cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1291385
title: Ouagadougou Land Use Map At Street Block Level

description: This datatset contains a land use classification of Ouagadougou (Burkina Faso) at the street block level. It was created following the methodology presented in [1].

Description of the files:


	"Ouaga_landuse_shapefile.zip" : Shapefile of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table.
	"Ouaga_landuse_style.zip" : Files for style of the shapefile.


Attribute table content:


	"CAT", "GID" : ID of the street block
	"PROB_ACS" : Probability to belong to class ACS
	"PROB_BARE" : Probability to belong to class BARE
	"PROB_PLAN" : Probability to belong to class PLAN
	"PROB_UNPLA" : Probability to belong to class UNPLAN
	"PROB_VEG" : Probability to belong to class VEG
	"FIRST_LABE" : Class with the highest classification probability
	"SEC_LABEL" : Class with the second highest classification probability
	"FIRST_PROB" : Value of the highest classification probability
	"SEC_PROB" : Value of the second highest classification probability
	"UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB"
	"BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map)
	"MAP_LABEL" : Final classification label with uncertainty and different density classes


Legend classes label:


	"VEG" : Vegetation
	"BARE" : Bare soils
	"ACS" : Non-residential built-up (administrative, commercial, services, etc.)
	"PLAN" : Planned residential built-up
	"PLAN_LD" : Planned residential low density built-up
	"UNPLAN" : Unplanned residential built-up
	"UNPLAN_LD" : Unplanned residential low density built-up
	"UNCERT" : Uncertain classification


References:

[1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", <em>ISPRS Int. J. Geo-Inf.</em> <strong>2018</strong>, <em>7</em>(7), 246. https://doi.org/10.3390/ijgi7070246 

[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. https://doi.org/10.5281/zenodo.1290637.

Funding: 

This dataset was produced in the frame of two research project : MAUPP (http://maupp.ulb.ac.be) and REACT (http://react.ulb.be), funded by the Belgian Federal Science Policy Office (BELSPO).
description: The production of this dataset was founded by BELSPO (Belgian Federal Science Policy Office) in the frame of the
STEREO III program, as part of the MAUPP (SR/00/304) and REACT (SR/00/337) project (http://maupp.ulb.ac.be
and http://react.ulb.be/).
DOI: 10.5281/zenodo.5047817
title: Hydrogen molecule ion benchmarks computed with the Riccati-Padé method

description: Benchmark values of the electronic energy, separation constant and internuclear distances for the hydrogen molecule cation computed with the Riccati-Padé method.
DOI: 10.5439/1150271
title: ARM: Balloon-borne sounding system (BBSS): research-mode winds, presssure, temp, and RH

description: Balloon-borne sounding system (BBSS): research-mode winds, presssure, temp, and RH
DOI: 10.6084/m9.figshare.10011788
title: State of Open Data 2019

description: Raw data and questionnaire from the Springer Nature - Digital Science collaboration of an annual survey of research authors on their attitudes and behaviour around research data.
DOI: 10.6084/m9.figshare.978502
title: Gaussian Job Archive for C58H118

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.978832
title: Gaussian Job Archive for C58H118

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.978833
title: Gaussian Job Archive for C58H118

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.5281/zenodo.3939623
title: State of open data in catalysis and contiguous fields

description: This dataset comprises two sub-sets of data: Dataset_1: field-specific dataset to article ratio from 2011 to 2019 Dataset_2: survey on datasets for hydrodeoxygenation These correspond to Figure 5 and Figure 4, respectively, of the following publication: P.S.F. Mendes, S. Siradze, L. Pirro, J.W. Thybaut, Open Data in Catalysis: From Today's Big Picture to the Future of Small Data, ChemCatChem, 2021, 13, 836-850, https://doi.org/10.1002/cctc.202001132
DOI: 10.5281/zenodo.7683787
title: All-Sky Imager Observations from Yellow River Station on 01 November 2007

