cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6085/aa/ybhx00_xxxitv2xmmr02_20191114.50.1
title: PISCO: Intertidal: mussel growth temperature data: Yachats Beach, Oregon, USA (YBHX00)

description: This metadata record describes a mix of intertidal seawater and air temperature data collected at Yachats Beach, Oregon, USA by PISCO. Measurements were collected using Onset Tidbit V2 Temp Data Logger (Onset Computer Corp. UTBI-001) beginning 2019-11-14. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 10.0 minute intervals.
DOI: 10.6085/aa/ybhx00_xxxitv2xmmr02_20200410.40.1
title: (:unkn)
DOI: 10.6085/aa/ybhx00_xxxitv2xmmr02_20200410.50.1
title: PISCO: Intertidal: mussel growth temperature data: Yachats Beach, Oregon, USA (YBHX00)

description: This metadata record describes a mix of intertidal seawater and air temperature data collected at Yachats Beach, Oregon, USA by PISCO. Measurements were collected using Onset Tidbit V2 Temp Data Logger (Onset Computer Corp. UTBI-001) beginning 2020-04-10. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 10.0 minute intervals.
DOI: 10.6085/aa/ybhx00_xxxitv2xmmr03_20191114.40.1
title: (:unkn)
DOI: 10.6085/aa/ybhx00_xxxitv2xmmr03_20200410.40.1
title: (:unkn)
DOI: 10.6085/aa/ybhx00_xxxitv2xmmr03_20191114.50.1
title: PISCO: Intertidal: mussel growth temperature data: Yachats Beach, Oregon, USA (YBHX00)

description: This metadata record describes a mix of intertidal seawater and air temperature data collected at Yachats Beach, Oregon, USA by PISCO. Measurements were collected using Onset Tidbit V2 Temp Data Logger (Onset Computer Corp. UTBI-001) beginning 2019-11-14. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 10.0 minute intervals.
DOI: 10.6085/aa/ybhx00_xxxitv2xmmr03_20200410.50.1
title: PISCO: Intertidal: mussel growth temperature data: Yachats Beach, Oregon, USA (YBHX00)

description: This metadata record describes a mix of intertidal seawater and air temperature data collected at Yachats Beach, Oregon, USA by PISCO. Measurements were collected using Onset Tidbit V2 Temp Data Logger (Onset Computer Corp. UTBI-001) beginning 2020-04-10. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 10.0 minute intervals.
DOI: 10.6085/aa/ybhx00_xxxitv2xmmr03_20200919.40.1
title: (:unkn)
DOI: 10.6085/aa/ybhx00_xxxitv2xmmr03_20200919.50.1
title: PISCO: Intertidal: mussel growth temperature data: Yachats Beach, Oregon, USA (YBHX00)

description: This metadata record describes a mix of intertidal seawater and air temperature data collected at Yachats Beach, Oregon, USA by PISCO. Measurements were collected using Onset Tidbit V2 Temp Data Logger (Onset Computer Corp. UTBI-001) beginning 2020-09-19. Site temperature loggers are bolted down in a wire cage at various locations within each site. Mussel growth temperature loggers are bolted down in a wire cage at high, mid, or low positions within a mussel bed. Temperature is recorded at 10.0 minute intervals.
DOI: 10.6084/m9.figshare.16696192
title: mco.xlsx

description: This data is mining based on hashtag from social media for movement tracking during COVID-19 movement control order (MCO).<br>
DOI: 10.6084/m9.figshare.16696192.v1
title: mco.xlsx

description: This data is mining based on hashtag from social media for movement tracking during COVID-19 movement control order (MCO).<br>
DOI: 10.6084/m9.figshare.16677640
title: Output of the planned and exploratory analysis from Age differences in COVID-19 risk-taking, and the relationship with risk attitude and numerical ability

description: This document contains the output of the analysis, both planned and exploratory.
DOI: 10.6084/m9.figshare.16677655
title: Output of data cleaning from Age differences in COVID-19 risk-taking, and the relationship with risk attitude and numerical ability

