cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e104642
title: 2016 Annual Survey of Refugees

description: Since the 1980s, the Office of Refugee Resettlement[1] (ORR) has conducted the Annual Survey of Refugees (ASR), which collects information on refugees during their first five years after arrival in the U.S. The ASR is the only scientifically-collected source of national data on refugees’ progress toward self-sufficiency and integration. ORR uses the ASR results alongside other information sources to fulfill its Congressionally-mandated reporting following the Refugee Act of 1980. Historically, the microdata from these surveys have generally been unavailable to researchers. <br> <br>In the Spring of 2017 ORR completed its 50th Annual Survey of Refugees (ASR). The data from the ASR offer a window into respondents’ first five years in the United States and shows the progress that refugee families made towards learning English, participating in the workforce, and establishing permanent residence. This public use data deposit is only for the 2016 ASR with future years likely to be added to the ICPSR archive.. <br> [1] The Office of Refugee Resettlement (ORR) at the Administration for Children and Families in the U.S. Department of Health and Human Services (HHS) serves refugees and other humanitarian entrants, including asylees, Cuban and Haitian entrants, Special Immigrant Visa holders, Amerasians, victims of human trafficking, and unaccompanied children. By providing these arrived populations with critical resources, ORR promotes their economic and social well-being. The Annual Survey of Refugees focuses solely on those refugees who have come to the U.S. in the past five fiscal years.
description: Household- and person-level analytic weights were developed for the 2016 ASR to allow for valid statistical estimates of the target refugee population. Both sets of weights are comprised of two components – a base weight reflecting the selection probability and an adjustment that corrects for differential nonresponse and aligns the population to known totals from the sampling frame (RADS universe file).
description: Response Rates: An overall response rate of 24 percent was achieved. The response rate was driven by the ability to locate and speak to (1500+468)/6176 = 32 percent of the sample, meaning that two thirds of the sample could either not be located, or (if located) could not be successfully contacted. The overall response rates decreased with time since arrival to the U.S., varying from 20 percent for FY 2011-2012 refugees to 25 percent for FY 2013-2014 refugees and a high of 31 percent for FY 2015 refugees.
description: telephone interview~~
description: Refugees aged 16 years old or over at the time of interview who arrived in the U.S. during FY 2011-2015 Smallest Geographic Unit: Census region originally resettled in and the country of birth
description: The 2016 ASR employed a stratified probability sample design of refugees. The first stage of selection was the household (PA), and the second stage was the selection of persons within households.
DOI: 10.3886/e104642v3-12865
title: 2016 Annual Survey of Refugees

description: Since the 1980s, the Office of Refugee Resettlement[1] (ORR) has conducted the Annual Survey of Refugees (ASR), which collects information on refugees during their first five years after arrival in the U.S. The ASR is the only scientifically-collected source of national data on refugees’ progress toward self-sufficiency and integration. ORR uses the ASR results alongside other information sources to fulfill its Congressionally-mandated reporting following the Refugee Act of 1980. Historically, the microdata from these surveys have generally been unavailable to researchers. <br> <br>In the Spring of 2017 ORR completed its 50th Annual Survey of Refugees (ASR). The data from the ASR offer a window into respondents’ first five years in the United States and shows the progress that refugee families made towards learning English, participating in the workforce, and establishing permanent residence. This public use data deposit is only for the 2016 ASR with future years likely to be added to the ICPSR archive.. <br> [1] The Office of Refugee Resettlement (ORR) at the Administration for Children and Families in the U.S. Department of Health and Human Services (HHS) serves refugees and other humanitarian entrants, including asylees, Cuban and Haitian entrants, Special Immigrant Visa holders, Amerasians, victims of human trafficking, and unaccompanied children. By providing these arrived populations with critical resources, ORR promotes their economic and social well-being. The Annual Survey of Refugees focuses solely on those refugees who have come to the U.S. in the past five fiscal years.
description: Household- and person-level analytic weights were developed for the 2016 ASR to allow for valid statistical estimates of the target refugee population. Both sets of weights are comprised of two components – a base weight reflecting the selection probability and an adjustment that corrects for differential nonresponse and aligns the population to known totals from the sampling frame (RADS universe file).
description: Response Rates: An overall response rate of 24 percent was achieved. The response rate was driven by the ability to locate and speak to (1500+468)/6176 = 32 percent of the sample, meaning that two thirds of the sample could either not be located, or (if located) could not be successfully contacted. The overall response rates decreased with time since arrival to the U.S., varying from 20 percent for FY 2011-2012 refugees to 25 percent for FY 2013-2014 refugees and a high of 31 percent for FY 2015 refugees.
description: telephone interview~~
description: Refugees aged 16 years old or over at the time of interview who arrived in the U.S. during FY 2011-2015 Smallest Geographic Unit: Census region originally resettled in and the country of birth
description: The 2016 ASR employed a stratified probability sample design of refugees. The first stage of selection was the household (PA), and the second stage was the selection of persons within households.
DOI: 10.5281/zenodo.3361833
title: DWD European Weather 2