description: This dataset contains the all-sky imager observations from a Chinese arctic station, the Yellow River Station, on 01 November 2007. The name of each compressed file corresponds to the observed waveband, i.e., 6300nm, 5577nm, and 4278nm. The observation data files are in img format.
DOI: 10.4225/15/53266bec9607f
title: Waves in-ice observations made during the SIPEX II voyage of the Aurora Australis, 2012
DOI: 10.5066/p95xfiyv
title: Pumping Rate, Drawdown, and Atmospheric Pressure Data from Hydraulic Tomography Experiment at the Former Naval Air Warfare Center, West Trenton, NJ, 2015-2016

description: This data release presents pumping rate data, water-level drawdown and recovery data, and atmospheric pressure data collected during 48 pumping tests conducted in 2015 and 2016 for a hydraulic tomography field experiment in the mudstone aquifer underlying the former Naval Air Warfare Center (NAWC), West Trenton, NJ. The pumping rate data include rates measured by in-line flow meters and rates measured manually by recording the time to fill a known volume with the pump discharge water. The water-level drawdown data include drawdowns measured by fiber-optic pressure transducers, and drawdowns measured by strain-gauge transducers. For many tests, water-level recovery data are also provided, which measure the change in water levels after the end of a test. The data were collected in seven closely-spaced boreholes (wells 83-89), each open to approximately 20 meters of aquifer thickness. The wells were instrumented with inflatable packers to divide the open wells into multiple shorter isolated observation intervals in which the pumping tests occurred and drawdowns were monitored. The locations of the wells and the configuration of the wells into named isolated intervals are given in "Tiedeman, C.R., and Barrash, W., 2019, Hydraulic Tomography: 3D Hydraulic Conductivity, Fracture Network, and Connectivity in a Mudstone Aquifer: Groundwater, https://doi.org/10.1111/gwat.12915" (referred to as Tiedeman and Barrash 2019), Figures 1 and S2 and Tables S1 and S2. Information about each of the pumping tests is given in Table 1 of that publication.
DOI: 10.6073/pasta/2148e1a33390fc0a4ecc5226c5ccc31a
title: Snow cover profile data for Niwot Ridge and Green Lakes Valley, 1993 - ongoing.

description: ABSTRACT.Snow pits were excavated at various locations on Niwot Ridge and within the Green Lakes Valley. Temperature and snow density were measured at various depths throughout the snow cover profiles to characterize the temperature and snow water equivalent (SWE) of the snowpack throughout the year. Snow density was measured at 10-cm intervals using a 1000-ml cutter. Data on snow grain qualities were collected beginning in the 1994-95 snow season.
DOI: 10.15468/pkhsnb
title: The Second Atlas of Breeding Birds in Britain and Ireland: 1988-1991

description: A dataset of the distribution of the breeding birds of Britain & Ireland from 1988 to 1991, generated using a list of the species observed within each 10km square over the survey period.
DOI: 10.5066/f7xk8d2r
title: Nitrogen, Phosphorus, and Suspended-Sediment Loads and Trends measured at the Chesapeake Bay Nontidal Network Stations: Water Years 1985-2014

description: Nitrogen, phosphorus, and suspended-sediment loads, and changes in loads, in rivers across the Chesapeake Bay watershed have been calculated using monitoring data from the Chesapeake Bay Nontidal Network (NTN) stations for the period 1985 through 2014. Nutrient and suspended-sediment loads and changes in loads were determined by applying a weighted regression approach called WRTDS (Weighted Regression on Time, Discharge, and Season). The load results represent the total mass of nitrogen, phosphorus, and suspended sediment that was exported from each of the NTN watersheds. To determine the trend in loads, the annual load results are flow normalized to integrate out the year-to-year variability in river discharge. The trend in load is derived from the flow-normalized load timeseries and represents the change in load resulting from changes in sources, delays associated with storage or transport of historical inputs, and (or) implemented management actions. Four data tables are provided that describe nitrogen, phosphorus, and suspended-sediment conditions across the NTN: (1) Annual Loads, (2) Monthly Loads, (3) Trends in Annual Loads, and (4) Average Yield (mass per unit area).
DOI: 10.5066/p94jc2pd
title: Data and estimates for wet deposition and streamwater solute fluxes at Panola Mountain Research Watershed, Stockbridge, Ga., water years 1986-2016

