cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22812383.v1
title: Series of photos of Golden snub-nosed monkey (Rhinopithecus roxellana)

description: Golden snub-nosed monkey (Rhinopithecus roxellana) is endangered leaf-eatingmonkeys that inhabit the northemnmost distribution of all colobines primates. Thisprimates inhabiting seasonal temperate forests in the mountainous highlands (extremelycold in winter with snow coving, very different in non-human primates) across centraand westemn China. They lived in aggregated multilevel societies comprised with largenumber of one-male, multiple female social units and bachelor groups.
DOI: 10.6073/pasta/00a943113f6915fab65db3979205f5f9
title: Water stable isotopes for streams and precipitation samples in the HJ Andrews Experimental Forest and Mary's River Watershed, 2014-2018

description: Water samples have been collected for analysis of water stable isotope (O18/16 and H2/H1) at different locations in the HJ Andrews Experimental Forest and over different periods since 2014 2014 in the HJ Andrews Experimental Forest and in the Mary's River Basin. The database includes: 1) grab samples collected over the mainstream network of the Andrews Forest on three sampling campaigns during baseflow conditions, July-September, 2015, June 2016, and September 2016. In each of the three samplings, the samples were collected at approximately 100-meter intervals in the 2nd to 5th order streams of Cold, Lookout, Mack, and McRae Creeks; 2) water samples collected in the Andrews Forest every 1-3 weeks between 2014 and 2018 in streams (Lookout Creek, Mack Creek, McRae Creek, WS01, WS02, WS07, and WS08) and bulk precipitation from benchmark meteorological stations (PRIMET, MACK, and H15MET); 3) stream and precipitation samples collected in the Andrews Forest at high temporal resolution during discrete storm events at Mack Creek in 2015; and 4) water samples collected every 2-3 weeks between 2014 and 2015 at 24 streams and 3 bulk precipitation collectors in the Marys River basin.
DOI: 10.5281/zenodo.7930556
title: Performance monitoring leveraging advanced AI technique with CNN

description: The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting time of the signal, the resolution of images, and the number of images. 2) The second step is to study damage prediction with four different stress levels. The FE model is used to simulate structural performance with different delamination conditions. Moreover, except for field test results, the artificial delamination model is created. We performed numerous finite element (FE) simulation to create inputs for CNN for damage detection. The result shows improved NDT results, and CNN can achieve structural performance prediction. We performed six tasks based on these objectives: Task 1. literature review; Task 2. Collect data from bridges; Task 3. perform filed test NDT results; Task 4. Develop FE model based on field test results; Task 5. development of a machine learning model for damage prediction.
description: Tran-SET Project: 21CUTA01
DOI: 10.5281/zenodo.7930555
title: Performance monitoring leveraging advanced AI technique with CNN