description: This document contains the output of the data cleaning for this project.
DOI: 10.6084/m9.figshare.16677640.v2
title: Output of the planned and exploratory analysis from Age differences in COVID-19 risk-taking, and the relationship with risk attitude and numerical ability

description: This document contains the output of the analysis, both planned and exploratory.
DOI: 10.6084/m9.figshare.16677655.v2
title: Output of data cleaning from Age differences in COVID-19 risk-taking, and the relationship with risk attitude and numerical ability

description: This document contains the output of the data cleaning for this project.
DOI: 10.6084/m9.figshare.16677655.v1
title: Output of data cleaning from Age differences in COVID-19 risk-taking, and the relationship with risk attitude and numerical ability

description: This document contains the output of the data cleaning for this project.
DOI: 10.6084/m9.figshare.16677640.v1
title: Output of the planned and exploratory analysis from Age differences in COVID-19 risk-taking, and the relationship with risk attitude and numerical ability

description: This document contains the output of the analysis, both planned and exploratory.
DOI: 10.25493/zvhs-83b
title: Probabilistic cytoarchitectonic map of SF (Amygdala) (v8.0)

description: This dataset contains the distinct architectonic SF (Amygdala) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using classical histological criteria and quantitative cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to both reference spaces, where each voxel was assigned the probability to belong to SF (Amygdala). The probability map of SF (Amygdala) is provided in the NifTi format for each brain reference space and hemisphere. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and integration of new brain structures may lead to small deviations in earlier released datasets. Other available data versions of SF (Amygdala): Kedo et al. (2018) [Data set, v6.1] [DOI: 10.25493/BRNZ-SXW](https://doi.org/10.25493%2FBRNZ-SXW) Kedo et al. (2019) [Data set, v6.4] [DOI: 10.25493/WD31-SEA](https://doi.org/10.25493%2FWD31-SEA) The most probable delineation of SF (Amygdala) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.13] [DOI: 10.25493/Q3ZS-NV6](https://doi.org/10.25493%2FQ3ZS-NV6) Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D) Amunts et al. (2020) [Data set, v2.4] [DOI: 10.25493/A7Y0-NX9](https://doi.org/10.25493%2FA7Y0-NX9) Amunts et al. (2020) [Data set, v2.5] [DOI: 10.25493/8JKE-M53](https://doi.org/10.25493/8JKE-M53) Amunts et al. (2021) [Data set, v2.6] [DOI: 10.25493/KJQN-AM0](https://doi.org/10.25493%2FKJQN-AM0)
DOI: 10.25493/svey-zbs
title: Probabilistic cytoarchitectonic map of Area hIP6 (IPS) (v7.0)

description: This dataset contains the distinct architectonic Area hIP6 (IPS) in the individual, single subject template of the MNI Colin 27 as well as the MNI ICBM 152 2009c nonlinear asymmetric reference space. As part of the Julich-Brain cytoarchitectonic atlas, the area was identified using cytoarchitectonic analysis on cell-body-stained histological sections of 10 human postmortem brains obtained from the body donor program of the University of Düsseldorf. The results of the cytoarchitectonic analysis were then mapped to the reference space, where each voxel was assigned the probability to belong to Area hIP6 (IPS). The probability map of Area hIP6 (IPS) is provided in NifTi format for each hemisphere in the reference space. The Julich-Brain atlas relies on a modular, flexible and adaptive framework containing workflows to create the probabilistic brain maps for these structures. Note that methodological improvements and updated probability estimates for new brain structures may in some cases lead to measurable but negligible deviations of existing probability maps, as compared to earlier released datasets. Other available data versions of Area hIP6 (IPS): Richter et al. (2019) [Data set, v7.1] [DOI: 10.25493/AFQR-50Q](https://doi.org/10.25493%2FAFQR-50Q) The most probable delineation of Area hIP6 (IPS) derived from the calculation of a maximum probability map of all currently released Julich-Brain brain structures can be found here: Amunts et al. (2019) [Data set, v1.18] [DOI: 10.25493/8EGG-ZAR](https://doi.org/10.25493%2F8EGG-ZAR) Amunts et al. (2020) [Data set, v2.2] [DOI: 10.25493/TAKY-64D](https://doi.org/10.25493%2FTAKY-64D)
DOI: 10.25982/44746.21/1635640
title: Complete Genome Sequence of Terriglobus albidus Strain ORNL, an Acidobacterium Isolated from the Populus deltoides Rhizosphere

