cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19410154.v1
title: Co-production of climate change vulnerability assessment : A case study of the Indian Lesser Himalayan region, Darjeeling

description: The intensity and extent of climate change impacts differ significantly with the geographical and ecological structure of the landscape. This is especially evident in mountain ecosystems where topographic, climatic and biological gradients make them extremely vulnerable to global environmental changes . Designing strategies to mitigate and adapt to global climate change on such local landscapes requires a context-specific vulnerabilities that take into account their particular characteristics. Presently, there are two main challenges in assessing climate change vulnerability in mountain ecosystems: 1) The models that are used for vulnerability assessments at global scales are being used at local scales with broad variables from few sectors that do not capture the range of characteristics of mountain ecosystems 2) indigenous knowledge about climate change are not considered in these models, which makes the implementation of mitigation/adaptation measures less successful. In this study, we highlight these issues drawing from our data collected in India’s Lesser Himalayan region (Darjeeling). We used a mixed research approach that combines a vulnerability assessment model with a participatory knowledge approach. We based climate change vulnerability around the socio-ecological system of the mountain landscape. The results from the interactive process showed that Darjeeling region is experiencing higher climate change vulnerability than the results produced by the model at the subregional level. We highlight critical variables that influence the socio-ecological system and need to be taken into account when assessing vulnerability and future adaptation scenarios. The study offers a decision support process for policymakers to plan climate mitigation/adaptation measures and future sustainability pathways.
DOI: 10.6084/m9.figshare.20472292.v2
title: Addressing climate goals and the SDGs through a just energy transition? Empirical evidence from Germany and South Africa

description: In striving to achieve the Paris Agreement and the 2030 Agenda, governments have the opportunity to implement their climate and sustainability goals more coherently. Such coherence requires the coordination of interdependent policies across different policy fields, sectors and actors. This paper explores how governments design and implement synergic solutions to concomitantly achieve both international agendas. With the empirical cases of Germany and South Africa, we investigate two independent approaches to the synergic solution of a just energy transition, whereby countries aim to phase out coal as a means to tackle climate change while also ensuring that the achievement of other Sustainable Development Goals (SDGs) is not hindered. Methodologically, we apply a deductive qualitative approach consisting of literature review, semi-structured interviews, and content analysis. To that end, we examine relevant policies and institutional arrangements by applying a combined conceptual framework of energy justice and just transition in both countries. We find major challenges in overcoming environmental, economic and social burdens of the coal phase-out, especially related to jobs and inequality (SDGs 8, 10) and the Water-Energy-Food-Land nexus (SDGs 2, 6, 7, 15). Through the selection of Germany and South Africa, we illustrate how countries with different political, social and economic backgrounds strive to manage such a transition. Our developed framework and case-studies’ findings point towards important considerations when designing just energy transition pathways, such as ensuring inclusiveness in decision-making, thoroughly assessing social, economic and environmental impacts, and adequately coordinating across different actors and the local, provincial and national levels.
DOI: 10.17638/datacat.liverpool.ac.uk/1960
title: Single-molecule conductance data for a set of molecular wires and their host-guest complexes with a pillar[5]arene

description: As in the title. Structures of interest and data analysis software are included in this submission.
DOI: 10.6084/m9.figshare.21657806
title: High-resolution analyses of associations between medications, microbiome and mortality in cancer patients

description: Supplementary data files for a study investigating the associations between medication, microbiome and mortality in cancer patients. <br> <strong>Summary</strong> Discerning the effect of pharmacological exposures on intestinal bacterial communities in cancer patients is challenging. Here, we deconvoluted the relationship between drug exposures and changes in microbial composition by developing and applying a new computational method, PARADIGM (PARameters Associated with DynamIcs of Gut Microbiota), to a large set of longitudinal fecal microbiome profiles with detailed medication-administration records from patients undergoing allogeneic hematopoietic cell transplantation. We observed that several non-antibiotic drugs, including laxatives, antiemetics, and opioids, are associated with increased <em>Enterococcus</em> relative abundance and decreased alpha-diversity. Shotgun metagenomic sequencing further demonstrated subspecies competition, leading to increased dominant strain genetic convergence during allo-HCT that is significantly associated with antibiotic exposures. We integrated drug-microbiome associations to predict clinical outcomes in two validation cohorts on the basis of drug exposures alone, suggesting that this approach can generate biologically and clinically relevant insights on how pharmacological exposures can perturb or preserve microbiota composition.
DOI: 10.6084/m9.figshare.6653393.v3
title: Plant Diversity of the Tropical Research and Education Center, University of Florida, Homestead