description: opendata.dwd.de - OpenData by Deutscher Wetter Dienst Conditions: https://www.dwd.de/EN/service/copyright/copyright_node.html dates and times are UTC.
DOI: 10.5281/zenodo.3361827
title: FoodEx2vec: Poincaré Embeddings of a FoodEx2 Hierarchy for Advanced Food Data Analysis

description: A dataset consisting of Poincaré embeddings of the FoodEx2 hierarchy, as well as the clustering results obtained with the Poincaré embeddings. It consists of two folders: foodex2vec_clusters - where the clustering using the Poincaré embeddings can be seen; foodex2vec_poincare_embeddings - the actual Poincaré embeddings with different dimensions.
description: This work was supported by the Slovenian Research AgencyProgram P2-0098; and the European Union's Horizon 2020 research andinnovation programme [grant agreements No. 769661 and No. (FNS-Cloud)].
DOI: 10.5281/zenodo.3361828
title: FoodEx2vec: Poincaré Embeddings of a FoodEx2 Hierarchy for Advanced Food Data Analysis

description: A dataset consisting of Poincaré embeddings of the FoodEx2 hierarchy, as well as the clustering results obtained with the Poincaré embeddings. It consists of two folders: foodex2vec_clusters - where the clustering using the Poincaré embeddings can be seen; foodex2vec_poincare_embeddings - the actual Poincaré embeddings with different dimensions.
description: This work was supported by the Slovenian Research AgencyProgram P2-0098; and the European Union's Horizon 2020 research andinnovation programme [grant agreements No. 769661 and No. (FNS-Cloud)].
DOI: 10.26300/wk90-an66
title: Olivine-Olivine.RHIT
DOI: 10.7910/dvn/w3ukpw/o3cp91
title: Oerter_Metaschoepite_Data2019_vFormatedForRepository.pdf

description: :unav
DOI: 10.6073/pasta/6264baad5fa8d54044803e89b3bb7bc6
title: Streamside mosses at the Andrews Experimental Forest, 1994

description: This data set describes basic description of bryophyte communities in riparian forests, relations between community patterns and major environmental gradients, and compares the local microsite factors with site level influences.
DOI: 10.5281/zenodo.3361844
title: Measuring similarity between gene interaction profiles

description: This repository contains the following: Original genetic interaction score matrices for the genes. Also contains a binarized version of this data based on a threshold of 0.05, for use in the binary distance measures. The distance measure matrices that were calculated based on the genetic interaction score matrices; Pearson, Maryland bridge, Ochiai, and Braun-Blanquet matrices for the one-square case and the same for the two-squares case. Lists of all the genes used. Lists of modules for each of the eight distance measure matrices, with the genes for each. Summary statistics for the two-squares distance matrices, network and module summary statistics for the two-squares modules, and stats on how many modules for each of the eight different module clusterings could be mapped to subnetworks found in Tong et al. (reference 14 in the paper). A figure containing distribution of distance measure values for the two-square distance matrices. View the readme file for full details.
DOI: 10.18434/m32082
title: Characterization of zinc carboxylates on an oil paint test panel [dataset]