description: This Narrative was used for a complete genome sequence of a new strain of Terriglobus albidus. It is a heterotrophic bacterium associated with the rhizophere of the eastern cottonwood tree (Populus deltoides).
DOI: 10.3886/e150981
title: The Effect of Brief Mindfulness Induction on Memory Bias for Emotional Stimuli

description: This study aimed to investigate the effect of mindfulness induction on memory bias for emotional stimuli. In total, 60 college students were assigned randomly to either mindfulness group or control group. Experiments were conducted on two separate days with a one-week interval in between. In day-1 experiment, mindfulness group proceeded in the order of education and treatment, while control group proceeded with the treatment phase without prior education. Participants in mindfulness group were instructed to observe, without judgment, the thoughts, feelings, and body sensations that arose when they heard the sound coming from the computer. Participants in control group were only informed that the sound was coming from the computer, and no special instructions were given. after treatment for each group, learning task was conducted. In learning task, picture stimuli were presented as either a positive-neutral stimulus pair or negative-neutral stimulus pair, rather than as a single stimulus. A week later, in day-2 experiment, a recognition task was conducted. In recognition task, participants were asked to distinguish between the pictures they had seen in the day-1 experiment and the pictures they were seeing for the first time. Recognition accuracy and memory bias index were calculated according to results of recognition task.
description: Response Rates: A total of 60 college students were recruited. Although 58 participants participated in both experiments, the results of only 56 participants (male: 14, female: 42) were analyzed. 2 participants had been excluded due to attitude problems, such as using a mobile phone during the experiment.
description: In a psychology-related lecture, people who voluntarily wanted to participate in the study were recruited.
description: College student in South Korea.
DOI: 10.21227/fnea-8z25
title: Water samples for turbidity detection

description: Computer vision and image processing have made significant progress in many real-world applications, including environmental monitoring and protection. Recent studies have shown that computer vision and image processing can be used to quantify water turbidity, a crucial physical parameter in water quality assessment. This paper presents a procedure to determine water turbidity using deep learning methods, specifically, convolutional neural network (CNN). At first, water samples were located inside a dark cabin before digital images of the samples were captured with a smartphone camera. A total of 71 samples were taken, representing varying magnitudes of nephelometry turbidity unit (NTU) between 0 to 100. In this research, CNN models were used to detect water turbidity. Based on the results, when grayscale images were used, and an NTU of less than 5 are considered images of clean water, the CNN algorithm achieved an accuracy of 0.9811 and a loss of 0.0514. When color images were used and an NTU less than 5 is considered clean water, the CNN algorithm achieved an accuracy of 0.9811 and a loss of 0.0414. The exact process was repeated when grayscale and color images were used, with NTU less than 15 considered as clean water. The performance of Python and R in terms of time required to train a convolutional neural network using Keras was compared. The results found that training a CNN Keras model in Python is faster than R, while the accuracy of the provided model is independent of the programming language
DOI: 10.6084/m9.figshare.16696243
title: Additional file 4 of Activity of the mouse Notch ligand DLL1 is sensitive to C-terminal tagging in vivo

description: Additional file 4: Table S1. Quantification of DLL1AcGFPHA cell surface presentation. The determined values of inputs (full length and cleaved product) were multiplied with the factor 50 and the Avidin pull downs (full length and cleaved product) with the factor 2,2 to calculate the total amount of detected DLL1 in the lysate and IP in each sample. Each sample was analysed twice (#x.1 and #x.2).
DOI: 10.6084/m9.figshare.16696243.v1
title: Additional file 4 of Activity of the mouse Notch ligand DLL1 is sensitive to C-terminal tagging in vivo

