cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11638365
title: DataMaster_and_Metadata.xlsx

description: Antelope Pasture, located in the Black Pine Valley of Oneida County, Idaho, is a 1500-acre portion of the Curlew Grazing Allotment, managed by the Pocatello Field Office of the BLM. The pasture is heavily invaded with exotic annuals and grazing is temporarily discontinued. The Pocatello BLM Office partnered with Utah State University in 2017 to provide detailed soil and vegetation maps of Antelope Pasture, and determine plant-soil relationships. A survey of the soil and vegetation of Antelope Pasture was conducted in the summer of 2017. Vegetation sampling took place in May and early June, and soil sampling in June and July. We found a total of 42 plant species at Antelope Pasture, 26 of which are native to the region. However, species of the highest cover and frequency were introduced annuals such as Bromus tectorum (cheatgrass), Lepidium perfoliatum (clasping pepperweed), Descurainia pinnata (flixweed) and Ranunculus testiculatus (burr buttercup), as well as the seeded perennial Agropyron cristatum (crested wheatgrass). The soils of Antelope pasture are primarily characterized by translocated accumulations of calcium carbonate and exchangeable sodium in the subsurface horizons, and the primary difference among pedons was the presence or absence of natric horizons. The majority of soils were classified into the Xeric Natrargids subgroup and were similar in morphology to the Mellor soil series. We concluded that the strongest driving factor in relationships between pasture soils and vegetation identified through analyses was the presence or absence of a biological soil crust or moss cover. Because so few relationships were found between vegetation and soil variables, other factors, such as disturbance history, are more likely to be the central cause of variation in vegetation across the pasture. See "Soil and Vegetation Survey of Antelope Pasture, Curlew Grazing Allotment, Oneida County, ID" Report for full methodology: https://works.bepress.com/kari_veblen/75/
DOI: 10.6084/m9.figshare.11638365.v1
title: DataMaster_and_Metadata.xlsx

description: Antelope Pasture, located in the Black Pine Valley of Oneida County, Idaho, is a 1500-acre portion of the Curlew Grazing Allotment, managed by the Pocatello Field Office of the BLM. The pasture is heavily invaded with exotic annuals and grazing is temporarily discontinued. The Pocatello BLM Office partnered with Utah State University in 2017 to provide detailed soil and vegetation maps of Antelope Pasture, and determine plant-soil relationships. A survey of the soil and vegetation of Antelope Pasture was conducted in the summer of 2017. Vegetation sampling took place in May and early June, and soil sampling in June and July. We found a total of 42 plant species at Antelope Pasture, 26 of which are native to the region. However, species of the highest cover and frequency were introduced annuals such as Bromus tectorum (cheatgrass), Lepidium perfoliatum (clasping pepperweed), Descurainia pinnata (flixweed) and Ranunculus testiculatus (burr buttercup), as well as the seeded perennial Agropyron cristatum (crested wheatgrass). The soils of Antelope pasture are primarily characterized by translocated accumulations of calcium carbonate and exchangeable sodium in the subsurface horizons, and the primary difference among pedons was the presence or absence of natric horizons. The majority of soils were classified into the Xeric Natrargids subgroup and were similar in morphology to the Mellor soil series. We concluded that the strongest driving factor in relationships between pasture soils and vegetation identified through analyses was the presence or absence of a biological soil crust or moss cover. Because so few relationships were found between vegetation and soil variables, other factors, such as disturbance history, are more likely to be the central cause of variation in vegetation across the pasture. See "Soil and Vegetation Survey of Antelope Pasture, Curlew Grazing Allotment, Oneida County, ID" Report for full methodology: https://works.bepress.com/kari_veblen/75/
DOI: 10.5522/04/11638275
title: Evaluating Saliency Map Explanations for CNN

description: Participants' answers to the following study: Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study.
DOI: 10.5522/04/11638275.v1
title: Evaluating Saliency Map Explanations for CNN

description: Participants answers to the following study: Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study.
DOI: 10.5522/04/11638275.v2
title: Evaluating Saliency Map Explanations for CNN

