cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5759694
title: Open Soil Spectral Library (training data and calibration models)

description: <strong>Open Soil Spectral Library</strong> contains training MIR (68,257) and VisNIR (67,464) spectral scans + soil calibration data (51,732 unique locations) and calibration models. Key data set: rm.ossl_v1.rds: a global regression-matrix used to fit all calibration models; Important note: The data set spatially over-represents USA and European Union, with little training data in Asia, South America and Australia, hence calibration models reflect primarily soils of USA and Europe. To use the models and data please install R and required packages. Read more about the RDS data format and how to convert it to CSV or similar. Modeling steps are explained in detail in: https://github.com/soilspectroscopy/ossl-models. To visualize database please use: https://explorer.soilspectroscopy.org/ Complete OSSL documentation can be found at: https://soilspectroscopy.github.io/ossl-manual/ <strong>Soil Spectroscopy for the Global Good</strong> is a Coordinated Innovation Network funded by USDA NIFA Food and Agriculture Cyberinformatics Tools Program (Award #2020-67021-32467). Input datasets are property of the USDA NRCS National Soil Survey Center – Kellogg Soil Survey Laboratory, ICRAF-World Agroforestry, ISRIC-World Soil Information, the Africa Soil Information Service funded by the Bill and Melinda Gates Foundation, the European Soil Data Centre, the National Ecological Observatory Network, and ETH Zurich. For more advanced uses of the soil spectral libraries <strong>we advise to contact the original data producers</strong> especially to get help with using, extending and improving the original SSL data.
description: {"references": ["Orgiazzi, Alberto, Cristiano Ballabio, Panagiotis Panagos, Arwyn Jones, and Oihane Fern\u00e1ndez-Ugalde. 2018. \"LUCAS Soil, the largest expandable soil dataset for Europe: a review.\" European Journal of Soil Science 69 (1): 140\u201353. https://doi.org/10.1111/ejss.12499.", "Sanderman, Jonathan, Kathleen Savage, and Shree RS Dangal. 2020. \"Mid-infrared spectroscopy for prediction of soil health indicators in the United States.\" Soil Science Society of America Journal 84 (1): 251\u201361. https://doi.org/10.1002/saj2.20009.", "Summerauer, L., P. Baumann, L. Ramirez-Lopez, M. Barthel, M. Bauters, B. Bukombe, M. Reichenbach, et al. 2021. \"The Central African Soil Spectral Library: A New Soil Infrared Repository and a Geographical Prediction Analysis.\" SOIL 7 (2): 693\u2013715. https://doi.org/10.5194/soil-7-693-2021.", "Vagen, Tor-Gunnar, Leigh Ann Winowiecki, Luseged Desta, Ebagnerin Jerome Tondoh, Elvis Weullow, Keith Shepherd, and Andrew Sila. 2020. Mid-Infrared Spectra (MIRS) from ICRAF Soil and Plant Spectroscopy Laboratory: Africa Soil Information Service (AfSIS) Phase I 2009-2013. World Agroforestry - Research Data Repository. https://doi.org/10.34725/DVN/QXCWP1."]}
DOI: 10.6073/pasta/e8ad9c52753d8e59af302cffa6e83bfe
title: Monthly precipitation data from a network of standard gauges at the Jornada Experimental Range (Jornada Basin LTER) in southern New Mexico, January 1916 - ongoing

description: This ongoing dataset contains monthly precipitation measurements from a network of standard can rain gauges at the Jornada Experimental Range in Dona Ana County, New Mexico, USA. Precipitation physically collects within gauges during the month and is manually measured with a graduated cylinder at the end of each month. This network is maintained by USDA Agricultural Research Service personnel. This dataset includes 39 different locations but only 29 of them are current. Other precipitation data exist for this area, including event-based tipping bucket data with timestamps, but do not go as far back in time as this dataset.
DOI: 10.5281/zenodo.5762433
title: ReaLSAT, a global dataset of reservoir and lake surface area variations