description: The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting time of the signal, the resolution of images, and the number of images. 2) The second step is to study damage prediction with four different stress levels. The FE model is used to simulate structural performance with different delamination conditions. Moreover, except for field test results, the artificial delamination model is created. We performed numerous finite element (FE) simulation to create inputs for CNN for damage detection. The result shows improved NDT results, and CNN can achieve structural performance prediction. We performed six tasks based on these objectives: Task 1. literature review; Task 2. Collect data from bridges; Task 3. perform filed test NDT results; Task 4. Develop FE model based on field test results; Task 5. development of a machine learning model for damage prediction.
description: Tran-SET Project: 21CUTA01
DOI: 10.25345/c5dz03b70
title: MassIVE MSV000091927 - GFP Pulldown from A375 cells expression microprotein-GFP candidates
title: Bottom-up mass spectrometric identification of interaction partners of candidate microproteins-GFP fusions after anti-GFP pulldown and tryptic digest Sample preparation For each of the conditions, we prepared two biological replicates. For each replicate, a confluent T75 flask of cells was pelleted for five minutes at 300 x g. Pellets were washed with PBS (Sigma-Aldrich, D8537), the suspension centrifuged for five minutes at 300 x g again and the resulting pellet flash frozen in dry ice and Methanol (VWR, 20847.307). Pellets were thawed on ice, resuspended in ice-cold RIPA buffer (50 mM Tris-HCl pH 7.6, 150 mM NaCl, 0.25 % sodium deoxycholate, 1mM EDTA, 1% NP-40) supplemented with Complete Protease inhibitor (Sigma, 5056489001) and incubated for five minutes on ice. The resulting protein extracts were centrifuged at maximum speed on a table-top centrifuge for 15 minutes at 4C and the supernatant (soluble fraction) incubated with 25 ul GFP-trap magnetic beads (ChromoTek, gtma) for four hours at 4C under constant rotation. The protein-bound beads were then washed once with RIPA buffer, twice with PBS or wash buffer (0.5 % NP-40, 0.1 mM EDTA, 20 mM Tris-HCl pH 7.4, 500 mM NaCl) and once with ddH20. Bound proteins were subsequently eluted twice for five minutes with 15 ul 1% acetic acid. Protein eluates were reduced and alkylated in the same step with 5 mM TCEP (Thermo, PG82080) and 20 mM chloroacetamide (Sigma, C0267) in 250 mM Tris buffer pH 8 for 30 minutes at room temperature. Subsequently, Sera-Mag magnetic bead mix (1:1 ratio of Sigma, GE45152105050250 and GE65152105050250) was added in a 25:1 protein to bead volume ratio and proteins were bound onto the beads by adding an equal volume (protein plus beads) of absolute ethanol. After a five minute incubation, the supernatant was removed and beads were washed three times with 80% ethanol. To perform on-bead digestion, protein-bound beads were resuspended in 50mM TEAB pH 8 (Sigma, T7408) containing 1 ug trypsin (Thermo, 90057) and incubated overnight gently shaking at 37C. Afterwards, beads were allowed to settle on the magnetic rack and the supernatant was taken and kept as the first protein eluate. Subsequently, another elution was performed by adding 50 ul 2% acetonitrile (Sigma, 34851) in 50 mM TEAB. The second elution was combined with the first one and the eluate was dried in a SpeedVac.
DOI: 10.18434/mds2-178
title: Guide to Available Mathematical Software
DOI: 10.5281/zenodo.7892204
title: Collapse of false vacuum bubbles with trapped particles - simulation data

description: This repository contains data of simulations for looking into false vacuum bubble collapses. In this scenario we assume feebly interacting particles which get trapped inside the false vacuum bubbles.
DOI: 10.5281/zenodo.7892203
title: Collapse of false vacuum bubbles with trapped particles - simulation data

description: This repository contains data of simulations for looking into false vacuum bubble collapses. In this scenario we assume feebly interacting particles which get trapped inside the false vacuum bubbles.
DOI: 10.35078/ytji4g
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Uberaba| Município: Iraí de Minas

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.18434/mds2-184
title: Matrix Market
DOI: 10.5281/zenodo.7930461
title: Datasets and configuration files for EmbDI: Embeddings for Data Integration

description: ## License ```<br> Copyright 2020 Riccardo CAPPUZZO Licensed under the Apache License, Version 2.0 (the "License");<br> you may not use this file except in compliance with the License.<br> You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software<br> distributed under the License is distributed on an "AS IS" BASIS,<br> WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.<br> See the License for the specific language governing permissions and<br> limitations under the License.<br> ``` ## EmbDI datasets The datasets contained in this directory were used while working with [EmbDI](https://gitlab.eurecom.fr/cappuzzo/embdi) on the relevant paper. Please refer to the full repository for more info. What is provided here was sourced mostly from [The Magellan Data Repository](https://sites.google.com/site/anhaidgroup/useful-stuff/data#TOC-The-Corleone-Data-Sets). For each dataset, three tables are provided: table-A and table-B are taken from the original repository and slightly modified (lower casing, spaces were replaced by `_`, some special characters were removed), while the third table is the concatenation of tables A and B. ## Edgelists Edgelists are the data structures used by EmbDI. They are generated starting from each concatenated dataset and are then fed to the algorithm. ## EQ tests The `EQ tests` folder contains all the tests used to perform the Embeddings Quality evaluation in the paper. The additional resources include: * (partially preprocessed) base datasets. * Their Entity Resolution and Schema Matching versions. * Edgelists for both ER and SM versions. * Ground truth files for ER and SM tasks. * Test directories for the EQ task. * Copies of the configuration files provided in this repository. Configuration files, info files and ER match files were left in this repository in `pipeline/config_files/default`, `pipeline/info` and `pipeline/matches/default`. <br>
DOI: 10.5281/zenodo.7930460
title: Datasets and configuration files for EmbDI: Embeddings for Data Integration