description: Participants' answers to the following study: Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study.
DOI: 10.6084/m9.figshare.11638008
title: Data.Li_et_al.zip

description: The dataset for the paper Li et al. "Polymicrobial Periodontal Disease Triggers a Wide Radius of Effect and Unique Virome"
DOI: 10.6084/m9.figshare.11638008.v1
title: Data.Li_et_al.zip

description: The dataset for the paper Li et al. "Polymicrobial Periodontal Disease Triggers a Wide Radius of Effect and Unique Virome"
DOI: 10.6084/m9.figshare.11637786
title: Metadata record for: The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries

description: This dataset contains key characteristics about the data described in the Data Descriptor The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.<br>
DOI: 10.6084/m9.figshare.11637786.v2
title: Metadata record for: The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries

description: This dataset contains key characteristics about the data described in the Data Descriptor The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> Versioning Note:Version 2 was generated when the metadata format was updated from JSON to JSON-LD. This was an automatic process that changed only the format, not the contents, of the metadata.<br>
DOI: 10.6084/m9.figshare.11637786.v1
title: Metadata record for: The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries

description: This dataset contains key characteristics about the data described in the Data Descriptor The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> <br>
DOI: 10.6084/m9.figshare.11637729
title: Skills in Software Testing

description: This is the archived Online Appendix for "Beyond Technical Skills in Software Testing: Automated versus Manual Testing". In 13th International Workshop on Cooperative and Human Aspects of Software Engineering(CHASE 2020).<br>This exploratory study aims to identify what are the most valued skills with regards to manual and automated testing. To do so, a survey was applied among software practitioners and 72 responses were received. The questionnaire covers 35 skills grouped in technical (hard) and non-technical (soft) skills. The results provide empirical evidence that reveals the importance that software practitioners give to hard and soft skills alike.<br><br>The survey was designed, carried out, and analyzed by Mary Sánchez-Gordón, Laxmi Rijal, and Ricardo Colomo-Palacios.<br><br>The present file contains:<br>Questionnaire SurveyRaw DataStatistical Analysis<br><br>
DOI: 10.6084/m9.figshare.11637729.v1
title: Skills in Software Testing

description: The csv table with the data of the survey (72 point data)<br>Questionnaire SurveyRaw Data<br>Statistical Analysis<br><br>
DOI: 10.25377/sussex.11637702
title: Dataset for paper "Anticipating causes and consequences" - Experiment 2 Early Data with Differences

description: <b>Data for paper appearing in Journal of Memory and Language 2020</b><br><br>csv file for import into R as data frame<br>for the analysis:<br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 200-1100ms from the beginning of the conjunction, "because" or "and so")<br>Independent Variables<br>Vbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"<br>Sources of random effects<br>Part - participantsItem - items<br>DV is numerical, other variables are factors<br>The file includes other variables
DOI: 10.25377/sussex.11637705
title: Dataset for paper "Anticipating causes and consequences" - Experiment 2 Late Effect Data with Differences

description: <b>Data for paper appearing in Journal of Memory and Language, 2020</b><br><br>csv file for import into R as data frame<br>for the analysis:<br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 6720-10000ms from the beginning of the display of the picture)<br>Independent Variables<br>Vbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"<br>Sources of random effects<br>Part - participantsItem - items<br>DV is numerical, other variables are factors<br>The file includes other variables
DOI: 10.6084/m9.figshare.11637729.v2
title: Skills in Software Testing

description: The csv table with the data of the survey (72 point data)<br>Questionnaire SurveyRaw Data<br>Statistical Analysis<br><br>
DOI: 10.25377/sussex.11637702.v2
title: Dataset for paper "Anticipating causes and consequences" - Experiment 2 Early Data with Differences

description: <b>Data for paper appearing in Journal of Memory and Language 2020</b><br><br>csv file for import into R as data frame<br>for the analysis:<br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 200-1100ms from the beginning of the conjunction, "because" or "and so")<br>Independent Variables<br>Vbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"<br>Sources of random effects<br>Part - participantsItem - items<br>DV is numerical, other variables are factors<br>The file includes other variables
DOI: 10.25377/sussex.11637705.v2
title: Dataset for paper "Anticipating causes and consequences" - Experiment 2 Late Effect Data with Differences

