cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26171/khye-3813
title: ReNovRisk - TRANSFERTS - Données brutes utilisées pour le calcul des bilans de productivité et de calcification sur le récif de La Saline (La Réunion)

description: Afin d'estimer les bilans de productivité (photosynthèse-respiration) et de calcification sur le récif frangeant de La Saline, des mesures de lumière, température, salinité, courant et chimie de l'eau (oxygène dissous, pH, alcalinité) ont été effectuées sur 5 sites (Toboggan, Livingstone, Trou d'Eau, Bodega et sur la pente externe) répartis sur 5 périodes (Février-Mars 2020, Octobre 2020, Avril 2021, Octobre 2021 et Juin 2022).
Ces données ont permis de caractériser la variabilité spatio-temporelle naturelle de la chimie de l'eau de mer et d'identifier l'influence des facteurs locaux sur cette variabilité. Elles ont aussi permis d'étudier la faisabilité de la méthode Eulérienne de respirométrie en flux pour l'estimation des bilans de productivité et de calcification sur le récif frangeant de La Saline et ainsi de calculer ces bilans sur deux secteurs (Toboggan et Trou d'Eau/Bodega).
description: On the reef flat, autonomous measurements and water sampling were performed at three sites over three periods to
characterize the spatial and temporal variability of physical and biogeochemical parameters.
Measurements of light, temperature, salinity, currents, and water chemistry were carried out at Toboggan and
Livingstone in February 2020 (summer) for 4 and 2 days respectively, at Bodega in March 2020 (summer) for 3
days, at Livingstone in October 2020 (winter) for 4 days, at Trou d’Eau in April 2021 (summer) for 15 days, at
Livingstone, Toboggan and Trou d’Eau in October 2021 (winter) for 5, 3 and 2 days respectively and Toboggan in
June 2022 (mid-season) for 3 days. Offshore measurements of temperature, salinity and water chemistry were
performed in April and October 2021.
DOI: 10.25582/data.2021-01.2125139/2160190
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2106500/2160191
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2125165/2160192
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2116281/2160193
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2104677/2160194
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115378/2160195
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099824/2160196
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2123212/2160197
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2099222/2160198
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2104286/2160199
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2117717/2160200
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2118661/2160201
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2104188/2160202
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099179/2160203
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099134/2160204
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099211/2160205
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2114865/2160206
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099233/2160207
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099516/2160208
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099175/2160209
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2123016/2160210
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2123289/2160211
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2117731/2160212
title: Data for EMSL Project 51287 from January 2021
DOI: 10.26171/wpzr-av10
title: SYSMOP : Signature des pluie sur des récepteurs sismiques

description: Le projet a pour objectif d'améliorer le signal de la pluie détectée par les géophones en comparant les données météorologiques d'un disdromètre, d'un pluviomètre et de 3 géophones afin d'extraire des données des sismographes pour mieux comprendre le transport sédimentaire issu du réseau sismologique installé dans la rivière des pluies et la rivière du Mat.
Les objectifs sont :
1) déterminer les caractéristiques sismiques de la pluie sur le site de mesures
2) A terme, comprendre le déclenchement des éboulements et glissements liés aux pluies
A court terme, ce projet devrait aussi permettre de :
1) comprendre pour un même type de pluie l'influence de sols de rugosités différentes sur les signaux enregistrés par les sismomètres
2) intégrer/contraindre pour un même type de sol l'influence de types de pluies différentes sur les enregistrements des sismomètres
3) déterminuer l'influence des tailles des gouttes et du nombre de gouttes (indications données par le disdromètre) sur le signal sismique
DOI: 10.25582/data.2021-01.2118751/2160213
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2115780/2160214
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2104328/2160215
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2125000/2160216
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2104977/2160217
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2106554/2160218
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2114784/2160219
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2114773/2160220
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2117972/2160221
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2124896/2160222
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2104347/2160223
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2118608/2160224
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2106784/2160225
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2125193/2160226
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2100023/2160227
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099858/2160228
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2122930/2160229
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2115324/2160230
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2118664/2160231
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2113656/2160232
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2105790/2160233
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115421/2160234
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2115598/2160235
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2118588/2160236
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2099244/2160237
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2104575/2160238
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099192/2160239
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2113657/2160240
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2021-01.2118594/2160241
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2118748/2160242
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2124858/2160243
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2122934/2160244
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2021-01.2124655/2160245
title: Data for EMSL Project 51287 from January 2021
DOI: 10.25582/data.2020-12.2104644/2160246
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2114878/2160247
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2099126/2160248
title: Data for EMSL Project 51287 from December 2020
DOI: 10.25582/data.2020-12.2100795/2160249
title: Data for EMSL Project 51287 from December 2020
DOI: 10.3886/e112326v1-142732
title: Replication data for: Multinationals and Anti-sweatshop Activism

