cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/7243e2e61bc65173689e2aa6244c1823
title: Snowmelt periods as hot moments for soil N dynamics: A case study in Maine, USA

description: This is the data archive for the corresponding publication with the same title (doi: 0.1007/s10661-020-08733-0). The vernal transition represents the seasonal transition to spring, occurring as temperatures rise at the end of winter. With rapid snowmelt, microbial community turnover, and accelerated nutrient cycling, this is a critical but relatively under-studied period of ecosystem function. We conducted a study over two consecutive winters (2015-2016) at the Bear Brook Watershed in Maine to examine how changing winter conditions (warming winters, reduced snow accumulation) altered soil nitrogen availability and stream N export during winter and the vernal transition, and how these patterns were influenced by ecosystem N status (N-enriched vs. N-limited). Of the two study years, 2016 had a warmer winter with substantially less snow accumulation and a discontinuous snowpack — and as a result, had a longer vernal transition and a snowpack that thawed before the vernal transition began. Across both years, snowmelt triggered a transition, signaled by increased ammonium concentrations in soil, decreased soil nitrate concentrations due to flushing by meltwater, and increased stream nitrate exports. Despite the contrasting winter conditions, both years showed similar patterns in N availability and export, differing only in the timing of these transitions. The vernal transition has conventionally been considered a critical period for biogeochemical cycling, because the associated snowmelt event triggers physicochemical and biochemical changes in soil systems. This was consistent with our results in 2015, but our data for 2016 show that this may not always hold true, and instead, that warmer, low-snow winters may demonstrate a temporal asynchrony between snowmelt and the vernal transition. We also show that ecosystem N status is a strong driver of the seasonal N pattern, and the interaction of N status and changing climate must be further investigated to understand ecosystem function under our current predicted trajectory of warming winters, declining snowfall, and winter thaw events.
DOI: 10.5281/zenodo.8007358
title: MAST melody dataset

description: The Musical Aptitude Standard Test <strong>(MAST) melody dataset </strong>is designed and shared to facilitate comparison of algorithms in the field of automatic music performance assessment. The dataset includes melodic pattern reproduction performances by students (singing) together with the reference melodic pattern played on piano and assessment results. All recordings are collected during entrance exams (in 2015 and 2016) of Istanbul Technical University (ITU). The recordings are annotations in 2022 within the context of another research project supported by TUBITAK with grant number 121E198 as a part of the Scientific and Technological Research Projects Funding Program (1001). Annotations are performed via blind listening of individual performances after listening to a few renditions of the melodic pattern by the experts. The files were presented in random order (after grouping samples in terms of melodic patterns) (i.e. the expert annotated all samples of a melodic pattern in random order and moved to the next group of samples for the next melodic pattern). A 4-level grading system was used during the evaluations of the data set; <br> 1-Completely Off, 2-Major Mistakes, 3-Minor Mistakes, and 4-Perfect. Annotations were carried by 3 experts; a professor of musicology who has taken part as a jury member in entrance <br> exam auditions, and two music conservatory students of graduate-level programs. The last two annotators re-annotated<br> all collection a few months after the first annotation task. The csv file contains 5 annotations in 5 columns where<br> two of these columns are for the repeated annotations. To facilitate analysis, we also added columns that carry a flag<br> if all annotations match (column: 'fullAgree'), the score/grade all annotations agreed on (column: 'fullAgree_score') <br> and the majority score. In addition to audio files and annotations, two commonly used features are also included: 1) f0-series extracted using Crepe Pitch Tracker (https://github.com/marl/crepe), 2) chroma features computed using Librosa library's chroma_stft function (https://librosa.org/doc/main/generated/librosa.feature.chroma_stft.html) The directory structure is: annotations: Contains 5 distinct annotations by 3 experts in a scale 1-4 in a csv file. f0data_crepe: MASTmelody dataset, latest version (f0 series extracted using Crepe Pitch Tracker) audioFiles: Audio files sampled at 8kHz chroma: chroma features computed using Librosa library's chroma_stft function using its default settings except n_chroma which is set to 24. If you use this dataset, please refer to the following paper which announced its original version: Bozkurt, B., Baysal, O., Yuret, D. A Dataset and Baseline System for Singing Voice Assessment, 13th Int. Symposium on Computer Music Multidisciplinary Research, Porto, Sept. 25-28, 2017. <pre><code>@inproceedings{inproceedings, author={Bozkurt, B., Baysal, O., Yuret, D.}, title={A Dataset and Baseline System for Singing Voice Assessment}, year={2017}, booktitle={13th Int. Symposium on Computer Music Multidisciplinary Research, CMMR 2017} }</code></pre>
DOI: 10.5281/zenodo.8007357
title: MAST melody dataset