description: Reservoir and Lake Surface Area Timeseries (ReaLSAT) dataset provides an unprecedented reconstruction of surface area variations of lakes and reservoirs at a global scale using Earth Observation (EO) data and novel machine learning techniques. The dataset provides monthly scale surface area variations (1984 to 2015) of 683734 water bodies below 50°N and sizes between 0.1 to 100 square kilometers. The dataset contains the following files: 1) ReaLSAT.zip: A shapefile that contains the reference shape of waterbodies in the dataset. 2) monthly_timeseries.zip: contains one CSV file for each water body. The CSV file provides monthly surface area variation values. The CSV files are stored in a subfolder corresponding to each 10 degree by 10 degree cell. For example, monthly_timeseries_60_-50 folders contain CSV files of lakes that lie between 60 E and 70 E longitude, and 50S and 40 S. 3) monthly_shapes_&lt;bottom_left_lon&gt;_&lt;bottom_left_lat&gt;.zip: contains shapefile for each water body that lie within the 10 degree by 10 degree cell. The shapefile provides monthly shapes together with their time and area attributes. 4) ReaLSAT.html: a readme python notebook that provides information about reading and visualizing the dataset. The notebook also contains the code to download the data to reduce the overhead of downloading each file manually. 5) evaluation_data.zip: contains the random subsets of the dataset used for evaluation. The zip file contains a README file that describes the evaluation data. 6) generate_realsat_timeseries.ipynb: a Google Colab notebook that provides the code to generate timerseries and surface extent maps for any waterbody. Please refer to the following papers to learn more about the processing pipeline used to create ReaLSAT dataset: [1] Khandelwal, Ankush, Rahul Ghosh, Zhihao Wei, Huangying Kuang, Hilary Dugan, Paul Hanson, Anuj Karpatne, and Vipin Kumar. "ReaLSAT: A new Reservoir and Lake Surface Area Timeseries Dataset created using machine learning and satellite imagery." (2020). [2] Khandelwal, Ankush. "ORBIT (Ordering Based Information Transfer): A Physics Guided Machine Learning Framework to Monitor the Dynamics of Water Bodies at a Global Scale." (2019).
DOI: 10.6073/pasta/5dc19f7d3cc4db6fb86a2984fe126f4a
title: Scaling Sarracenia in North America 1900-2100

description: Scaling in Ecology with a Model System synthesizes central theories from ecology, biogeography, and macroecology through the lens of scale and scaling across spatiotemporal extents and grains, levels of biological organization, and with dimensionless ratios. In this book, we link 25 years of detailed natural history observations and laboratory and field experiments on the “Sarracenia microecosystem” (the northern pitcher plant, Sarracenia purpurea, and its associated food web of microbes and macrobes) with new models and analyses to generate novel insights into ecophysiology and stoichiometry; demography and species distribution models; food webs and trophic dynamics; and tipping points and regime shifts. Our work addresses the Sarracenia microecosystem at a hierarchy of spatial scales: individual pitchers within plants, plants within bogs, and bogs within landscapes, all of which can be treated as replicate “island” ecosystems that can be studied throughout the United States east of the Mississippi River and Canada east of the Rocky Mountains. Through integrated studies of its proteomics, physiology, population dynamics, community ecology, and ecosystem processes, the Sarracenia microecosystem has emerged as a model system for experimental ecology. Our synthetic work clearly illustrates that working with the Sarracenia microecosystem can yield new results and understanding of the importance of ecosystem-wide disturbances and anthropogenically-driven environmental and climatic change. They also show that research with the experimentally tractable Sarracenia microecosystem proceeds much more rapidly than studies of larger, more slowly changing ecosystems such as forests, grasslands, lakes, and streams that are more difficult to replicate and experimentally manipulate.
DOI: 10.5281/zenodo.5762036
title: Supplementary Material

description: Supplementary material.
description: Quantitative Proteomic Analysis of Cervical Cancer Tissues
DOI: 10.5281/zenodo.5762035
title: Supplementary Material

description: Supplementary material.
description: Quantitative Proteomic Analysis of Cervical Cancer Tissues
DOI: 10.25345/c5c28n
title: MassIVE MSV000088526 - Differences in Cord Blood Extracellular Vesicle Cargo in Preterm and Term Births
title: This study determined the cord plasma-derived extracellular vesicle (exosomes; 30-160 nm particles) proteomic profile in patients who had spontaneous preterm birth (PTB) or preterm premature rupture of membranes (pPROM), compared to those who delivered at term regardless of labor status.
DOI: 10.6073/pasta/963ef9875283a6e4da18ef0827839b13
title: Estimation of Abundance and Distribution of Salt Marsh Plants from Images Using Deep Learning