description: This dataset contains the data and results of an analysis estimating wet deposition and streamwater solute fluxes at Panola Mountain Research Watershed (PMRW), Panola Mountain State Park, Stockbridge, Georgia for water years 1986-2016. The PMRW is a small (41 ha), relatively undisturbed, forested headwater catchment in the Piedmont Province of Southeastern United States. This data provides the basis for using a watershed mass-balance approach, in which inputs and outputs of water and solutes are quantified and compared to better understand hydrologic and biogeochemical processes on a watershed scale. The dataset contains 13 datasets consisting of a variety of data series and results, which are summarized herein including their purpose(s): (1) Precipitation amount (1-minute time-step) used to estimate wet deposition, for predicting monthly soil moisture, and as variables in most of the streamwater concentration regression models. (2) Precipitation water quality (mostly weekly composite samples) used to estimate wet deposition. (3) Streamwater stage and flow (5-minute time-step, 1-minute during stormflow) used to estimate streamwater solute fluxes, for predicting monthly soil moisture, and as variables in the streamwater concentration regression models. (4) Stream water quality (discrete weekly and storm samples) used to estimate streamwater solute fluxes using a regression-based approach. (5) 13-year soil moisture time-series from a profile with 3 depths (15, 40, and 70 cm; 5-minute time-step), which represents shallow watershed storage, which was used to classify climatic conditions of some solutes (separate concentration regression models were developed for each climate category). (6) Data, calibration variables, and predictions for a USGS monthly water-balance program used to model monthly watershed soil moisture when measured soil moisture data was unavailable. (7) Unit-value base flow (as determined from a hydrograph separation using the Eckhardt filter), which was used to calculate the base-flow ratio that was a variable within a few of the streamwater concentration regression models. The hydrograph separation also defined base-flow and stormflow periods used to determine the base-flow and rising limb indicator variables used by most of the concentration models. Estimates of wet deposition for 9 solutes, summarized on (8) daily, (9) monthly, and (10) annual time-steps. Streamwater solute flux estimates for 10 solutes, summarized on (11) daily and (12) annual time-steps. (13) A dataset of edit code descriptions used by the unit-value precipitation amount, streamwater stage and flow, and soil moisture time-series.
DOI: 10.5067/aura/tes/tl2hdoln_l2.006
title: TES/Aura L2 HDO Lite Nadir - Version 6
DOI: 10.5067/measures/swdb/data201
title: SeaWiFS Deep Blue Aerosol Optical Depth and Angstrom Exponent Daily Level 2 Data V4.0
DOI: 10.5258/ecrystals/680
title: N,N'-bis(4-Methoxyphenyl)urea
DOI: 10.5281/zenodo.7787502
title: Supplementary data for Saintilan et al. 2023

description: Supplementary Data 1: Surface Elevation Table data for Tidal Marshes, including elevation rate, elevation deficit, elevation capital, losses and gains of water area within 5 kilometres, losses and gains of tidal marsh within 5 kilometres. Supplementary Data 2: Area change in coral islands.
DOI: 10.5439/1027282
title: ARM: ARSCL: multiple outputs from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

description: ARSCL: multiple outputs from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR
DOI: 10.6075/j00z71hw
title: Data from: Readily Available Primary Aminoboranes as Powerful Reagents for Aldimine Synthesis