description: This dataset (`TRECflora.csv`) and accompanying metadata file (`TRECflora-metadata.doc`) were first presented in the 2018 Proceedings of the Florida State Horticultural Society and updated April 2019.<br><br><i>Abstract</i>. The Tropical Research and Education Center (TREC) is the southernmost campus of the University of Florida and is in Miami-Dade County, FL. TREC is dedicated to research, teaching, and extension in natural resources, agriculture, and ornamental, vegetable, fruit, and biofuel crops. As part of this mission, a large diversity of cultivated plants was established at TREC, while also preserving native pine rockland and rockdale hammock. The mission of TREC was recently expanded to include agroecology, which investigates the production, diversity, and resilience of agricultural systems relative to surrounding areas. Identifying TREC plant species is a first step in establishing this context for agricultural systems. Although many partial lists of vascular plant species have been made for TREC, there has been no comprehensive list. We report a comprehensive inventory of plant species diversity completed over the last decade (`TRECflora.csv`). Each plant species was identified with botanical keys and confirmed with online specimens or a previous report from a trusted source. The inventory currently has 707 species including 240 native; 237 established, non-native; 190 non-established, cultivated; and 40 unknowns. We gathered detailed information for each species identified, including plant use, general habitat, weediness, specific location, and identification sources other than the authors. This data will support agroecological research into the effects of plant diversity and surrounding areas on crop physiology and production.
DOI: 10.3886/icpsr38560.v1
title: College and Beyond II (CBII) Contextual Data, [United States], 2004-2021
title: Version 1

description: The College and Beyond II (CBII) Contextual Data Study contains publicly available data on colleges and universities from the Integrated Postsecondary Education Data System (IPEDS) and publicly available data on the socioeconomic characteristics of neighborhoods from the National Neighborhood Data Archive (NaNDA). CBII did not collect this data; rather, this data is made available in the ICPSR Virtual Data Enclave for the convenience of restricted data users who may want to link this contextual data to other studies in the CBII study series. IPEDS can be merged with any CBII file that contains a variable whose values are UnitIDs. NaNDA can be merged with any CBII file that contains a variable whose values are Zip Codes. If you use the NaNDA data in your work, please cite the data using the following: Melendez, Robert, Clarke, Philippa, Khan, Anam, Gomez-Lopez, Iris, Li, Mao, and Chenoweth, Megan. National Neighborhood Data Archive (NaNDA): Socioeconomic Status and Demographic Characteristics of ZIP Code Tabulation Areas, United States, 2008-2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-07-30. https://doi.org/10.3886/E120462V1.
description: To construct the NaNDA dataset, researchers extracted key census indicators related to race, ethnicity, age, income level, employment, poverty, and home ownership from the ACS 2012 five-year estimate (covering 2008-2012). They merged the variables with the same variables from the ACS 2017 five-year estimate (covering 2013-2017) and with each ZCTA's land area from the 2010 TIGER/Line shapefiles for ZIP code tabulation areas. They then used those variables to construct three indices: neighborhood disadvantage, neighborhood affluence, and ethnic immigrant concentration.
description: Datasets: DS0: Study-Level Files DS1: IPEDS DS2: NaNDA
description: IPEDS: Every college, university, and technical/vocational institution that participates in the federal student financial aid programs (Title IV-eligible institutions) mandatorily reports data to IPEDS. Institutions that are not eligible for participation in Title IV may request to be included. There is no information on what percentage of non-eligible institutions participate in IPEDS. NaNDA: The NaNDA data cover all U.S. Zip Codes except those that are large unpopulated places. Zip Codes assigned to businesses only or single delivery point address will not necessarily appear as Zip Code Tabulation Areas (ZCTAs). Please see the Census documentation for further information.. Smallest Geographic Unit: Country
description: For IPEDS, there is a zip file for each year that contains all survey modules and a corresponding data dictionary for the given year.All the NaNDA data is in one file, and it is not separated by year.
DOI: 10.3886/icpsr38560
title: College and Beyond II (CBII) Contextual Data, [United States], 2004-2021
title: Archival Version

