cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/9argzn/xhhmdh
title: Model_oc_68_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/zglcb2
title: Model_ic_12_M&A (v0=1e14).tab

description: :unav
DOI: 10.5066/p90w9jme
title: Model Archive Summary for acoustic derived suspended-sediment concentration at 14246900 Columbia River at Port Westward NR Quincy, OR

description: Model archive summary describing the development of an acoustic derived suspended sediment time series computation model. The model archive summary describes the methods and techniques used to develop the model for the Columbia River at Port Westward site - USGS site ID# 14246900. The time series suspended sediment data will help determine the suspended sediment concentration at the site in real-time enabling science entities, managers, and others, track the movement of sediment in the Columbia River.
DOI: 10.5066/p98n1gwv
title: Reported groundwater levels and groundwater pump-and-treat withdrawal volumes, former Naval Air Warfare Center, West Trenton, New Jersey, 2018

description: This dataset contains U.S. Navy contractor-reported groundwater level data measured on June 18, 2018 and reported daily total groundwater pump-and-treat withdrawal volumes for calendar year 2018 at the former Naval Air Warfare Center, West Trenton, New Jersey. This data release supports: Fiore, A.R., and Lacombe, P.J., 2020, Groundwater levels and generalized potentiometric surfaces, former Naval Air Warfare Center, West Trenton, New Jersey, 2018: U.S. Geological Survey Open-File Report 2020-1016, 28 p., https://doi.org/10.3133/ofr20201016
DOI: 10.6073/pasta/32efbf57fa480578ae7a6deae2797196
title: Structural size measurements and isotopic signatures of foraging among adult male and female Adélie penguins (Pygoscelis adeliae) nesting along the Palmer Archipelago near Palmer Station, 2007-2009

description: Sexual segregation in vertebrate foraging niche is often associated with sexual size dimorphism (SSD), i.e., ecological sexual dimorphism. We examined ecological sexual dimorphism among sympatric nesting Pygoscelis penguins near Palmer Station, Antarctica, asking whether environmental variability in the form of winter sea ice is associated with differences in male and female pre-breeding foraging niche. Each season, study nests, where pairs of adults were present, were individually marked and chosen before the onset of egg-laying, and consistently monitored. When study nests were found at the one-egg stage, both adults were captured to obtain blood samples used for molecular sexing and stable isotope analyses, and measurements of structural size and body mass. At the time of capture, each adult penguin was quickly blood sampled (~1 ml) from the brachial vein. After handling, individuals at study nests were further monitored to ensure the pair reached clutch completion, i.e., two eggs. Molecular analyses were conducted at Simon Fraser University following standard PCR protocols, and stable isotope analyses were conducted at the Stable Isotope Facility at the University of California, Davis using an elemental analyzer interfaced with an isotope ratio mass spectrometer
DOI: 10.6073/pasta/66b883bbbb12d6a87b5bf405720b00c1
title: Thicket Shoot Growth on Hog Island, VA 1990-2007

description: Mean annual shoot growth for Morella (formerly Myrica) cerifera thickets at five locations on Hog Island. The "eroding" site was lost due to complete erosion after 1997. A replacement "colonizing" site was added in 2004.
DOI: 10.6073/pasta/73ad4c4192c070a757fe7c5267f1e91d
title: 2003 GPS shorelines for Hog, Wreck, and Metompkin Islands, VA

description: This dataset was collected by Curtis Smith during the summer of 2003 as a Research Experiences for Undergraduates (REU) participant. It consists of Global Positioning System (GPS) lines collected by walking high-tide and vegetation (Hog Island only) lines using a Trimble Geoexplorer XT GPS unit. The resulting data was processed with GPS Pathfinder Office using differential correction. All data are in the UTM (zone 18) and the WGS-84 projection was used throughout. During the repeated surveys of Hog, Curtis was developing methodologies and conventions for surveying sometimes confusing high-tide lines. Each shapefile name contains the island and the date of the survey within 2003 in the form Rmmdd where mm is the month and dd is the day. For example Hog_R0717_05A.shp would be Hog Island, VA on July 17, 2003. The raw GPS position files are also included. These proprietary (.SSF) files require the Trimble Pathfinder Office software to be interpreted. Â Individual shapefiles for each survey day were later (2013) combined into a single shapefile with the island and date identified as attributes. Â
DOI: 10.6073/pasta/d9151abbd298289b824b806a88a3cc2e
title: Elevation, mesh elements, and strickler roughness coefficient for the Virginia Coastal Reserve