description: Gaussian 09, Revision D.01,M. J. Frisch, G. W. Trucks, H. B. Schlegel, G. E. Scuseria, M. A. Robb, J. R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G. A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H. P. Hratchian, A. F. Izmaylov, J. Bloino, G. Zheng, J. L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J. A. Montgomery, Jr., J. E. Peralta, F. Ogliaro, M. Bearpark, J. J. Heyd, E. Brothers, K. N. Kudin, V. N. Staroverov, T. Keith, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J. C. Burant, S. S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J. M. Millam, M. Klene, J. E. Knox, J. B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R. E. Stratmann, O. Yazyev, A. J. Austin, R. Cammi, C. Pomelli, J. W. Ochterski, R. L. Martin, K. Morokuma, V. G. Zakrzewski, G. A. Voth, P. Salvador, J. J. Dannenberg, S. Dapprich, A. D. Daniels, O. Farkas, J. B. Foresman, J. V. Ortiz, J. Cioslowski, and D. J. Fox, Gaussian, Inc., Wallingford CT, 2013.
description: Abstract: Primary aminoboranes (RNHBR2), which are readily available by spontaneous dehydrocoupling of amines and boranes cleanly react at room temperature with aldehydes to give aldimines. The overall transformation from amines to aldimines can be conveniently performed by a sequential one-pot reaction. This synthetic strategy is especially useful for electron poor and bulky amines which are reluctant to react with aldehydes under dehydration conditions. Using a Glorius robustness screen, we show that this methodology is chemoselective, and functional group tolerant. Computational and experimental data support the irreversible formation of the aldimine product in marked contrast with traditional methods.
DOI: 10.5519/qd.hdjrnwmi
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (177 records)
DOI: 10.17632/gkzwg6g8d2.1
title: AUTOP data

description: Database
Protocol
SAP
annoted eCRF
DOI: 10.6084/m9.figshare.23633907
title: Huddling network analysis (group-level network metrics)

description: This folder includes the data sets and R-codes used to compare group-level network metrics (i.e., density, modularity, and centralization) from huddling data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.6084/m9.figshare.23633907.v1
title: Huddling network analysis (group-level network metrics)

description: This folder includes the data sets and R-codes used to compare group-level network metrics (i.e., density, modularity, and centralization) from huddling data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.6084/m9.figshare.23585943
title: Aggression network analysis (group-level network metrics)

description: This folder includes the data sets and R-codes used to compare group-level network metrics (i.e., density, modularity, and centralization) from aggression data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.6084/m9.figshare.23585943.v1
title: Aggression network analysis (group-level network metrics)

description: This folder includes the data sets and R-codes used to compare group-level network metrics (i.e., density, modularity, and centralization) from aggression data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.5281/zenodo.8332074
title: Experten Interview Transkript - Video Angebot Zeitung - 2023 September

description: Experten Interview Transkript - Video Angebot Zeitung - 2023 September
DOI: 10.5281/zenodo.8332073
title: Experten Interview Transkript - Video Angebot Zeitung - 2023 September

description: Experten Interview Transkript - Video Angebot Zeitung - 2023 September
DOI: 10.5281/zenodo.8332070
title: Experten Interview Transkript - Video Player - 2023 September

description: Experten Interview Transkript - Video Player - 2023 September
DOI: 10.5281/zenodo.8332069
title: Experten Interview Transkript - Video Player - 2023 September

description: Experten Interview Transkript - Video Player - 2023 September
DOI: 10.5281/zenodo.8332062
title: Experten Interview Transkript - Video Encoding - 2023 September

description: Experten Interview Transkript - Video Encoding - 2023 September
DOI: 10.5281/zenodo.8332061
title: Experten Interview Transkript - Video Encoding - 2023 September

description: Experten Interview Transkript - Video Encoding - 2023 September
DOI: 10.6084/m9.figshare.23530893
title: Grooming network analysis (group-level network metrics)

description: This folder includes the data sets and R-codes used to compare group-level network metrics (i.e., density, modularity, and centralization) from grooming data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.6084/m9.figshare.23530893.v1
title: Grooming network analysis (group-level network metrics)

description: This folder includes the data sets and R-codes used to compare group-level network metrics (i.e., density, modularity, and centralization) from grooming data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.5281/zenodo.8332076
title: Experten Interview Transkript - Video Playout - 2023 August

description: Experten Interview Transkript - Video Playout - 2023 August
DOI: 10.5281/zenodo.8332075
title: Experten Interview Transkript - Video Playout - 2023 August