description: This dataset contains X-ray EDS (energy dispersive spectroscopy), FTIR (Fourier transform infrared spectroscopy), and DART-MS (Direct analysis in real time mass spectrometry) data and analysis files thatcan be used to reproduce the results from the paper:Christine P. Romano, Thomas Lam, G. Asher Newsome, Joshua A. Taillon, Nicole C. Little, and Jia-sun Tsang. "Characterization of zinc carboxylates on an oil paint test panel," Studies in Conservation, (2019). [in press]For a detailed description of the files contained within this dataset, please extract the "README.zip" file into the root of the dataset folder, and open the "README.html" that is placed in the root folder afterwards. This top-level file can be opened with any web browser, and contains links to all the README files in the folders within the dataset. Each of those files contains a more complete description of the data contained within.
DOI: 10.26300/pf0p-mq33
title: Supplementary Table A
DOI: 10.26300/38vz-s216
title: Supplementary Table B
DOI: 10.5281/zenodo.3361812
title: query suggestion with siri - query suggestions for instagram accounts

description: Das Datenset enthält Query Suggestions zu 20 Instragram-Accounts bzw. Personen oder Firmen dahinter. (Instagram, Cristiano Ronaldo, Ariana Grande, Selena Gomez, The Rock, Kim Kardashian West, Kylie Jenner, Beyoncé, Taylor Swift, Leo Messi, Neymar jr, Kendall Jenner, Justin Bieber, National Geographic, Barbie, Khloe Kardashian, Jennifer Lopez, Miley Cyrus, Nike, Katy Perry) Die Datenerhebung erfolgte vom 26.05.2019 - 23.06.2019. Enthalten sind Vorschläge der Suchmaschinen Bing, DuckDuckGo, Google und der Suche in Siri (an einem Tablet und in einer XCode Simulation), wobei die Anzahl der zurückgelieferten Vorschläge variiert. Das Datenset enthält folgende Spalten: Plattform, Suchbegriff, Datum, Vorschlag Position
DOI: 10.5281/zenodo.3361813
title: query suggestion with siri - query suggestions for instagram accounts

description: Das Datenset enthält Query Suggestions zu 20 Instragram-Accounts bzw. Personen oder Firmen dahinter. (Instagram, Cristiano Ronaldo, Ariana Grande, Selena Gomez, The Rock, Kim Kardashian West, Kylie Jenner, Beyoncé, Taylor Swift, Leo Messi, Neymar jr, Kendall Jenner, Justin Bieber, National Geographic, Barbie, Khloe Kardashian, Jennifer Lopez, Miley Cyrus, Nike, Katy Perry) Die Datenerhebung erfolgte vom 26.05.2019 - 23.06.2019. Enthalten sind Vorschläge der Suchmaschinen Bing, DuckDuckGo, Google und der Suche in Siri (an einem Tablet und in einer XCode Simulation), wobei die Anzahl der zurückgelieferten Vorschläge variiert. Das Datenset enthält folgende Spalten: Plattform, Suchbegriff, Datum, Vorschlag Position
DOI: 10.26186/5d4a3bf30c93d
title: Beagle Marine Park backscatter data 2018

description: This resource includes multibeam sonar backscatter data for Beagle Marine Park (Bass Strait) collected by Geoscience Australia (GA) and the Institute for Marine & Antarctic Studies (University of Tasmania; UTAS) during the period 17 – 26 June 2018 on the RV Bluefin. The survey was undertaken as a collaborative project funded through the National Environmental Science Program Marine Biodiversity Hub, with co-investment by GA and UTAS. The purpose of the project was to build baseline information for benthic habitats in the Beagle Marine Park that will support ongoing environmental monitoring within the South-east Marine Park Network as part of the 10-year management plan (2013-2023). Data acquisition for the project was completed during three separate voyages: Phase 1 - Seabed mapping by multibeam sonar; Phase 2 – Seabed imagery acquisition by Autonomous Underwater Vehicle, and sediment sampling; Phase 3 – Survey of demersal fish communities using Baited Remote Underwater Video (BRUVs). This dataset from Phase 1 comprises 11 backscatter grids derived from multibeam sonar data gridded at 1 m spatial resolution, covering a combined area of 364 km2. A detailed report on the survey is provided in: Falster, G., Monk, J., Carroll, A., Siwabessy, J., Deane, A., Picard, K., Dando, N., Hulls, J., Nichol, S., Barrett, N. 2019. Australian Marine Park Baseline and Monitoring Survey: Post Survey Report, Beagle Marine Park, South-east Marine Park Network. Report to the National Environmental Science Program, Marine Biodiversity Hub.
DOI: 10.21227/sk74-7419
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation
DOI: 10.21227/g1vc-k485
title: The USyd Campus Dataset