description: ## License ```<br> Copyright 2020 Riccardo CAPPUZZO Licensed under the Apache License, Version 2.0 (the "License");<br> you may not use this file except in compliance with the License.<br> You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software<br> distributed under the License is distributed on an "AS IS" BASIS,<br> WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.<br> See the License for the specific language governing permissions and<br> limitations under the License.<br> ``` ## EmbDI datasets The datasets contained in this directory were used while working with [EmbDI](https://gitlab.eurecom.fr/cappuzzo/embdi) on the relevant paper. Please refer to the full repository for more info. What is provided here was sourced mostly from [The Magellan Data Repository](https://sites.google.com/site/anhaidgroup/useful-stuff/data#TOC-The-Corleone-Data-Sets). For each dataset, three tables are provided: table-A and table-B are taken from the original repository and slightly modified (lower casing, spaces were replaced by `_`, some special characters were removed), while the third table is the concatenation of tables A and B. ## Edgelists Edgelists are the data structures used by EmbDI. They are generated starting from each concatenated dataset and are then fed to the algorithm. ## EQ tests The `EQ tests` folder contains all the tests used to perform the Embeddings Quality evaluation in the paper. The additional resources include: * (partially preprocessed) base datasets. * Their Entity Resolution and Schema Matching versions. * Edgelists for both ER and SM versions. * Ground truth files for ER and SM tasks. * Test directories for the EQ task. * Copies of the configuration files provided in this repository. Configuration files, info files and ER match files were left in this repository in `pipeline/config_files/default`, `pipeline/info` and `pipeline/matches/default`. <br>
DOI: 10.5683/sp2/rncawq
title: Total Column Mixing Ratios of Carbon Dioxide, Methane and Carbon Monoxide from Toronto (ta)

description: Column-averaged dry-air mole fractions (DMF) of trace gases CO2, CH4 and CO were collected using a Bruker Optics EM27/SUN Fourier Transform Spectrometer (FTS) at the University of Toronto, St George Campus. The data were processed using GGG2020.
DOI: 10.6084/m9.figshare.21802704.v2
title: Dataset and R code

description: Dataset and R code for project: <strong>Wild bumblebees’ recent foraging experience affects nectar acceptance and flower visitation</strong>
DOI: 10.7284/149579
title: Multiplexed data collected during research cruise AT45 using a WHOI dsLog instrument system onboard the platform RV Atlantis

description: Processed data created by vessel technician during a Field_expedition
DOI: 10.18434/mds2-1446
title: Parallel Hierarchical Adaptive Multilevel Project (PHAML)
DOI: 10.7284/149576
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AT45 using a Hawaii UHDAS instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149581
title: Fluorometer data collected during research cruise AT45 using a WETLabs WETStar instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149575
title: GNSS (Global Navigation Satellite System) data collected during research cruise AT45 using a CCTech C-Nav 3050 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149578
title: Gravimeter data collected during research cruise AT45 using a Bell BGM-3 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149585
title: INS (Inertial Navigation System) data collected during research cruise AT45 using a iXblue Phins Surface instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149582
title: Meteorological Station data collected during research cruise AT45 using a Vaisala WXT520 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149583
title: Meteorological Station data collected during research cruise AT45 using a Vaisala WXT520 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149586
title: Multibeam Echosounder data collected during research cruise AT45 using a Kongsberg EM124 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149577
title: Singlebeam Echosounder data collected during research cruise AT45 using a Knudsen 3260 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149587
title: Speedlog data collected during research cruise AT45 using a Furuno DS-60 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149588
title: Splitbeam Echosounder data collected during research cruise AT45 using a Kongsberg EK80 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149584
title: Water Temperature data collected during research cruise AT45 using a SeaBird SBE-48 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/149601
title: TSG (Thermosalinograph) data collected during research cruise AT45 using a SeaBird SBE-45 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.22812383
title: Series of photos of Golden snub-nosed monkey (Rhinopithecus roxellana)