description: Additional file 4: Table S1. Quantification of DLL1AcGFPHA cell surface presentation. The determined values of inputs (full length and cleaved product) were multiplied with the factor 50 and the Avidin pull downs (full length and cleaved product) with the factor 2,2 to calculate the total amount of detected DLL1 in the lysate and IP in each sample. Each sample was analysed twice (#x.1 and #x.2).
DOI: 10.6084/m9.figshare.16696246
title: Additional file 5 of Activity of the mouse Notch ligand DLL1 is sensitive to C-terminal tagging in vivo

description: Additional file 5: Table S2. Quantification of DLL1SF cell surface presentation. The determined values of inputs (full length and cleaved product) were multiplied with the factor 50 and the Avidin pull downs (full length and cleaved product) with the factor 2,2 to calculate the total amount of detected DLL1 in the lysate and IP in each sample. Each sample was analysed twice (#x.1 and #x.2).
DOI: 10.6084/m9.figshare.16696246.v1
title: Additional file 5 of Activity of the mouse Notch ligand DLL1 is sensitive to C-terminal tagging in vivo

description: Additional file 5: Table S2. Quantification of DLL1SF cell surface presentation. The determined values of inputs (full length and cleaved product) were multiplied with the factor 50 and the Avidin pull downs (full length and cleaved product) with the factor 2,2 to calculate the total amount of detected DLL1 in the lysate and IP in each sample. Each sample was analysed twice (#x.1 and #x.2).
DOI: 10.6084/m9.figshare.16696249
title: Additional file 6 of Activity of the mouse Notch ligand DLL1 is sensitive to C-terminal tagging in vivo

description: Additional file 6: Table S3. Proteins detected in DLL1 complexes. Listed are significantly detected proteins. The full mass spectrometry data are available in the PRIDE database under Accession number PXD024680.
DOI: 10.6084/m9.figshare.16696249.v1
title: Additional file 6 of Activity of the mouse Notch ligand DLL1 is sensitive to C-terminal tagging in vivo

description: Additional file 6: Table S3. Proteins detected in DLL1 complexes. Listed are significantly detected proteins. The full mass spectrometry data are available in the PRIDE database under Accession number PXD024680.
DOI: 10.25345/c5cz5g
title: MassIVE MSV000088169 - IBA Strain Yeast Proteomics Project
title: Raw mass spectrometry files for proteomics results of IBA yeast strains
DOI: 10.6084/m9.figshare.16696293
title: Additional file 1 of Oral iron supplementation after antibiotic exposure induces a deleterious recovery of the gut microbiota

description: Additional file 1.
DOI: 10.6084/m9.figshare.16696293.v1
title: Additional file 1 of Oral iron supplementation after antibiotic exposure induces a deleterious recovery of the gut microbiota

description: Additional file 1.
DOI: 10.6084/m9.figshare.16664468.v1
title: Additional file 1 of Same, same, but different? A longitudinal, mixed-methods study of stability in values and preferences for future end-of-life care among community-dwelling, older adults

description: Additional file 1. Overview of participants’ card choices.
DOI: 10.6084/m9.figshare.16664468
title: Additional file 1 of Same, same, but different? A longitudinal, mixed-methods study of stability in values and preferences for future end-of-life care among community-dwelling, older adults