description: <b>Data for paper appearing in Journal of Memory and Language, 2020</b><br><br>csv file for import into R as data frame<br>for the analysis:<br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 6720-10000ms from the beginning of the display of the picture)<br>Independent Variables<br>Vbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"<br>Sources of random effects<br>Part - participantsItem - items<br>DV is numerical, other variables are factors<br>The file includes other variables
DOI: 10.25377/sussex.11637702.v1
title: Dataset for paper "Anticipating causes and consequences" currently under review - Experiment 2 Early Data with Differences

description: csv file for import into R as data frame<br>for the analysis:<br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 200-1100ms from the beginning of the conjunction, "because" or "and so")<br>Independent Variables<br>Vbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"<br>Sources of random effects<br>Part - participantsItem - items<br>DV is numerical, other variables are factors<br>The file includes other variables
DOI: 10.25377/sussex.11637705.v1
title: Dataset for paper "Anticipating causes and consequences" currently under review - Experiment 2 Late Effect Data with Differences

description: csv file for import into R as data frame<br>for the analysis:<br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 6720-10000ms from the beginning of the display of the picture)<br>Independent Variables<br>Vbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"<br>Sources of random effects<br>Part - participantsItem - items<br>DV is numerical, other variables are factors<br>The file includes other variables
DOI: 10.25377/sussex.11637678
title: Dataset for paper "Anticipating causes and consequences" - Experiment 2 Very Early Data with Differences

description: <b>Data for paper appearing in Journal of Memory and Language 2020</b><br><br>csv file for import into R as data frame<br>for the analysis:<br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 200-1400ms from the beginning of the padding phrase)<br>Independent Variables<br>Vbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"<br>Sources of random effects<br>Part - participantsItem - items<br>DV is numerical, other variables are factors<br>The file includes other variables
DOI: 10.6084/m9.figshare.11637675
title: Atomic force microscopy indentation data of stiff and compliant polyacrylamide hydrogels

description: <b><br>Atomic force microscopy indentation data of stiff and compliant polyacrylamide hydrogels</b><br><br>This dataset consists of 110 force-distance curves of two (stiff and compliant) polyacrylamide (PAAm) gels recorded using atomic force microscopy. Each file represents one measurement. The file names indicate whether a measurement was performed on the stiff or on the compliant gel. For each gel, curves were recorded from six regions of interest (ROIs).<br><br>The data were recorded using a JPK NanoWizard 4 and a PNP-TR-TL cantilever (Nanoworld) with a spring constant of 0.08 N/m. For probing, a bead with a radius of 5 µm was attached to the cantilever. The cantilever was calibrated using the thermal noise method in phosphate buffered saline (PBS) at room temperature.<br><br>The hydrogels were produced according to the protocol described in [1]. The gels have an expected Young's modulus of 0.8 to 1.2 kPa (compliant) and 25 to 28 kPa (stiff).<br><br><br><b>References</b><br>[1] G. Rosso et al., Schwann cells and neurite outgrowth from embryonic dorsal root ganglions are highly mechanosensitive, Nanomedicine: Nanotechnology, Biology and Medicine, 2016, doi:10.1016/j.nano.2016.06.011<br><br>
DOI: 10.25377/sussex.11637678.v2
title: Dataset for paper "Anticipating causes and consequences" - Experiment 2 Very Early Data with Differences

description: <b>Data for paper appearing in Journal of Memory and Language 2020</b><br><br>csv file for import into R as data frame<br>for the analysis:<br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 200-1400ms from the beginning of the padding phrase)<br>Independent Variables<br>Vbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"<br>Sources of random effects<br>Part - participantsItem - items<br>DV is numerical, other variables are factors<br>The file includes other variables
DOI: 10.25377/sussex.11637678.v1
title: Dataset for paper "Anticipating causes and consequences" currently under review - Experiment 2 Very Early Data with Differences