description: Golden snub-nosed monkey (Rhinopithecus roxellana) is endangered leaf-eatingmonkeys that inhabit the northemnmost distribution of all colobines primates. Thisprimates inhabiting seasonal temperate forests in the mountainous highlands (extremelycold in winter with snow coving, very different in non-human primates) across centraand westemn China. They lived in aggregated multilevel societies comprised with largenumber of one-male, multiple female social units and bachelor groups.
DOI: 10.6084/m9.figshare.22812383.v3
title: Series of photos of Golden snub-nosed monkey (Rhinopithecus roxellana)

description: Golden snub-nosed monkey (Rhinopithecus roxellana) is endangered leaf-eatingmonkeys that inhabit the northemnmost distribution of all colobines primates. Thisprimates inhabiting seasonal temperate forests in the mountainous highlands (extremelycold in winter with snow coving, very different in non-human primates) across centraand westemn China. They lived in aggregated multilevel societies comprised with largenumber of one-male, multiple female social units and bachelor groups.
DOI: 10.6084/m9.figshare.22812383.v2
title: Series of photos of Golden snub-nosed monkey (Rhinopithecus roxellana)

description: Golden snub-nosed monkey (Rhinopithecus roxellana) is endangered leaf-eatingmonkeys that inhabit the northemnmost distribution of all colobines primates. Thisprimates inhabiting seasonal temperate forests in the mountainous highlands (extremelycold in winter with snow coving, very different in non-human primates) across centraand westemn China. They lived in aggregated multilevel societies comprised with largenumber of one-male, multiple female social units and bachelor groups.
DOI: 10.5285/0be5339c-9d35-44c9-a10f-da4b5356840b
title: High resolution vector polygons of the Antarctic coastline - VERSION 7.7

description: Coastline for Antarctica created from various mapping and remote sensing sources, provided as polygons with 'land', 'ice shelf', 'ice tongue' or 'rumple' attribute. Covering all land and ice shelves south of 60S. Suitable for topographic mapping and analysis. High resolution versions of ADD data are suitable for scales larger than 1:1,000,000. The largest suitable scale is changeable and dependent on the region. Changes in v7.7 include updates Brunt Ice Shelf, ice fronts on the east side of the Antarctic Peninsula, Pine Island and Thwaites Glaciers and the ice fronts and coastline around Charcot Island. Data quality checks were also performed to ensure existing features were categorised correctly around Getz Ice Shelf. Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.
description: Dataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each polygon contains a 'surface' attribute with either 'land', 'ice shelf', 'ice tongue' or 'rumple'. Details of when and how each line was created can be found in the attributes of the high resolution polyline coastline dataset. Data sources range in time from 1990s-2023 - individual lines contain exact source dates.
DOI: 10.5285/cc111b81-5066-42dd-be60-9f05abcb5873
title: Medium resolution vector polygons of the Antarctic coastline - VERSION 7.7

description: Coastline for Antarctica created from various mapping and remote sensing sources, provided as polygons with 'land', 'ice shelf', 'ice tongue' or 'rumple' attribute. Covering all land and ice shelves south of 60S. Suitable for topographic mapping and analysis. This dataset has been generalised from the high resolution vector polygons. Medium resolution versions of ADD data are suitable for scales smaller than 1:1,000,000, although certain regions will appear more detailed than others due to variable data availability and coastline characteristics. Changes in v7.7 include updates Brunt Ice Shelf, ice fronts on the east side of the Antarctic Peninsula, Pine Island and Thwaites Glaciers and the ice fronts and coastline around Charcot Island. Data quality checks were also performed to ensure existing features were categorised correctly around Getz Ice Shelf. Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research.
description: Dataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry checks, but does not contain strict topology. Quality varies across the dataset and certain areas where high resolution source data were available are suitable for large scale maps whereas other areas are only suitable for smaller scales. Each polygon contains a 'surface' attribute with either 'land', 'ice shelf', 'ice tongue' or 'rumple'. Details of when and how each line was created can be found in the attributes of the high or medium resolution polyline coastline dataset. Data sources range in time from 1990s-2023 - individual lines contain exact source dates. This medium resolution version has been generalised from the high resolution version. All polygons inferior to 0.1km2 not intersecting anything else were deleted and the 'simplify' tool was used in ArcGIS with the 'point remove' algorithm and a smoothing tolerance of 50 m.