description: Additional file 1. Overview of participants’ card choices.
DOI: 10.6084/m9.figshare.16696450.v1
title: Additional file 1 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 1: Table S1. Foraminiferal census data of the living fauna, Japsand, German Wadden Sea.
DOI: 10.6084/m9.figshare.16696453
title: Additional file 2 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 2: Table S2. Foraminiferal census data of the dead assemblage, Japsand, German Wadden Sea.
DOI: 10.6084/m9.figshare.16696450
title: Additional file 1 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 1: Table S1. Foraminiferal census data of the living fauna, Japsand, German Wadden Sea.
DOI: 10.6084/m9.figshare.16696456
title: Additional file 3 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 3: Table S3. Biometric measurements of the most important foraminiferal species from the living fauna, Japsand, German Wadden Sea.
DOI: 10.6084/m9.figshare.16696453.v1
title: Additional file 2 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 2: Table S2. Foraminiferal census data of the dead assemblage, Japsand, German Wadden Sea.
DOI: 10.6084/m9.figshare.16696459.v1
title: Additional file 4 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 4: Table S4. Biometric measurements of the most important foraminiferal species from the dead assemblage, Japsand, German Wadden Sea.
DOI: 10.6084/m9.figshare.16696456.v1
title: Additional file 3 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 3: Table S3. Biometric measurements of the most important foraminiferal species from the living fauna, Japsand, German Wadden Sea.
DOI: 10.6084/m9.figshare.16696459
title: Additional file 4 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 4: Table S4. Biometric measurements of the most important foraminiferal species from the dead assemblage, Japsand, German Wadden Sea.
DOI: 10.6084/m9.figshare.16696462
title: Additional file 5 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 5: Table S5. Foraminiferal census data of the living fauna from Helgoland inner port.
DOI: 10.6084/m9.figshare.16696462.v1
title: Additional file 5 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 5: Table S5. Foraminiferal census data of the living fauna from Helgoland inner port.
DOI: 10.6084/m9.figshare.16696465
title: Additional file 6 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 6: Table S6. Foraminiferal census data of the living fauna from the Bay of Tümlau, near Westerhever [4] used for the comparison of locations in this study.
DOI: 10.6084/m9.figshare.16696465.v1
title: Additional file 6 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 6: Table S6. Foraminiferal census data of the living fauna from the Bay of Tümlau, near Westerhever [4] used for the comparison of locations in this study.
DOI: 10.6084/m9.figshare.16696468.v1
title: Additional file 7 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 7: Table S7. Foraminiferal census data of the living fauna from Schobüll.
DOI: 10.6084/m9.figshare.16696468
title: Additional file 7 of Living and dead foraminiferal assemblage from the supratidal sand Japsand, North Frisian Wadden Sea: distributional patterns and controlling factors

description: Additional file 7: Table S7. Foraminiferal census data of the living fauna from Schobüll.
DOI: 10.6084/m9.figshare.16696498
title: Additional file 2 of Investigating the DNA methylation profile of e-cigarette use

description: Additional file 2. Supplementary tables.
DOI: 10.6084/m9.figshare.16696498.v1
title: Additional file 2 of Investigating the DNA methylation profile of e-cigarette use

description: Additional file 2. Supplementary tables.
DOI: 10.6084/m9.figshare.16696440
title: Additional file 10 of Single-cell RNA sequencing reveals markers of disease progression in primary cutaneous T-cell lymphoma

description: Additional file 10: Table S9.
DOI: 10.6084/m9.figshare.16696440.v1
title: Additional file 10 of Single-cell RNA sequencing reveals markers of disease progression in primary cutaneous T-cell lymphoma

description: Additional file 10: Table S9.
DOI: 10.6084/m9.figshare.16696443
title: Additional file 11 of Single-cell RNA sequencing reveals markers of disease progression in primary cutaneous T-cell lymphoma

description: Additional file 11: Table S10.
DOI: 10.6084/m9.figshare.16696443.v1
title: Additional file 11 of Single-cell RNA sequencing reveals markers of disease progression in primary cutaneous T-cell lymphoma

description: Additional file 11: Table S10.
DOI: 10.6084/m9.figshare.16696446.v1
title: Additional file 12 of Single-cell RNA sequencing reveals markers of disease progression in primary cutaneous T-cell lymphoma

description: Additional file 12: Table S11.
DOI: 10.6084/m9.figshare.16696446
title: Additional file 12 of Single-cell RNA sequencing reveals markers of disease progression in primary cutaneous T-cell lymphoma

description: Additional file 12: Table S11.