description: Â This dataset contains a mesh and associated elements for the southern Delmarva Peninsula
DOI: 10.7910/dvn/9argzn/4apyvu
title: Model_oc_9_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/6emmpn
title: Model_oc_22_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/9vpaob
title: Model_ic_23_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/b86nyj
title: Model_oc_97_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/bjnqpq
title: Model_ic_11_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/bjzww9
title: Model_oc_53_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ci3ztw
title: Model_ic_41_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/cuvplt
title: Model_oc_29_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/dqrqz0
title: Model_oc_35_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/fkhf70
title: Model_oc_67_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/g9qhsf
title: Model_oc_34_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/jjn85p
title: Model_ic_13_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/jyi3ae
title: Model_oc_31_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/k2oas2
title: Model_ic_19_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/kbxyzg
title: Model_oc_28_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/lk5oxv
title: Model_oc_10_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/mucic7
title: Model_oc_25_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ntsr7u
title: Model_ic_49_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/otgofc
title: Model_oc_90_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/pxinto
title: Model_ic_6_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/qauzof
title: Model_oc_20_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/qqcm6t
title: Model_ic_38_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/saxxin
title: Model_oc_58_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/suqh7c
title: Model_ic_39_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/txkycx
title: Model_oc_94_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/u1arh9
title: Model_ic_9_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/uebgh0
title: Model_oc_47_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/um4ygg
title: Model_oc_4_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/xgzn0n
title: Model_oc_74_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/yqf3km
title: Model_oc_4_M&A (v0=1e13).tab

description: :unav
DOI: 10.21227/4em3-7g35
title: Intel Open Wi-Fi RTT Dataset

description: Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range. Please see the README file for more details.
DOI: 10.21227/etbq-na65
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that contained the exact location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 160+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in both CSV and JSON formats) that were tweeted between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.3886/e119802
title: Data and Code for: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia

description: We use unique data from over 600 Indonesian communities on what individuals know about the poverty status of others to study how network structure influences information aggregation. We develop a model of semi-Bayesian learning on networks, which we structurally estimate using within-village data. The model generates qualitative predictions about how cross-village patterns of learning relate to network structure, which we show are borne out in the data. We apply our findings to a community-based targeting program, where citizens chose households to receive aid, and show that the networks that the model predicts to be more diffusive differentially benefit from community targeting<br><br>
DOI: 10.3886/e119802v1
title: Data and Code for: Network Structure and the Aggregation of Information: Theory and Evidence from Indonesia

description: We use unique data from over 600 Indonesian communities on what individuals know about the poverty status of others to study how network structure influences information aggregation. We develop a model of semi-Bayesian learning on networks, which we structurally estimate using within-village data. The model generates qualitative predictions about how cross-village patterns of learning relate to network structure, which we show are borne out in the data. We apply our findings to a community-based targeting program, where citizens chose households to receive aid, and show that the networks that the model predicts to be more diffusive differentially benefit from community targeting<br><br>
DOI: 10.5281/zenodo.3884453
title: Draft genome assembly of a Japanese Oikopleura dioica male individual (O3), using Nanopore long reads.

description: This draft assembly was used to validate the chrY scaffolds of the OSKA2016 reference genome in the publication “A genome database for a Japanese population of the larvacean Oikopleura dioica”, Development Growth and Differentiation, Wang and coll., 2020 (in press). It is provided as supplemental data for the reproducibility of this work; please note that no further polishing has been done to correct sequencing errors. Genome sequence reads were produced on a MinION sequencer (Oxford Nanopore Technologies) using high-molecular weight DNA from a male individual of the Oikopleura dioica species of zooplankton. The individual was related to the laboratory strain established from a western Japanese population that was used to produce the OSKA2016 reference genome. The raw reads were basecalled with the Guppy software version 3.3.0 using its dna_r9.4.1_450bps algorithm, and deposited in the European Nucleotide Archive (Study ID: PRJEB38559). The draft assembly was made with the Flye software version 2.7 with the options --genome-size 65m and --min-overlap 3000.
DOI: 10.5281/zenodo.3884454
title: Draft genome assembly of a Japanese Oikopleura dioica male individual (O3), using Nanopore long reads.

description: This draft assembly was used to validate the chrY scaffolds of the OSKA2016 reference genome in the publication “A genome database for a Japanese population of the larvacean Oikopleura dioica”, Development Growth and Differentiation, Wang and coll., 2020 (in press). It is provided as supplemental data for the reproducibility of this work; please note that no further polishing has been done to correct sequencing errors. Genome sequence reads were produced on a MinION sequencer (Oxford Nanopore Technologies) using high-molecular weight DNA from a male individual of the Oikopleura dioica species of zooplankton. The individual was related to the laboratory strain established from a western Japanese population that was used to produce the OSKA2016 reference genome. The raw reads were basecalled with the Guppy software version 3.3.0 using its dna_r9.4.1_450bps algorithm, and deposited in the European Nucleotide Archive (Study ID: PRJEB38559). The draft assembly was made with the Flye software version 2.7 with the options --genome-size 65m and --min-overlap 3000.
DOI: 10.5281/zenodo.3885416
title: Some Notable Records of Mayflies (Insecta: Ephemeroptera) from Big Rivers in Indiana: Methodology