description: csv file for import into R as data frame<br>for the analysis:<br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 200-1400ms from the beginning of the padding phrase)<br>Independent Variables<br>Vbias - Causal bias of the verb (note that consequentiality bias is to the other NP)Conj - "because" or "and so"<br>Sources of random effects<br>Part - participantsItem - items<br>DV is numerical, other variables are factors<br>The file includes other variables
DOI: 10.6084/m9.figshare.11637675.v1
title: Atomic force microscopy indentation data of stiff and compliant polyacrylamide hydrogels

description: <b><br>Atomic force microscopy indentation data of stiff and compliant polyacrylamide hydrogels</b><br><br>This dataset consists of 110 force-distance curves of two (stiff and compliant) polyacrylamide (PAAm) gels recorded using atomic force microscopy. Each file represents one measurement. The file names indicate whether a measurement was performed on the stiff or on the compliant gel. For each gel, curves were recorded from six regions of interest (ROIs).<br><br>The data were recorded using a JPK NanoWizard 4 and a PNP-TR-TL cantilever (Nanoworld) with a spring constant of 0.08 N/m. For probing, a bead with a radius of 10 µm was attached to the cantilever. The cantilever was calibrated using the thermal noise method in phosphate buffered saline (PBS) at room temperature.<br><br>The hydrogels were produced according to the protocol described in [1]. The gels have an expected Young's modulus of 0.8 to 1.2 kPa (soft) and 25 to 28 kPa (stiff).<br><br><br><b>References</b><br>[1] G. Rosso et al., Schwann cells and neurite outgrowth from embryonic dorsal root ganglions are highly mechanosensitive, Nanomedicine: Nanotechnology, Biology and Medicine, 2016, doi:10.1016/j.nano.2016.06.011<br><br>
DOI: 10.6084/m9.figshare.11637675.v3
title: Atomic force microscopy indentation data of stiff and compliant polyacrylamide hydrogels

description: <b><br>Atomic force microscopy indentation data of stiff and compliant polyacrylamide hydrogels</b><br><br>This dataset consists of 110 force-distance curves of two (stiff and compliant) polyacrylamide (PAAm) gels recorded using atomic force microscopy. Each file represents one measurement. The file names indicate whether a measurement was performed on the stiff or on the compliant gel. For each gel, curves were recorded from six regions of interest (ROIs).<br><br>The data were recorded using a JPK NanoWizard 4 and a PNP-TR-TL cantilever (Nanoworld) with a spring constant of 0.08 N/m. For probing, a bead with a radius of 5 µm was attached to the cantilever. The cantilever was calibrated using the thermal noise method in phosphate buffered saline (PBS) at room temperature.<br><br>The hydrogels were produced according to the protocol described in [1]. The gels have an expected Young's modulus of 0.8 to 1.2 kPa (compliant) and 25 to 28 kPa (stiff).<br><br><br><b>References</b><br>[1] G. Rosso et al., Schwann cells and neurite outgrowth from embryonic dorsal root ganglions are highly mechanosensitive, Nanomedicine: Nanotechnology, Biology and Medicine, 2016, doi:10.1016/j.nano.2016.06.011<br><br>
DOI: 10.6084/m9.figshare.11637675.v2
title: Atomic force microscopy indentation data of stiff and compliant polyacrylamide hydrogels

description: <b><br>Atomic force microscopy indentation data of stiff and compliant polyacrylamide hydrogels</b><br><br>This dataset consists of 110 force-distance curves of two (stiff and compliant) polyacrylamide (PAAm) gels recorded using atomic force microscopy. Each file represents one measurement. The file names indicate whether a measurement was performed on the stiff or on the compliant gel. For each gel, curves were recorded from six regions of interest (ROIs).<br><br>The data were recorded using a JPK NanoWizard 4 and a PNP-TR-TL cantilever (Nanoworld) with a spring constant of 0.08 N/m. For probing, a bead with a radius of 10 µm was attached to the cantilever. The cantilever was calibrated using the thermal noise method in phosphate buffered saline (PBS) at room temperature.<br><br>The hydrogels were produced according to the protocol described in [1]. The gels have an expected Young's modulus of 0.8 to 1.2 kPa (soft) and 25 to 28 kPa (stiff).<br><br><br><b>References</b><br>[1] G. Rosso et al., Schwann cells and neurite outgrowth from embryonic dorsal root ganglions are highly mechanosensitive, Nanomedicine: Nanotechnology, Biology and Medicine, 2016, doi:10.1016/j.nano.2016.06.011<br><br>
DOI: 10.6084/m9.figshare.11637573
title: Supplementary B. <sup>40</sup>Ar/<sup>39</sup>Ar mica dating of late Cenozoic sediments in SE Tibet: Implications for sediment recycling and drainage evolution