description: The Musical Aptitude Standard Test <strong>(MAST) melody dataset </strong>is designed and shared to facilitate comparison of algorithms in the field of automatic music performance assessment. The dataset includes melodic pattern reproduction performances by students (singing) together with the reference melodic pattern played on piano and assessment results. All recordings are collected during entrance exams (in 2015 and 2016) of Istanbul Technical University (ITU). The recordings are annotations in 2022 within the context of another research project supported by TUBITAK with grant number 121E198 as a part of the Scientific and Technological Research Projects Funding Program (1001). Annotations are performed via blind listening of individual performances after listening to a few renditions of the melodic pattern by the experts. The files were presented in random order (after grouping samples in terms of melodic patterns) (i.e. the expert annotated all samples of a melodic pattern in random order and moved to the next group of samples for the next melodic pattern). A 4-level grading system was used during the evaluations of the data set; <br> 1-Completely Off, 2-Major Mistakes, 3-Minor Mistakes, and 4-Perfect. Annotations were carried by 3 experts; a professor of musicology who has taken part as a jury member in entrance <br> exam auditions, and two music conservatory students of graduate-level programs. The last two annotators re-annotated<br> all collection a few months after the first annotation task. The csv file contains 5 annotations in 5 columns where<br> two of these columns are for the repeated annotations. To facilitate analysis, we also added columns that carry a flag<br> if all annotations match (column: 'fullAgree'), the score/grade all annotations agreed on (column: 'fullAgree_score') <br> and the majority score. In addition to audio files and annotations, two commonly used features are also included: 1) f0-series extracted using Crepe Pitch Tracker (https://github.com/marl/crepe), 2) chroma features computed using Librosa library's chroma_stft function (https://librosa.org/doc/main/generated/librosa.feature.chroma_stft.html) The directory structure is: annotations: Contains 5 distinct annotations by 3 experts in a scale 1-4 in a csv file. f0data_crepe: MASTmelody dataset, latest version (f0 series extracted using Crepe Pitch Tracker) audioFiles: Audio files sampled at 8kHz chroma: chroma features computed using Librosa library's chroma_stft function using its default settings except n_chroma which is set to 24. If you use this dataset, please refer to the following paper which announced its original version: Bozkurt, B., Baysal, O., Yuret, D. A Dataset and Baseline System for Singing Voice Assessment, 13th Int. Symposium on Computer Music Multidisciplinary Research, Porto, Sept. 25-28, 2017. <pre><code>@inproceedings{inproceedings, author={Bozkurt, B., Baysal, O., Yuret, D.}, title={A Dataset and Baseline System for Singing Voice Assessment}, year={2017}, booktitle={13th Int. Symposium on Computer Music Multidisciplinary Research, CMMR 2017} }</code></pre>
DOI: 10.6084/m9.figshare.23297645
title: Table

description: The number of missing data in the heart failure diagnosis of both COVID-19 and control groups.
DOI: 10.6084/m9.figshare.23297645.v5
title: Table

description: The number of missing data in the heart failure diagnosis of both COVID-19 and control groups.
DOI: 10.6084/m9.figshare.23297645.v4
title: Table

description: The number of missing data in the heart failure diagnosis of both COVID-19 and control groups.
DOI: 10.5281/zenodo.8002215
title: TMMI&DO-178C Mapping

description: TMMI&DO-178C Mapping document
DOI: 10.5281/zenodo.8002214
title: TMMI&DO-178C Mapping

description: TMMI&DO-178C Mapping document
DOI: 10.5281/zenodo.7967930
title: Characterization of mAMCase sugar-binding subsites

description: This directory contains all files required to characterize sugar-binding subsites across mouse AMCase structure 8FR9, 8FRA, and 8GCA as presented in <strong>Figure 2</strong> of Díaz et al.<em> </em>(2023). Structure models were analyzed in PyMOL. Figures were compiled using Adobe Illustrator. Files included in this directory: <strong>Figures</strong> - contains PNGs of all sugar-binding subsites, canonical subsites, and noncanonical subsites with and without 2fo-fc electron density displayed. <strong>PyMOL</strong> - contains all structural models, 2mFo-DFc maps, mFo-DFc maps, PyMOL script, and PyMOL session used to generate <strong>Figure 2</strong>. Contact:<br> Roberto Efraín Díaz, robertoefrain.diaz@ucsf.edu James Fraser, jfraser@fraserlab.com
description: {"references": ["D\u00edaz, Roberto Efra\u00edn et al. (2023). Structural characterization of ligand binding and pH-specific enzymatic activity of mouse Acidic Mammalian Chitinase."]}
DOI: 10.5281/zenodo.7967929
title: Characterization of mAMCase sugar-binding subsites