description: The College and Beyond II (CBII) Contextual Data Study contains publicly available data on colleges and universities from the Integrated Postsecondary Education Data System (IPEDS) and publicly available data on the socioeconomic characteristics of neighborhoods from the National Neighborhood Data Archive (NaNDA). CBII did not collect this data; rather, this data is made available in the ICPSR Virtual Data Enclave for the convenience of restricted data users who may want to link this contextual data to other studies in the CBII study series. IPEDS can be merged with any CBII file that contains a variable whose values are UnitIDs. NaNDA can be merged with any CBII file that contains a variable whose values are Zip Codes. If you use the NaNDA data in your work, please cite the data using the following: Melendez, Robert, Clarke, Philippa, Khan, Anam, Gomez-Lopez, Iris, Li, Mao, and Chenoweth, Megan. National Neighborhood Data Archive (NaNDA): Socioeconomic Status and Demographic Characteristics of ZIP Code Tabulation Areas, United States, 2008-2017. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-07-30. https://doi.org/10.3886/E120462V1.
description: To construct the NaNDA dataset, researchers extracted key census indicators related to race, ethnicity, age, income level, employment, poverty, and home ownership from the ACS 2012 five-year estimate (covering 2008-2012). They merged the variables with the same variables from the ACS 2017 five-year estimate (covering 2013-2017) and with each ZCTA's land area from the 2010 TIGER/Line shapefiles for ZIP code tabulation areas. They then used those variables to construct three indices: neighborhood disadvantage, neighborhood affluence, and ethnic immigrant concentration.
description: Datasets: DS0: Study-Level Files DS1: IPEDS DS2: NaNDA
description: IPEDS: Every college, university, and technical/vocational institution that participates in the federal student financial aid programs (Title IV-eligible institutions) mandatorily reports data to IPEDS. Institutions that are not eligible for participation in Title IV may request to be included. There is no information on what percentage of non-eligible institutions participate in IPEDS. NaNDA: The NaNDA data cover all U.S. Zip Codes except those that are large unpopulated places. Zip Codes assigned to businesses only or single delivery point address will not necessarily appear as Zip Code Tabulation Areas (ZCTAs). Please see the Census documentation for further information.. Smallest Geographic Unit: Country
description: For IPEDS, there is a zip file for each year that contains all survey modules and a corresponding data dictionary for the given year.All the NaNDA data is in one file, and it is not separated by year.
DOI: 10.6084/m9.figshare.21710870
title: qPCR raw data of M.abscessus KOs

description: raw Ct reads from RT-qPCR experiments, as described in the methods section of the original research article.
DOI: 10.6084/m9.figshare.21710870.v1
title: qPCR raw data of M.abscessus KOs

description: raw Ct reads from RT-qPCR experiments, as described in the methods section of the original research article.
DOI: 10.6084/m9.figshare.21710888
title: NGS raw reads from M. abscessus KOs

description: Untrimmed raw reads of deletion mutants generated for the original research article. <br> WT - wild type K - ppk1 KO X - ppx1 KO K1X1 - ppk1ppx1 KO K2 - ppk2 KO K1K2 - ppk1ppk2 KO
DOI: 10.6084/m9.figshare.21710888.v1
title: NGS raw reads from M. abscessus KOs

description: Untrimmed raw reads of deletion mutants generated for the original research article. <br> WT - wild type K - ppk1 KO X - ppx1 KO K1X1 - ppk1ppx1 KO K2 - ppk2 KO K1K2 - ppk1ppk2 KO
DOI: 10.6084/m9.figshare.20246763.v2
title: Tables_NMDS_Figs

description: Data files and R code used to create tables and NMDS figures
DOI: 10.17029/1c50f81b-dfe0-4701-8ee2-5920086a001d
title: Dataset for 'Ego-relevance in team production'.