description: During the 1990s, anti-sweatshop activists campaigned to improve conditions for workers in developing countries. This paper analyzes the impact of anti-sweatshop campaigns in Indonesia on wages and employment. Identification is based on comparing the wage growth of workers in foreign-owned and exporting firms in targeted regions or sectors before and after the initiation of anti-sweatshop campaigns. We find the campaigns led to large real wage increases for targeted enterprises. There were some costs in terms of reduced investment, falling profits, and increased probability of closure for smaller plants, but we fail to find significant effects on employment. (JEL F23, J31, J81, L67, O14, O15)
DOI: 10.5439/2203936
title: SAIL-Net CloudPuck CCN Data

description: SAIL-Net is a DOE funded project in the East River Watershed near Crested Butte, Colorado with the goal of advancing our understanding of aerosol-cloud interactions in complex, mountainous regions. Through the deployment of a network of six low cost microphysics nodes in Fall 2021 in the same domain at the SAIL campaign, SAIL-Net provides data on aerosol size distributions, cloud condensation nuclei (CCN), and ice nucleations particles (INP). This network enables the investigation of small-scale variations in complex terrain.This specific dataset provides the cleaned data recorded from the CloudPuck, an in house instrument made by Handix Scientific that counts CCN concentrations. The CloudPuck was deployed at the sites for the summer/fall of 2022 before winter conditions were too harsh to maintain the instrument. For more information on the the CloudPuck or how the raw data are processed, see the read me.
DOI: 10.25919/31xq-0p06
title: Parasite resistance in sheep - sheepgenomics program trials

description: The sheepgenomics program sought to discover the differences between sheep bred to be resistant to parasitic gastro-intestinal nematodes (GIN), and those bred to be susceptible to infections with these parasites. The data provided details the design of sheep trial experiments and data from the animals housed in the experiments, including information identifying individuals and their relatedness. The experiments were approved to be conducted under the animal ethics legislation of NSW, with animal research authorities (ARAs) issued by the CSIRO Armidale animal ethics committee (ARAs 03/75, 04/49, 05/58).
DOI: 10.5281/zenodo.10052537
title: Land use information and NO2 observations of environmental monitoring stations in China

description: The land use information for environmental monitoring stations in China is stored in the "site_lu.csv" file. The monthly NO2 observations from 2015 to 2017 are stored in the "NO2_ground.csv" file.
DOI: 10.5281/zenodo.10052536
title: Land use information and NO2 observations of environmental monitoring stations in China

description: The land use information for environmental monitoring stations in China is stored in the "site_lu.csv" file. The monthly NO2 observations from 2015 to 2017 are stored in the "NO2_ground.csv" file.
DOI: 10.25903/rfrs-gy54
title: Impacts of ocean warming on the settlement success and post-settlement survival of Pacific crown-of-thorns starfish (Acanthaster cf. solaris) 

description: <p>In this study we aimed to (1) establish the effect of warming on settlement success and metamorphosis in crown-of-thorns starfish (2) establish how prior thermal exposure of the settlement substrate (CCA) affects settlement success (3) determine how warming influences post-settlement survival of early juveniles. The data for (1) and (2) consists of a single line for each well at both 24 and 48 hrs, containing the counts of the number of settled and non-settled larvae, the number of completely metamorphosed settlers and partially metamorphosed settlers and the number of settlers on CCA and not on CCA. The CCA pre-treatment and surface area of the CCA for each well is also included. The survival data consists of a single line for each individual within every well, indicating the time of death, or the final timepoint, if the individual survived, and the status i.e. whether the individual died or survived. The surface area of the CCA in each well is also provided. The effect of temperature on settlement success, metamorphosis and CCA treatment was tested using various binomial generalised linear models, and cox regression was used to analyse post-settlement survival.</p>
<p>Software/equipment used to manipulate/analyse the data: R v.4.1.2 for Mac (R Core Team, 2021)</p>
DOI: 10.25903/1n1k-7d65
title: Diel rhythm and thermal independence of metabolic rate in a benthic shark

description: <p>Data presented in:<br />Wheeler CR, Kneebone J, Heinrich D, Strugnell JM, Mandelman JW, Rummer JL. 2022. Diel rhythm and thermal independence of metabolic rate in a benthic shark (Journal of Biological Rhythms)</p>
<p>Data collected from adult epaulette sharks (H. ocellatum) at JCU MARFU from July 2020-2021. Ethical approval for this study was received from JCU under ethics approval A2655. The full methodology is available in the Open Access publication from the Related publications link below.</p>
<p>This dataset contributes to a PhD dissertation by Carolyn Wheeler from James Cook University and The University of Massachussetts Boston.</p>
<p>Software/equipment used to create/collect the data: OXROB3 fibre optic probe with a Firesting Optical Oxygen Meter (Pyroscience GmbH, Aachen, Germany).<br />Software/equipment used to manipulate/analyse the data: R studio</p>
DOI: 10.25903/84e2-v630
title: Seagrass experts' and managers' vulnerability scores