description: Recent advances in computer vision and machine learning, most notably deep convolutional neural networks (CNNs), are exploited to identify and localize various plant species in salt marsh images. Three different approaches are explored that provide estimations of abundance and spatial distribution at varying levels of granularity in terms of spatial resolution. In the coarsest-grained approach, CNNs are tasked with identifying which of six plant species are present/absent in large patches within the salt marsh images. CNNs with diverse topological properties and attention mechanisms are shown capable of providing accurate estimations with &gt; 90% precision and recall in the case of the more abundant plant species whereas the performance of the CNNs is observed to decline in the case of less common plant species. Estimation of percent cover of each plant species is performed at a finer spatial resolution, where smaller image patches are extracted and the CNNs tasked with identifying the plant species or substrate at the center of the image patch. In an ecological setting, several image patches (~100) are extracted and classified using this approach to estimate the percent cover of the various plant species in the image. For the percent cover estimation task, the CNNs are observed to exhibit a performance profile similar to that for the presence/absence estimation task, but with an ~ 5–10% reduction in precision and recall. Finally, estimation of the spatial distribution of the various plant species is performed via semantic segmentation of the input images at the finest level of granularity in terms of spatial resolution. The Deeplab-V3 semantic segmentation architecture is observed to provide very accurate estimations for abundant plant species; however, a significant degradation in performance is observed in the case of less abundant plant species and, in extreme cases, rare plant classes are seen to be ignored entirely. Overall, a clear trade-off is observed between the CNN estimation quality and the spatial resolution of the underlying estimation thereby offering guidance for ecological applications of CNN-based approaches to automated plant identification and localization in salt marsh images.
DOI: 10.15482/usda.adc/1524294
title: Improved Tropical Forage Fertilizer-P Calibration: In support of FRST to conduct state-level soil-fertility correlation and calibration trials for P and/or K
DOI: 10.25345/c57c6r
title: MassIVE MSV000088527 - Inhibiting Host Protein Deposition on Urinary Ca 1 theters Reduces Urinary Tract 2 Infections
title: RAW and MaxLFQ processed data in support of Manuscript
DOI: 10.18112/openneuro.ds003505.v1.0.3
title: VEPCON: Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes
DOI: 10.6073/pasta/4c19209bd0cc73f5b30d6b98eca47bc9
title: McMurdo Dry Valleys LTER: Daily measurement summaries from Friis Hills Meteorological Station (FRSM) in Taylor Valley, Antarctica from 1993 to present

description: As part of the McMurdo Dry Valleys Long Term Ecological Research program, meteorological data are collected from various locations throughout the McMurdo Dry Valleys of Antarctica. This data package containsÂ daily measurement summaries derived from 15-minute data generated by theÂ Friis Hills Meteorological Station (FRSM), located in Taylor Valley (Latitude: -77.7474, Longitude: 161.5162, Elevation: 1,591 meters above sea level).
DOI: 10.6084/m9.figshare.17131496
title: Crucecita 2015 flow hydrograph

description: Flow hydrographs for the Crucecita Alta catchment and El Carmen river at the fan-river junction. The hydrologic model was tested under two conditions of the curve number method.
DOI: 10.25345/c53s24
title: MassIVE MSV000088528 - AAC_Protemics: No ABX Control DAP-R MRSA D712
title: DAP-R MRSA D712 No Antibiotic Control Test Raw from Proteome Discoverer
DOI: 10.6084/m9.figshare.17130089
title: Crucecita Alluvial fan's Topography

description: Topographies used for the study: A modeling methodology to study the tributary-junction alluvial fan connectivity during a debris flow event on NHESS<br>1- A reconstructed pre-event topography based on satellite images and the post-event Lidar topography. This dataset has a 1x1m2 horizontal resolution.<br>2.- Post-event topography lidar scan (acquired in Feb-March 2017 by the Chilean Ministry of Public Works). This dataset has a 1x1m2 horizontal resolution and was post-processed to eliminate vegetation and building.<br>3.- A hypothetical topography that considers a 10x10 m rectangular channel as mitigation works to study different scenarios.<br>
DOI: 10.5281/zenodo.5762187
title: The protein organization of a red blood cell

description: Elution profiles, feature matrix, train and test ppis, and metadata for the paper "The protein organization of a red blood cell". Sae-Lee et al.,
DOI: 10.5281/zenodo.5762442
title: Podcast annotation dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts "

description: Dataset for paper "Identifying Introductions in Podcast Episodes from Automatically Generated Transcripts". Please refer to the paper for details. Compared to the dataset used in the paper, 20 out of the 445 episodes have been removed due to copyright issues. Each data file contains the following fields: - "episode_intro_start": the time stamp for episode introduction start (in milliseconds) - "episode_intro_end": the time stamp for episode introduction end (in milliseconds) - "program_intro_start": the time stamp for program introduction start (in milliseconds) - "program_intro_end": the time stamp for program introduction end (in milliseconds) - "program_name": name of the podcast program - "episode_name": name of the podcast episode - "transcription": JSON string containing the transcription, including the timestamps. - "annotator": anonymized annotator ID.
DOI: 10.6073/pasta/100710260efe01294abdc614e43b75d9
title: Data from: Nutrient identity modifies the destabilizing effects of eutrophication in grasslands

description: Nutrient enrichment can simultaneously increase and destabilize plant biomass production, with co-limitation by multiple nutrients potentially intensifying these effects. Here, we test how factorial additions of nitrogen (N), phosphorus (P), and potassium with essential nutrients (K+) affect the stability (mean/standard deviation) of aboveground biomass in 34 grasslands over seven years. Destabilization with fertilization was prevalent but was driven by single nutrients, not synergistic nutrient interactions. On average, N-based treatments increased mean biomass production by 21-51% but increased its standard deviation by 40-68% and so consistently reduced stability. Adding P increased interannual variability and reduced stability without altering mean biomass, while K+ had no general effects. Declines in stability were largest in the most nutrient-limited grasslands, or where nutrients reduced species richness or intensified species synchrony. We show that nutrients can differentially impact the stability of biomass production, with N and P in particular disproportionately increasing its interannual variability.
DOI: 10.5281/zenodo.5762478
title: Cacatua leadbeateri decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5762477
title: Cacatua leadbeateri decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5762476
title: More random numbers

description: Random numbers used to showcase showyourwork!<br><br>Created using showyourwork from this GitHub repo using the following command:<br><br><pre><code class="language-bash">cd src/figures && python src/analysis/make_random.py</code></pre><br>
DOI: 10.18112/openneuro.ds003773.v1.0.4
title: OpenNeuroPET Test Data Set
DOI: 10.5517/ccdc.csd.cc28v2cl
title: CCDC 2110429: Experimental Crystal Structure Determination

description: Related Article: Jérôme Long, Aleksei O. Tolpygin, Anton V. Cherkasov, Yulia V. Nelyubina, Yannick Guari, Joulia Larionova, Alexander A. Trifonov|2021|CrystEngComm|23|8351|doi:10.1039/D1CE01266E
DOI: 10.5517/ccdc.csd.cc276kgh
title: CCDC 2061886: Experimental Crystal Structure Determination

description: Related Article: G. Gallo, M.W. Terban, I. Moudrakovski, T. Huber, M. Etter, M. Ernst, B. Hinrichsen, R.E. Dinnebier|2021|Acta Crystallogr.,Sect.B:Struct.Sci.,Cryst.Eng. and Mat.|77|986|doi:10.1107/S2052520621010088
DOI: 10.5517/ccdc.csd.cc285r9j
title: CCDC 2090897: Experimental Crystal Structure Determination

description: Related Article: Bo-Yi Chen, Min-Yem Tsai, Yu-Chia Su, Po-Heng Lin, Jérôme Long|2021|CrystEngComm|23|8343|doi:10.1039/D1CE00909E
DOI: 10.5517/ccdc.csd.cc285rcl
title: CCDC 2090899: Experimental Crystal Structure Determination

description: Related Article: Bo-Yi Chen, Min-Yem Tsai, Yu-Chia Su, Po-Heng Lin, Jérôme Long|2021|CrystEngComm|23|8343|doi:10.1039/D1CE00909E
DOI: 10.5517/ccdc.csd.cc285rdm
title: CCDC 2090900: Experimental Crystal Structure Determination

description: Related Article: Bo-Yi Chen, Min-Yem Tsai, Yu-Chia Su, Po-Heng Lin, Jérôme Long|2021|CrystEngComm|23|8343|doi:10.1039/D1CE00909E
DOI: 10.5517/ccdc.csd.cc285rbk
title: CCDC 2090898: Experimental Crystal Structure Determination

description: Related Article: Bo-Yi Chen, Min-Yem Tsai, Yu-Chia Su, Po-Heng Lin, Jérôme Long|2021|CrystEngComm|23|8343|doi:10.1039/D1CE00909E
DOI: 10.6084/m9.figshare.3761562.v2065
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.3886/e112542v1-103500
title: Replication data for: The Impact of Family Income on Child Achievement: Evidence from the Earned Income Tax Credit