description: Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation and deep-learning applications. Despite this success, the validation of algorithm robustness has recently been recognised as a major challenge for the massive deployment of these new technologies. It is well known that algorithms and models trained or tested with a particular dataset tend not to generalise well for other scenarios. For certain applications, autonomous vehicles in particular, algorithm failures can lead to potentially catastrophic consequences. To minimise this risk, it is essential to evaluate the robustness and reliability of algorithms before deploying them in real life applications. This paper presents a long-term, large-scale dataset collected over the period of 1.5 years on a weekly basis over the University of Sydney campus and surrounds. The USyd Campus Dataset has been designed to aid in the development, validation
DOI: 10.5281/zenodo.3361855
title: The distribution data of sandhill crane in Asia during non-breeding period from our fieldwork

description: This data is the wintering distribution data of sandhill cranes collected by our research team in the fieldwork. The data has been used to publish an article in Peerj.
DOI: 10.26186/5d4a3ed53bcbf
title: Beagle Marine Park backscatter data 2018

description: This resource includes multibeam sonar backscatter data for Beagle Marine Park (Bass Strait) collected by Geoscience Australia (GA) and the Institute for Marine & Antarctic Studies (University of Tasmania; UTAS) during the period 17 – 26 June 2018 on the RV Bluefin. The survey was undertaken as a collaborative project funded through the National Environmental Science Program Marine Biodiversity Hub, with co-investment by GA and UTAS. The purpose of the project was to build baseline information for benthic habitats in the Beagle Marine Park that will support ongoing environmental monitoring within the South-east Marine Park Network as part of the 10-year management plan (2013-2023). Data acquisition for the project was completed during three separate voyages: Phase 1 - Seabed mapping by multibeam sonar; Phase 2 – Seabed imagery acquisition by Autonomous Underwater Vehicle, and sediment sampling; Phase 3 – Survey of demersal fish communities using Baited Remote Underwater Video (BRUVs). This dataset from Phase 1 comprises 11 backscatter grids derived from multibeam sonar data gridded at 1 m spatial resolution, covering a combined area of 364 km2. A detailed report on the survey is provided in: Falster, G., Monk, J., Carroll, A., Siwabessy, J., Deane, A., Picard, K., Dando, N., Hulls, J., Nichol, S., Barrett, N. 2019. Australian Marine Park Baseline and Monitoring Survey: Post Survey Report, Beagle Marine Park, South-east Marine Park Network. Report to the National Environmental Science Program, Marine Biodiversity Hub.
DOI: 10.26186/5d4a3f17dd64a
title: Beagle Marine Park backscatter data 2018

description: This resource includes multibeam sonar backscatter data for Beagle Marine Park (Bass Strait) collected by Geoscience Australia (GA) and the Institute for Marine & Antarctic Studies (University of Tasmania; UTAS) during the period 17 – 26 June 2018 on the RV Bluefin. The survey was undertaken as a collaborative project funded through the National Environmental Science Program Marine Biodiversity Hub, with co-investment by GA and UTAS. The purpose of the project was to build baseline information for benthic habitats in the Beagle Marine Park that will support ongoing environmental monitoring within the South-east Marine Park Network as part of the 10-year management plan (2013-2023). Data acquisition for the project was completed during three separate voyages: Phase 1 - Seabed mapping by multibeam sonar; Phase 2 – Seabed imagery acquisition by Autonomous Underwater Vehicle, and sediment sampling; Phase 3 – Survey of demersal fish communities using Baited Remote Underwater Video (BRUVs). This dataset from Phase 1 comprises 11 backscatter grids derived from multibeam sonar data gridded at 1 m spatial resolution, covering a combined area of 364 km2. A detailed report on the survey is provided in: Falster, G., Monk, J., Carroll, A., Siwabessy, J., Deane, A., Picard, K., Dando, N., Hulls, J., Nichol, S., Barrett, N. 2019. Australian Marine Park Baseline and Monitoring Survey: Post Survey Report, Beagle Marine Park, South-east Marine Park Network. Report to the National Environmental Science Program, Marine Biodiversity Hub.
DOI: 10.26186/5d4a3fabcffa9
title: Beagle Marine Park backscatter data 2018