description: Supplementary B: Biotite <sup>40</sup>Ar/<sup>39</sup>Ar data
DOI: 10.6084/m9.figshare.11637570
title: Appendix C. <sup>40</sup>Ar/<sup>39</sup>Ar mica dating of late Cenozoic sediments in SE Tibet: Implications for sediment recycling and drainage evolution

description: Appendix C. Muscovite geochemistry
DOI: 10.6084/m9.figshare.11637573.v1
title: Supplementary B. <sup>40</sup>Ar/<sup>39</sup>Ar mica dating of late Cenozoic sediments in SE Tibet: Implications for sediment recycling and drainage evolution

description: Supplementary B: Biotite <sup>40</sup>Ar/<sup>39</sup>Ar data
DOI: 10.6084/m9.figshare.11637567
title: Supplementary A. <sup>40</sup>Ar/<sup>39</sup>Ar mica dating of late Cenozoic sediments in SE Tibet: Implications for sediment recycling and drainage evolution

description: Supplementary A: Muscovite <sup>40</sup>Ar/<sup>39</sup>Ar data
DOI: 10.6084/m9.figshare.11637567.v1
title: Supplementary A. <sup>40</sup>Ar/<sup>39</sup>Ar mica dating of late Cenozoic sediments in SE Tibet: Implications for sediment recycling and drainage evolution

description: Supplementary A: Muscovite <sup>40</sup>Ar/<sup>39</sup>Ar data
DOI: 10.6084/m9.figshare.11637570.v1
title: Appendix C. <sup>40</sup>Ar/<sup>39</sup>Ar mica dating of late Cenozoic sediments in SE Tibet: Implications for sediment recycling and drainage evolution

description: Appendix C. Muscovite geochemistry
DOI: 10.25377/sussex.11637489
title: Dataset for paper "Anticipating causes and consequences" - Experiment 1 Late Effect Data with Differences

description: <b>Data for paper appearing in Journal of Memory and Language 2020</b><br><br>csv file for import into R as data frame<br>for the analysis:<br><br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 6720ms into display until the end of the display - 10sec after initial presentation)<br>Independent Variable<br>VBias - causal bias of verb (note that consequentiality bias is to the other NP)<br><br>Sources of random effects<br>Part - participantsItem - items<br>IV is numerical, other variables are factors<br>Other variables are included in the file.
DOI: 10.25377/sussex.11637489.v2
title: Dataset for paper "Anticipating causes and consequences" - Experiment 1 Late Effect Data with Differences

description: <b>Data for paper appearing in Journal of Memory and Language 2020</b><br><br>csv file for import into R as data frame<br>for the analysis:<br><br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 6720ms into display until the end of the display - 10sec after initial presentation)<br>Independent Variable<br>VBias - causal bias of verb (note that consequentiality bias is to the other NP)<br><br>Sources of random effects<br>Part - participantsItem - items<br>IV is numerical, other variables are factors<br>Other variables are included in the file.
DOI: 10.25377/sussex.11637489.v1
title: Dataset for paper "Anticipating causes and consequences" currently under review - Experiment 1 Late Effect Data with Differences

description: csv file for import into R as data frame<br>for the analysis:<br><br>Dependent Variable - dplooks - difference in proportion of looks to NP1 and NP2 pictures (averaged across 6720ms into display until the end of the display - 10sec after initial presentation)<br>Independent Variable<br>VBias - causal bias of verb (note that consequentiality bias is to the other NP)<br><br>Sources of random effects<br>Part - participantsItem - items<br>IV is numerical, other variables are factors<br>Other variables are included in the file.