description: Microhabitats sampled and collection methods used to obtain data for: Some Notable Records of Mayflies (Insecta: Ephemeroptera) from Big Rivers in Indiana.
description: Referenced by PERC Catalog Number
DOI: 10.5281/zenodo.3885417
title: Some Notable Records of Mayflies (Insecta: Ephemeroptera) from Big Rivers in Indiana: Methodology

description: Microhabitats sampled and collection methods used to obtain data for: Some Notable Records of Mayflies (Insecta: Ephemeroptera) from Big Rivers in Indiana.
description: Referenced by PERC Catalog Number
DOI: 10.6073/pasta/23a499200cc32b29c4fa6e8107ed8c7b
title: End of Year Biomass at Surface Elevation Table plots in Upper Phillips Creek marsh 1999-2017

description: Background: Brinson et al. (1995) developed a model representing the change that occurs in ecosystem state (or habitat type) along the shorezone, from the forest -&gt; high marsh -&gt; low marsh -&gt; mud flat, in response to the increased inundation caused by rising sea-level. They suggested that a seaward shift in ecosystem state is largely dependent on local slope and sediment supply. The states are associated with the dominant vegetation found within each. The most seaward (lowest in elevation) state is the mud flat. It is frequently inundated by tide and typically supports algal species. The next landward state is the mineral low marsh; it is dominated by Spartina alterniflora and is typically flooded at high tide. Sediments here may be largely mineral in origin. The next landward state is the high marsh; it may be dominated by S. patens, Distichlis spicata, and Juncus roemerianus. It is occasionally inundated by high tides and the soil is usually organic. The transition zone between the high marsh and the forest is typically dominated by Iva frutescens, Baccharis hamifolia, and Juniperus virginiana. It is only inundated during severe storm surges. The forest may be dominated by either pines or hardwoods and is again flooded with sea water only by storm surges. Brinson, M.M., R.R. Christian, and L.K. Blum. 1995. Multiple states in the sea-level induced transition from terrestrial forest to estuary. Estuaries 18:648-659.
DOI: 10.7910/dvn/9argzn/49cwwd
title: Model_oc_69_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/5bybv4
title: Model_ic_3_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/64iwvb
title: Model_oc_93_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/7dfgmd
title: Model_oc_48_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/7ow4uk
title: Model_oc_39_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/964lhn
title: Model_oc_77_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/bu86zx
title: Model_ic_34_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/cex2vr
title: Model_oc_92_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/evvul8
title: Model_ic_14_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/g6xybc
title: Model_ic_52_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/gdsqtt
title: Model_oc_60_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ihb3ii
title: Model_oc_85_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ivqn4b
title: Model_ic_15_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/kjfvsd
title: Model_oc_83_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/kkbnbz
title: Model_oc_82_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/knjaiy
title: Model_oc_32_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/kvly6z
title: Model_ic_10_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/lbhole
title: Model_oc_35_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/metelo
title: Model_oc_71_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/mxqfs3
title: Model_oc_36_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/pl7tg8
title: Model_oc_80_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/rzwbdn
title: Model_oc_49_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/un5s15
title: Model_oc_3_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/urzr5a
title: Model_oc_86_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/w2n6gj
title: Model_oc_40_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/xfchmy
title: Model_oc_30_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/xregxe
title: Model_oc_99_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ydjtnt
title: Model_oc_88_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/zoygxm
title: Model_oc_85_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/2l9fr3
title: Model_oc_6_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/3sienb
title: Model_oc_70_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/6gzxys
title: Model_oc_14_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/9cukki
title: Model_oc_58_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/9lao0b
title: Model_oc_24_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/acoitu
title: Model_oc_65_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/aqaner
title: Model_oc_4_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/dnwo2t
title: Model_oc_22_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/dtzotl
title: Model_oc_24_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/eapk2z
title: Model_oc_70_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/exiugq
title: Model_ic_24_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/fpy53n
title: Model_ic_9_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/hlflc0
title: Model_ic_26_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/i2uzxq
title: Model_ic_37_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/jbje1f
title: Model_ic_28_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/jequyn
title: Model_oc_42_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/ji7wat
title: Model_oc_49_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/khrntu
title: Model_oc_9_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/lkqpnv
title: Model_oc_14_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/mhxqbx
title: Model_oc_72_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/n9e5gp
title: Model_oc_26_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/nfqkcx
title: Model_oc_30_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/ojb6bu
title: Model_oc_33_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/oobyd8
title: Model_oc_73_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/q3dz1u
title: Model_oc_16_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/qs3i5s
title: Model_oc_61_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/spxdoc
title: Model_oc_70_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/t47kwc
title: Model_oc_69_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/vyo9xh
title: Model_ic_12_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/xncwaa
title: Model_ic_3_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/znhgke
title: Model_oc_23_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/01rfrn
title: Model_oc_44_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/1vy0yq
title: Model_ic_34_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ahnjkm
title: Model_ic_59_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ckhtcf
title: Model_ic_55_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/de2cge
title: Model_ic_54_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/dmakwb
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