. (2020): Binhon atrum Pic - a junior synonym of Plastocerus thoracicus, with nomenclatural notes on Plastocerus (Coleoptera: Elateridae: Plastocerinae). Acta Entomologica Musei Nationalis Pragae 60 (2): 391-396, DOI: 10.37520/aemnp.2020.23
DOI: 10.7284/138857
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise LMG1702 using a Sea-Bird SBE-911+ instrument system onboard the platform RV Laurence M. Gould

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138858
title: Fluorometer data collected during research cruise LMG1702 using a WET Labs ECO-FL instrument system onboard the platform RV Laurence M. Gould

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/138863
title: GNSS (Global Navigation Satellite System) data collected during research cruise LMG1702 using a Garmin 19x instrument system onboard the platform RV Laurence M. Gould

description: Original (unprocessed) data collected during a Field_expedition