description: This resource includes multibeam sonar backscatter data for Beagle Marine Park (Bass Strait) collected by Geoscience Australia (GA) and the Institute for Marine & Antarctic Studies (University of Tasmania; UTAS) during the period 17 – 26 June 2018 on the RV Bluefin. The survey was undertaken as a collaborative project funded through the National Environmental Science Program Marine Biodiversity Hub, with co-investment by GA and UTAS. The purpose of the project was to build baseline information for benthic habitats in the Beagle Marine Park that will support ongoing environmental monitoring within the South-east Marine Park Network as part of the 10-year management plan (2013-2023). Data acquisition for the project was completed during three separate voyages: Phase 1 - Seabed mapping by multibeam sonar; Phase 2 – Seabed imagery acquisition by Autonomous Underwater Vehicle, and sediment sampling; Phase 3 – Survey of demersal fish communities using Baited Remote Underwater Video (BRUVs). This dataset from Phase 1 comprises 11 backscatter grids derived from multibeam sonar data gridded at 1 m spatial resolution, covering a combined area of 364 km2. A detailed report on the survey is provided in: Falster, G., Monk, J., Carroll, A., Siwabessy, J., Deane, A., Picard, K., Dando, N., Hulls, J., Nichol, S., Barrett, N. 2019. Australian Marine Park Baseline and Monitoring Survey: Post Survey Report, Beagle Marine Park, South-east Marine Park Network. Report to the National Environmental Science Program, Marine Biodiversity Hub.
DOI: 10.17632/pchzgx8y9x.1
title: Data for: Glassy NiTi Produced with Different Cooling Times: Structural Investigation Using Molecular Dynamics Simulations
DOI: 10.7910/dvn/ef1fwq/dwd0j3
title: GSEdimers_bioassay_rawData.tab

description: Raw bioassay data for dimers
DOI: 10.7910/dvn/ef1fwq/kcu23y
title: NMRdata.zip

description: Compounds raw NMR data
DOI: 10.7910/dvn/ef1fwq/p7rtth
title: DBMP_calculation_v02.tab

description: DBMP calculation with explanation
DOI: 10.7910/dvn/ef1fwq/zds0ni
title: GSEtrimers_bioassay_rawdata.tab

description: Raw bioassay data for trimers
DOI: 10.5061/dryad.6k58bt3/1
title: Dryad Item 10.5061/DRYAD.6K58BT3/1

description: (:tba)
DOI: 10.17632/6w3vy3ybhg.3
title: Data-set for Independent Gas turbine for Electricity Generation.

description: This dataset contains the raw, filtered and related parameters for the real-power measurements in an Independent 5.68 MW Gas turbine used for Electrical power generation. It also contains the correlation matrices of all 50 features of the complete dataset and that of the real power measurement related parameters.
DOI: 10.17632/br38t938c2.3
title: Code for Data-set investigation of a Gas Turbine for Electricity Generation

description: This contains the R markdown code used to perform the filtering and descriptive analysis on the raw turbine dataset found in http://dx.doi.org/10.17632/6w3vy3ybhg.2
DOI: 10.17632/kjvs5vgkbf.1
title: Novel drug-like RAR-Beta agonist induces BRCA1 to prevent neuropathic pain
DOI: 10.17632/543cv8yfx8.1
title: Data for: The Public Health Impact of Different Microbiological Criteria Approaches for Salmonella in Chicken Parts

description: Data and code referenced in the manuscript (see Supplementary Material file for a detailed explanation).
DOI: 10.17632/8pgv5xtrtj.1
title: O-D pairs between 7:00 a.m. and 8:00 a.m. (the morning rush hour in Beijing)

description: There have been between 66,000 and 69,000 taxis in Beijing over the last several years, almost all of which were equipped with GPS recorders. For this study, we managed to obtain three-month's worth of GPS trajectory data produced by 66,646 taxis from the Beijing Municipal Commission of Transport. The interval between two adjacent data records is one minute or several minutes, depending on the performance of the different GPS recorders. Each data record contains the following information: serial number, vehicle company, terminal ID, time label, WGS84 (World Geodetic System 1984) longitude, WGS84 latitude, mesh longitude, mesh latitude, vehicle speed, vehicle direction, vehicle state, event, and elevation. For our purposes, the most important information from the data records are terminal ID, time label, WGS84 longitude, WGS84 latitude, vehicle speed, and vehicle direction.
DOI: 10.17632/vy6pd947f8.1
title: Data for: 3433064

description: Data has been analysed using R. The final R code is also attached