description: This dataset accompanies the publication of "Ego-relevance in team production" by Mantilla and Murad published in PLoS ONE.
DOI: 10.5281/zenodo.7351622
title: Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset

description: <strong>Overview:</strong> This repository contains the <strong>raw images</strong> for the dataset of <strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong>. This dataset contains <strong>5696 drone images</strong> (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times. All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings. <strong>Usage:</strong> Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal: <pre><code>├── Flug_100/ │ ├── RGB/ │ │ ├── DJI_0004.jpg │ │ ├── DJI_0006.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0003_R.JPG │ ├── DJI_0005_R.JPG │ └── ... ├── Flug_101/ │ ├── RGB/ │ │ ├── DJI_0001.jpg │ │ ├── DJI_0003.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0000_R.JPG │ ├── DJI_0002_R.JPG │ └── ... └── ...</code></pre> <strong>File Numbering/Naming Scheme:</strong> The pairs of RGB + Thermal images follow the simple numbering scheme of: <strong>RGB = Thermal + 1</strong>.<br> For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image. To perform the merging, we recommend using the <strong>merge_image_layers.py</strong> script provided by the associated <strong>TBBRDet software</strong> (see the scripts/alignment/ directory). For convenience, we have provided a CSV listing all annotated images in the <strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong>. The CSV format is as follows: <pre><code>Flight,RGB,Thermal Flug_100,DJI_0048.jpg,DJI_0047_R.JPG Flug_100,DJI_0050.jpg,DJI_0049_R.JPG ...</code></pre>
DOI: 10.5281/zenodo.7351623
title: Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset

description: <strong>Overview:</strong> This repository contains the <strong>raw images</strong> for the dataset of <strong>Thermal Bridges on Building Rooftops (TBBR dataset)</strong>. This dataset contains <strong>5696 drone images</strong> (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times. All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings. <strong>Usage:</strong> Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal: <pre><code>├── Flug_100/ │ ├── RGB/ │ │ ├── DJI_0004.jpg │ │ ├── DJI_0006.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0003_R.JPG │ ├── DJI_0005_R.JPG │ └── ... ├── Flug_101/ │ ├── RGB/ │ │ ├── DJI_0001.jpg │ │ ├── DJI_0003.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0000_R.JPG │ ├── DJI_0002_R.JPG │ └── ... └── ...</code></pre> <strong>File Numbering/Naming Scheme:</strong> The pairs of RGB + Thermal images follow the simple numbering scheme of: <strong>RGB = Thermal + 1</strong>.<br> For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image. To perform the merging, we recommend using the <strong>merge_image_layers.py</strong> script provided by the associated <strong>TBBRDet package</strong> (see the scripts/alignment/ directory).
DOI: 10.5281/zenodo.7360996
title: Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset

description: <strong>Overview:</strong> This repository contains the <strong>raw images</strong> for the dataset of <strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong>. This dataset contains <strong>5696 drone images</strong> (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times. All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings. <strong>Usage:</strong> Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal: <pre><code>├── Flug_100/ │ ├── RGB/ │ │ ├── DJI_0004.jpg │ │ ├── DJI_0006.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0003_R.JPG │ ├── DJI_0005_R.JPG │ └── ... ├── Flug_101/ │ ├── RGB/ │ │ ├── DJI_0001.jpg │ │ ├── DJI_0003.jpg │ │ └── ... │ └── Thermal/ │ ├── DJI_0000_R.JPG │ ├── DJI_0002_R.JPG │ └── ... └── ...</code></pre> <strong>File Numbering/Naming Scheme:</strong> The pairs of RGB + Thermal images follow the simple numbering scheme of: <strong>RGB = Thermal + 1</strong>.<br> For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image. To perform the merging, we recommend using the <strong>merge_image_layers.py</strong> script provided by the associated <strong>TBBRDet software</strong> (see the scripts/alignment/ directory). For convenience, we have provided a CSV listing all annotated images in the <strong>Thermal Bridges on Building Rooftops (TBBR) dataset</strong>. The CSV format is as follows: <pre><code>Flight,RGB,Thermal Flug_100,DJI_0048.jpg,DJI_0047_R.JPG Flug_100,DJI_0050.jpg,DJI_0049_R.JPG ...</code></pre>
DOI: 10.6084/m9.figshare.20246661.v2
title: Stacked_IRI_Fig

description: Data files and R code used to create stacked IRI plot