description: This directory contains all files required to characterize sugar-binding subsites across mouse AMCase structure 8FR9, 8FRA, and 8GCA as presented in <strong>Figure 2</strong> of Díaz et al.<em> </em>(2023). Structure models were analyzed in PyMOL. Figures were compiled using Adobe Illustrator. Files included in this directory: <strong>Figures</strong> - contains PNGs of all sugar-binding subsites, canonical subsites, and noncanonical subsites with and without 2fo-fc electron density displayed. <strong>PyMOL</strong> - contains all structural models, 2mFo-DFc maps, mFo-DFc maps, PyMOL script, and PyMOL session used to generate <strong>Figure 2</strong>. Contact:<br> Roberto Efraín Díaz, robertoefrain.diaz@ucsf.edu James Fraser, jfraser@fraserlab.com
description: {"references": ["D\u00edaz, Roberto Efra\u00edn et al. (2023). Structural characterization of ligand binding and pH-specific enzymatic activity of mouse Acidic Mammalian Chitinase."]}
DOI: 10.6073/pasta/9dda244e95ce8dfcdbd9b94d2289ef1d
title: McMurdo Dry Valleys Friis Hills Meteorological Station Daily Averages

description: As part of the Long Term Ecological Research in the McMurdo Dry Valleys of Antarctica, meteorological data was collected from various locations throughout Beacon, Taylor, Wright and Victoria Valleys. These files contain daily averages derived from the high resolution sensor data for the Friis Hills meteorology station. The station captured data first on the 2006/07 season, installed by Cuffey and others, and brought to LTER format by THomas Nylen on 2010/11 season.
DOI: 10.6084/m9.figshare.22114373
title: Experimentation Data: Finding Short Slow Inputs Faster with Grammar-Based Search

description: Experiments data from SlackLine, TreeLine and PerfFuzz as seen in the ISSTA'23 paper. Data is over 7GB when uncompressed.
DOI: 10.6084/m9.figshare.22114373.v1
title: Experimentation Data: Finding Short Slow Inputs Faster with Grammar-Based Search

description: Experiments data from SlackLine, TreeLine and PerfFuzz as seen in the ISSTA'23 paper. Data is over 7GB when uncompressed.
DOI: 10.15468/dl.wp9jqa
title: Occurrence Download

description: A dataset containing 155 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Hippophae rhamnoides L."
 ]
}

 The dataset includes 155 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106202-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4c2mrj
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Continent is Asia",
 "Country is China",
 "Geometry POLYGON((102 30,114 30,114 36,102 36,102 30))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Mustela altaica Pallas, 1811"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106203-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xqvgsr
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Continent is Asia",
 "Country is China",
 "Geometry POLYGON((102 30,114 30,114 36,102 36,102 30))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Martes flavigula (Boddaert, 1785)"
 ]
}

 The dataset includes 7 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106204-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ru59ep
title: Occurrence Download

description: A dataset containing 4420 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (SWE.16_1, GBR.1.59_1, FIN.5.3.4_1, NOR.3.2_1, EST.7.1_1, RUS.14_1)",
 "TaxonKey is Pelophylax lessonae (Camerano, 1882)"
 ]
}

 The dataset includes 4420 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106205-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f3hwx4
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Continent is Asia",
 "Country is China",
 "Geometry POLYGON((102 30,114 30,114 36,102 36,102 30))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Meles leucurus (Hodgson, 1847)"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106209-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v4c2q7
title: Occurrence Download

description: A dataset containing 5455 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Hevea brasiliensis (Willd. ex A.Juss.) Müll.Arg., Hevea granthamii Bartlett, Hevea janeirensis Müll.Arg., Hevea randiana Huber, Hevea sieberi Warb., Siphonia brasiliensis Willd. ex A.Juss., Siphonia janeirensis (Müll.Arg.) O.F.Cook, Siphonia ridleyana O.F.Cook)"
 ]
}

 The dataset includes 5455 records from 324 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106208-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ktw43m
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation)",
 "Continent is Asia",
 "Country is China",
 "Geometry POLYGON((102 30,114 30,114 36,102 36,102 30))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Otocolobus manul (Pallas, 1776)"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106210-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13148965
title: Supplementary Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists

description: This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It replicates the main analysis in the article Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists and accompanies the main research data set (https://doi.org/10.5281/zenodo.10878070).