description: Using an instrumental variables strategy, we estimate the causal effect of income on children's math and reading achievement. Our identification derives from the large, nonlinear changes in the Earned Income Tax Credit. The largest of these changes increased family income by as much as 20 percent, or approximately $2,100, between 1993 and 1997. Our baseline estimates imply that a $1,000 increase in income raises combined math and reading test scores by 6 percent of a standard deviation in the short run. Test gains are larger for children from disadvantaged families and robust to a variety of alternative specifications. (JEL H24, H31, I21, I38, J13)
DOI: 10.5066/p91ebtvv
title: Enhanced Terrain Imagery of the Scranton 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution

description: This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Scranton 30 x 60 minute quadrangle in Pennsylvania. It also covers part of the Delaware River Basin. The source data used to construct this imagery consists of 1-meter resolution lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acquisitions published between 2017 and 2019 from the Pennsylvania Spatial Data Access (PASDA). The data were processed using geographic information systems (GIS) software. The data is projected in North America Datum (NAD) UTM Zone 18 North. This representation illustrates the terrain as a hillshade with contrast adjusted to highlight local relief according to a topographic position index (TPI) calculation.
DOI: 10.5281/zenodo.5753073
title: Acoustic Fabrics dataset

description: This dataset contains the data collected for the acoustic fabric project that are featured in the figures.
DOI: 10.5281/zenodo.5753072
title: Acoustic Fabrics dataset

description: This dataset contains the data collected for the acoustic fabric project that are featured in the figures.
DOI: 10.5066/p9uc5sb7
title: Enhanced Terrain Imagery of the Honesdale 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution

description: This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Honesdale 30 x 60 minute quadrangle in Pennsylvania and part of New York. It also covers part of the Delaware River Basin. The source data used to construct this imagery consists of 1-meter resolution lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acquisitions published between 2017 and 2019 from the Pennsylvania Spatial Data Access (PASDA). The data were processed using geographic information systems (GIS) software. The data is projected in North America Datum (NAD) UTM Zone 18 North. This representation illustrates the terrain as a hillshade with contrast adjusted to highlight local relief according to a topographic position index (TPI) calculation.
DOI: 10.6084/m9.figshare.17131649
title: Cortical functional profiles of face-selective neurons from distant face patches during movie watching

description: <b>1. correlationMatrix_neuron_fROI.mat</b><br>Contains the matrix of correlation (166 x 37) between 166 face-selective neurons recorded from different face patches and hemodynamic responses from 37 functional Regions of Interest (ROIs). fROIs are defined across visual cortex, including regions from occipital, temporal, parietal, and frontal lobes. Information of neurons and fROIs are provided in separate file (info.mat).<br><br><b>2. info.mat</b><br>Contains the information of the functional Regions of Interest (ROIs) and face patch neurons, used to compute the single-unit fMRI maps<br>- info.ROI : names of 37 cortical functional ROIs, in order- info.cell.setArea : name of face patch that each neuron is recorded from- info.cell.groupID : final grouping information after clustering based on the correlation matrix- info.cell.fsi : face-selective index computed based on responses to brief presentations of images of faces and other objects, collected from separate experiments.
DOI: 10.6084/m9.figshare.17131649.v1
title: Cortical functional profiles of face-selective neurons from distant face patches during movie watching

description: <b>1. correlationMatrix_neuron_fROI.mat</b><br>Contains the matrix of correlation (166 x 37) between 166 face-selective neurons recorded from different face patches and hemodynamic responses from 37 functional Regions of Interest (ROIs). fROIs are defined across visual cortex, including regions from occipital, temporal, parietal, and frontal lobes. Information of neurons and fROIs are provided in separate file (info.mat).<br><br><b>2. info.mat</b><br>Contains the information of the functional Regions of Interest (ROIs) and face patch neurons, used to compute the single-unit fMRI maps<br>- info.ROI : names of 37 cortical functional ROIs, in order- info.cell.setArea : name of face patch that each neuron is recorded from- info.cell.groupID : final grouping information after clustering based on the correlation matrix- info.cell.fsi : face-selective index computed based on responses to brief presentations of images of faces and other objects, collected from separate experiments.
DOI: 10.5281/zenodo.5762463
title: SOFIA Recipe example data

description: Example data for basic inspection and analysis of SOFIA data. Includes data for multiple instruments.
DOI: 10.25721/85dz-7302
title: Valse-Vienne
DOI: 10.25721/jp20-9h70
title: Bourrée
DOI: 10.25721/bnkn-fm81
title: Histoire en languedocien