description: <p>Data and R script related to the third (MPhil) thesis chapter: 'The Vulnerability of World Heritage seagrass habitats to climate change'</p>
<p>The data were collected through 2 quantitative surveys targeting seagrass experts and site managers of WH properties. Data includes opinions on exposure and sensitivity  of seagrass in 6 bioregions to 19 stressors, cumulative impact type and level of adaptive capacity. </p>
<p>Software/equipment used to create/collect the data: Data were collected through 2 online surveys with the software Survey Monkey (Advantage Plan)</p>
<p>Software/equipment used to manipulate/analyse the data: Data were analysed with R and RStudio <br />Version:<br />RStudio 2021.09.2+382 "Ghost Orchid" Release (fc9e217980ee9320126e33cdf334d4f4e105dc4f, 2022-01-04) for Windows<br />Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) QtWebEngine/5.12.8 Chrome/69.0.3497.128 Safari/537.36</p>
DOI: 10.25903/anb0-mc67
title: Prairie Pothole Region Brood Count Data 2008 - 2017

description: <p>These files contain brood count data along with the relevant covariates used to model brood abundance in a hierarchical spatial abundance model for the Prairie Pothole Region of the U.S. Data were amalgamated from three previous studies with the permission of the authors:</p>
<p>Distribution of Duck Broods Relative to Habitat Characteristics in the Prairie Pothole Region <a href="https://doi.org/10.1002/jwmg.466">https://doi.org/10.1002/jwmg.466</a></p>
<p>Impacts of Oil and Gas Development on Duck Brood Abundance <a href="https://doi.org/10.1002/jwmg.21742">https://doi.org/10.1002/jwmg.21742</a></p>
<p>Assessment of repeat-visit surveys as a viable method for estimating brood abundance at the 10.4-km2 scale <a href="https://doi.org/10.1002/wsb.848">https://doi.org/10.1002/wsb.848</a></p>
<p>The file traindata.csv was used to parameterize the model while the file testdata.csv was used to test model fit. Columns are defined as follows:</p>
<ul>
<li>X & Y: Geographic location of surveyed basin (in Albers Equal Area Conic)</li>
<li>C1 & C2: Counts 1 and 2 of survey (AM and PM count)</li>
<li>scalePE & scalePE2: Quadratic, scaled percent of the basin that is obscured by emergent cover</li>
<li>LWA: scaled variable representing log of basin July wet area</li>
<li>basinWA: scaled variable representing basin July wet area</li>
<li>scaleJWA: scaled variable representing the total July wet area within 2560 acres of the surveyed basin (summarized by acre)</li>
<li>scaleMWC: scaled variable representing total count of wet basins in May within 2560 acres of surveyed basin</li>
<li>scalePC: scaled variable representing the total percent of the 2560 acres around the surveyed basin that is under perennial cover.</li>
<li>Y09:Y17: Year of the survey</li>
<li>logDate: log of date</li>
<li>SEAS & SEMI & TEMP: basin regime</li>
<li>Walkin: How the basin was surveyed (1= by truck, 0 = walkin).</li>
</ul>
<p>Software/equipment used to create/collect the data: Program R; Python; ArcGIS</p>
<p>Software/equipment used to manipulate/analyse the data: Program R; Python; ArcGIS</p>
DOI: 10.25903/devh-2h34
title: Data for: The Role of Nocturnal Fishes on Coral Reefs: A Quantitative Functional Evaluation 

description: <p>Data on the estimated biomass and productivity of different reef fish familes from various locations around Lizard Island, Great Barrier Reef, Australia.</p>
<p>Abstract [Related publication]:</p>
<ol>
<li>The ecological functions of nocturnal coral reef fishes are poorly known. Yet, nocturnal resources for consumers are theoretically as abundant and productive, if not more so, than their diurnal counterparts. In this study, we quantify and contrast the energetic dynamics of nocturnal and diurnal fishes in a model coral reef ecosystem, evaluating whether they attain similar levels of biomass production.</li>
<li>We integrated a detailed dataset of coral reef fish counts, comprising diurnal and nocturnal species, in sites sheltered and exposed to wave action. We combined somatic growth and mortality models to estimate rates of consumer biomass production, a key ecosystem function.</li>
<li>We found that diurnal fish assemblages have a higher biomass than nocturnal fishes: 104% more in sheltered sites and 271% more in exposed sites. Differences in productivity were even more pronounced, with diurnal fishes contributing 163% more productivity in sheltered locations, and 558% more in exposed locations.</li>
<li>Apogonidae dominated biomass production within the nocturnal fishes, comprising 54% of total nocturnal fish productivity, proportionally more than any diurnal fish family.</li>
<li>The substantially lower contributions of nocturnal fishes to biomass and biomass production likely indicates constraints on resource accessibility. Taxa that overcome these constraints may thrive, as evidenced by apogonids. This study highlights the importance of nocturnal fishes in underpinning the flow of energy and nutrients from nocturnal resources to reef communities; a process driven mainly by small, cryptic fishes.</li>
</ol>
<p>Research data contains both the code and the data for the publication of "The Role of Nocturnal Fishes on Coral Reefs: A Quantitative Functional Evaluation". <br />The zipped file contains word documents that explain the raw data table and the KML files used to create figures. Data was analysed using RStudio and KMLs were created using Google Earth Pro.</p>
DOI: 10.25903/zds3-dj97
title: NIRS data for elephant ivory 