In this replication study, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to pulse density variation and how uncertainties and biases propagate to commonly used forest structure metrics.

The main data source for this study are ALS point clouds from nine U.S. sites, acquired by the National Ecological Observatory Network (NEON, 6 sites, 3 km x 3 km) and by the United States Geological Survey's 3DEP program (3 sites, also 3 km x 3 km). The underlying data can be found here: https://data.neonscience.org/data-products/DP3.30024.001 (NEON) and here: https://apps.nationalmap.gov/lidar-explorer (3DEP)

The different data layers are:

replicate.US.R   

   is a single R script that contains all the code necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data as well as the overall robustness analysis. To replicate the processing of the raw point clouds step by step, this script should be located in a folder called "rscripts".

pointclouds_original.zip

   is the set of original point clouds (3 km x 3 km in extent) used for the replication test, separated into 9 subfolders/sites. Can be used to reproduce the original workflow by placing them in a folder called "data/original". The script will then automatically produce derived point clouds at pulse densities of 2 and 16 per squaremetre and process them into digital terrain models (DTMs), digital surface models (DSMs) and CHMs. Note that, for convienence, these derived products are also included in a separate .zip file (cf. below).

processed_foranalysis.zip

   is the set of derived products (DTMs, DSMs, CHMs), separated into 9 subfolders/sites, i.e. the result of processing the original point clouds. To use these layers directly with the provided script, they should be put into a folder called "processed_foranalysis".

summaries.zip

   is a set of summary statistics (as .csv files) that were used to generate the main analysis tables in the replication study. To use these summary statistics directly with the provided script, they should be put into a folder called "summaries".

figures.zip

   is a set of figures displayed in the Supplementary Material of the paper Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists.
DOI: 10.5281/zenodo.13148966
title: Supplementary Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists

description: This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It replicates the main analysis in the article Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists and accompanies the main research data set (https://doi.org/10.5281/zenodo.10878070).

In this replication study, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to pulse density variation and how uncertainties and biases propagate to commonly used forest structure metrics.

The main data source for this study are ALS point clouds from nine U.S. sites, acquired by the National Ecological Observatory Network (NEON, 6 sites, 3 km x 3 km) and by the United States Geological Survey's 3DEP program (3 sites, also 3 km x 3 km). The underlying data can be found here: https://data.neonscience.org/data-products/DP3.30024.001 (NEON) and here: https://apps.nationalmap.gov/lidar-explorer (3DEP)

The different data layers are:

replicate.US.R   

   is a single R script that contains all the code necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data as well as the overall robustness analysis. To replicate the processing of the raw point clouds step by step, this script should be located in a folder called "rscripts".

pointclouds_original.zip

   is the set of original point clouds (3 km x 3 km in extent) used for the replication test, separated into 9 subfolders/sites. Can be used to reproduce the original workflow by placing them in a folder called "data/original". The script will then automatically produce derived point clouds at pulse densities of 2 and 16 per squaremetre and process them into digital terrain models (DTMs), digital surface models (DSMs) and CHMs. Note that, for convienence, these derived products are also included in a separate .zip file (cf. below).

processed_foranalysis.zip

   is the set of derived products (DTMs, DSMs, CHMs), separated into 9 subfolders/sites, i.e. the result of processing the original point clouds. To use these layers directly with the provided script, they should be put into a folder called "processed_foranalysis".

summaries.zip

   is a set of summary statistics (as .csv files) that were used to generate the main analysis tables in the replication study. To use these summary statistics directly with the provided script, they should be put into a folder called "summaries".

figures.zip

   is a set of figures displayed in the Supplementary Material of the paper Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists.
DOI: 10.15468/dl.ahp6dg
title: Occurrence Download

description: A dataset containing 210 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Ecuador",
 "TaxonKey is Inga spectabilis (Vahl) Willd."
 ]
}

 The dataset includes 210 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0106211-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26862373
title: Proteomics analysis of human pulmonary microvasculature endothelial cell-derived extracellular vesicles and soluble factors

description: Proteomics analysis of human pulmonary microvasculature endothelial cell-derived extracellular vesicles and soluble factors