description: Experten Interview Transkript - Video Playout - 2023 August
DOI: 10.5281/zenodo.8332082
title: Experten Interview Transkript - Videostreaming Industrie - 2023 August

description: Experten Interview Transkript - Videostreaming Industrie - 2023 August
DOI: 10.5281/zenodo.8332081
title: Experten Interview Transkript - Videostreaming Industrie - 2023 August

description: Experten Interview Transkript - Videostreaming Industrie - 2023 August
DOI: 10.6084/m9.figshare.24115383
title: machine-learning dataset

description: The dataset is used to train machine learning model for the study of passivation effect of small molecules
DOI: 10.6084/m9.figshare.22782215
title: Aggression network analysis (individual-level network metrics)

description: This folder includes the data sets and R-codes used to compare individual-level network metrics (i.e., degree, eigenvector and betweenness) from aggression data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.6084/m9.figshare.22782215.v1
title: Aggression network analysis (individual-level network metrics)

description: This folder includes the data sets and R-codes used to compare individual-level network metrics (i.e., degree, eigenvector and betweenness) from aggression data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.6084/m9.figshare.22762424
title: Huddling network analysis (individual-level network metrics)

description: This folder includes the data sets and R-codes used to compare individual-level network metrics (i.e., degree, eigenvector and betweenness) from huddling data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.6084/m9.figshare.22762424.v1
title: Huddling network analysis (individual-level network metrics)

description: This folder includes the data sets and R-codes used to compare individual-level network metrics (i.e., degree, eigenvector and betweenness) from huddling data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.6084/m9.figshare.22762421
title: Grooming network analysis (individual-level network metrics)

description: This folder includes the data sets and R-codes used to compare individual-level network metrics (i.e., degree, eigenvector and betweenness) from grooming data collected through focal animal sampling and all-occurrences behavioural sampling
DOI: 10.6084/m9.figshare.22731083
title: Network robustness

description: This folder contains data and R-codes to assess social network robustness. We used two approaches to do this: we first assessed, for each data collection method and for each social behaviour, the variation in mean value of all three individual-level network measures as well as the variation of all three network-level measures over time with monthly increases of data collected. We expected the curves to become progressively “flatter” because, if the networks were becoming more and more stable over time, monthly variation in network measures would become smaller and smaller as observers recorded fewer and fewer new edges between nodes. Second, we used bootstrapping to estimate network uncertainty, which reflects the (un)certainty with which network metrics were estimated. For each monthly data and for each social behaviour examined, the identity of the recipient was randomly reshuffled and social network metrics were re-calculated. This procedure was repeated 1000 times, eventually generating a distribution of possible values. From this distribution, we extracted the 95% confidence interval and subtracted the maximum and minimum value of this range in order to calculate the uncertainty index. We then assessed, for both sampling method, the monthly variation of this uncertainty index, expecting this value to decline as more and more observations were recorded and networks would become more certain.
DOI: 10.5281/zenodo.5790078
title: A dataset of 150000 terminal weighted projective spaces

description: <strong>Weighted projective spaces with at worst terminal singularities</strong> A dataset of 150000 randomly generated weighted projective spaces with at worst terminal singularities, in dimensions 1 to 10. The data consists of the plain text files "rank_1_dim_N.txt" where N, which is the dimension of the weighted projective space, is in the range 1 to 10. Each line of the file is a sequence of weights of length N+1. For example, the first line of "rank_1_dim_4.txt" is: [1,2,5,14,21] and this corresponds to the 4-dimensional weighted projective space P(1,2,5,14,21). For details, see the paper: "Machine learning the dimension of a Fano variety", Tom Coates, Alexander M. Kasprzyk, and Sara Veneziale, <em>Nature Communications</em>, <strong>14:</strong>5526 (2023). doi:10.1038/s41467-023-41157-1 Magma code capable of generating this dataset is in the file "generate_rank_1.m". If you make use of this data, please cite the above paper and the DOI for this data: doi:10.5281/zenodo.5790079