description: <p>Data consists of NIRS average spectra of elephant ivory: African ivory, wild Asiana ivory and domesticated Asian ivory. </p>
<p>Software/equipment used to create/collect the data: NIR spectrometers<br />Device 1 (600 &ndash; 1100 nm) at 0.46 nm scanning resolution, interactance mode;<br />Device 2 (700 &ndash; 1200 nm) at 0.23 nm scanning resolution, interactance mode, and;<br />Device 3 (900 &ndash; 1700 nm) with scanning resolution of 2.50 nm, reflectance mode.   </p>
<p>Software/equipment used to manipulate/analyse the data: Unscrambler 11 used for data processing and analysis using Partial Least Squares Discriminant Analysis (PLS-DA).</p>
<p>The workbook includes 6 datasheets - for devices (D) 1-3 and longitudinal plane samples (LP) or cross-sectional plane (XP) samples e.g. D1_LP</p>
DOI: 10.25903/jy3s-fk04
title: Data from: Social adaptation can reduce the strength of social–ecological feedbacks from ecosystem degradation

description: <p>Data used for thesis chapter and People and Nature (PAN) journal article on Great Barrier Reef (GBR) visitor numbers and satisfaction.</p>
<p>Please refer to the thesis and article (Related publications) for further details and the full methods.</p>
<p>This dataset consists of:</p>
<ul>
<li>2 zip files containing reviews for rainforest (18 CSV files) and reef tourism (41 files) and a spreadsheet with aggregated Environmental Management Charge (EMC) data for 2010-2020 financial years (visitation, vessel, bareboat, cruiseship datasheets) saved in both MS Excel and Open Document (.ods) format.<br />These files are available via conditional access only.</li>
</ul>
<ul>
<li>summary data for rainforest and reef reviews (2 spreadsheets saved in both MS Excel (.xlsx) and Open Document (.ods) formats) and the R code for detrending analysis. <br />These files are open access and can be downloaded from the links provided.</li>
</ul>
<p style="text-align: left;">Software/equipment used to create/collect the data: Excel and R programming language</p>
<p style="text-align: left;">Software/equipment used to manipulate/analyse the data: Excel and R programming language</p>
DOI: 10.25903/dbvn-1t91
title: Understanding the motivations and barriers to adoption and effective use of connectivity technologies by SMEs in RRR Australia: Survey data

description: <p><strong>Project aims:</strong> Digital connectivity is increasingly becoming critical to not only growth, but survival, for many rural, regional and remote (RRR) businesses. For a long time, lack of reliable access has been one of the main barriers to adoption of digital connectivity technologies in RRR areas of Australia. However, in recent years internet access has improved and key emerging issues include a lack of adoption, or ineffective use of these technologies. This project will explore the human factors that influence the implementation and adoption of connectivity technologies by small and medium enterprises (SMEs) in RRR Australia.</p>
<p><strong>Project methods: </strong>The study is designed as a sequential mixed methods research project. Taking an iterative approach, quantitative and qualitative methods will be mixed over five phases to gain an understanding of the human factors that influence the adoption and implementation of connectivity technology in RRR SME. Each phase will expand on the findings from the previous phase.<br /><br />Phase 1: Exploratory Research - Co-design and Literature Review exploration of topic<br />Phase 2: Online Survey - Quantitative research<br />Phase 3: Initial Data Analysis - identify areas for further exploration<br />Phase 4: Online Focus Groups - Qualitative research to expand on findings<br />Phase 5: Detailed Data Analysis<br /><br /><strong>This dataset consists of the codebook and survey results (processed and de-identified) for Phase 2: Online Survey. </strong><br>The data is an MS Excel (.xlsx) workbook containing 2 datasheets and has also been saved in OpenDocument (.ods) format for long-term access and preservation.</p>
<p>Software/equipment used to create/collect the data: Qualtrics</p>
<p>Software/equipment used to manipulate/analyse the data: Microsoft Excel, SPSS</p>