cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/fz2q-th61
title: Dataset for Android Malware Detection

description: The Android application samples for evaluation come from the following three sources: the Drebin dataset, AMD dataset, and the Google Play Store. We downloaded 7,681 popular benign applications from Google Play Store.Based on the above data sources, the following two datasets were generated for testing: Drebin4000 and AMD6000.We randomly selected 4,000 benign samples and 4,000 malicious samples from the Drebin dataset to form the Drebin4000 dataset.We randomly selected 6,000 benign samples from the benign downloaded applications and 6,000 malicious samples from the AMD dataset to form the AMD6000 dataset.
DOI: 10.17632/63h6s3kt6k.1
title: Data for: Investigation of photochemical conversion processes in aromatic hydrocarbons of Balakhani oil well

description: Figure 1, Electron absorption spectra of BO and its components in various concentrations. Figure 2, UV-absorption spectra of dilute solutions of sample; Figure. 3. IR spectra of BO separated groups after 2, 4, 6, 8 and 10 hours of photoirradiation. Figure 4. The optical density of the 1730 cm-1 adsorption band of different aromatic groups
DOI: 10.21227/t99g-1j67
title: Simple Model

description: v1.0 Categories houses into 4 consumption patterns, based on available months.
DOI: 10.6084/m9.figshare.13139699
title: Global Snow Drought Data Set - Standardized Snow Water Equivalent Index (SWEI)

description: This data set contains information about snow droughts (or deficits in snow water equivalent, SWE) and their characteristics (e.g., duration and intensity) across the globe. The snow drought information is derived using SWE from a global reanalysis over 1980-2018, which was standardized using methods described in Huning and AghaKouchak (2020a). We provide the 3-month standardized snow drought index (SWEI) fields used to characterize and monitor snow droughts in the NetCDF4 (classic) format. Changes in snow drought characteristics for seven study regions around the world: the western United States, Europe, Hindu Kush and Central Asia, greater Himalayas, eastern Russia, extratropical Andes, and Patagonia are also provided.<br>For more information about the standardized SWE index (SWEI), refer to Huning and AghaKouchak (2020a).<br>This data set provides one SWEI file for each study year. The following data flags/codes are used in the SWEI files:FillValue: 9.9692e+36 (No data, i.e., missing value.)MissingValue: Same as FillValueNoSnowPermValue: -999 (No SWEI value is ever assigned to the grid cell. Refer to Huning and AghaKouchak (2020a).)NoSnowTempValue: -888 (SWEI value not assigned to the grid cell during this time of the year. Refer to Huning and AghaKouchak (2020a).)<br>As a condition of using data from this collection, you must cite this data set/collection and the corresponding publication: 1) Huning, L.S., and AghaKouchak, A. (2020b): Global Snow Drought Data Set, <i>figshare</i>, Collection, https://doi.org/10.6084/m9.figshare.c.5055179.<br>2) Huning, L.S., and AghaKouchak, A. (2020a): Global Snow Drought Hot Spots and Characteristics, <i>Proceedings of the National Academy of Sciences</i>, 117(33), 19753-19759, https://doi.org/10.1073/pnas.1915921117.
DOI: 10.21227/a4f2-w212
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. 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.-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 253,389 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.6084/m9.figshare.12637424
title: Global Snow Drought Data Set - Drought Characteristics

description: This data set contains information about snow droughts (or deficits in snow water equivalent, SWE) and their characteristics (e.g., duration and intensity) across the globe. The snow drought information is derived using SWE from a global reanalysis over 1980-2018, which was standardized using methods described in Huning and AghaKouchak (2020a). We provide the 3-month standardized snow drought index (SWEI) fields used to characterize and monitor snow droughts in the NetCDF4 (classic) format. Changes in snow drought characteristics for seven study regions around the world: the western United States, Europe, Hindu Kush and Central Asia, greater Himalayas, eastern Russia, extratropical Andes, and Patagonia are also provided in an ASCII file.For more information about the standardized SWE index (SWEI) and the regions listed above, refer to Huning and AghaKouchak (2020a).<br>The ASCII file summarizes the change in snow drought characteristics for seven study regions around the world: the western United States, Europe, Hindu Kush and Central Asia, greater Himalayas, eastern Russia, extratropical Andes, and Patagonia. Each row in the file contains information for one of these regions. The first column lists the names of the regions. Columns 2-4 contain information related to the change in 1) total duration of snow droughts, 2) average duration of snow droughts, and 3) exceedance probability of snow drought intensity. A positive (negative) change indicates that an increase (decrease) in the respective variable occurred during the latter half of years in the period 1980-2018. Percent changes are given for both the total and average snow drought durations as the difference normalized by the value of the variable from the first half of the time period. The exceedance probability refers to the chance that the drought intensity (i.e., strength or severity of drought conditions) is greater than the average intensity from the first half of the record. Changes in the probabilities are provided as differences.<br>As a condition of using data from this collection, you must cite this data set/collection and the corresponding publication:<br>1) Huning, L.S., and AghaKouchak, A. (2020b): Global Snow Drought Data Set, <i>figshare</i>, Collection, https://doi.org/10.6084/m9.figshare.c.5055179.2) Huning, L.S., and AghaKouchak, A. (2020a): Global Snow Drought Hot Spots and Characteristics, <i>Proceedings of the National Academy of Sciences</i>, 117(33), 19753-19759, https://doi.org/10.1073/pnas.1915921117.<br>
DOI: 10.6084/m9.figshare.12637424.v1
title: Global Snow Drought Data Set

description: This data set contains information about snow droughts (or deficits in snow water equivalent, SWE) and their characteristics (e.g., duration and intensity) across the globe. The snow drought information is derived using SWE from a global reanalysis over 1980-2018, which was standardized using methods described in Huning and AghaKouchak (2020).<br>The ASCII file summarizes the change in snow drought characteristics for seven study regions around the world: the western United States, Europe, Hindu Kush and Central Asia, greater Himalayas, eastern Russia, extratropical Andes, and Patagonia. Each row in the file contains information for one of these regions. The first column lists the names of the regions. Columns 2-4 contain information related to the change in 1) total duration of snow droughts, 2) average duration of snow droughts, and 3) exceedance probability of snow drought intensity. A positive (negative) change indicates that an increase (decrease) in the respective variable occurred during the latter half of years in the period 1980-2018. Percent changes are given for both the total and average snow drought durations as the difference normalized by the value of the variable from the first half of the time period. The exceedance probability refers to the chance that the drought intensity (i.e., strength or severity of drought conditions) is greater than the average intensity from the first half of the record. Changes in the probabilities are provided as differences.<br>For more information about the regions listed above and the standardized SWE index (SWEI) used to generate the data provided here, refer to Huning and AghaKouchak (2020).<br><br><b>References</b>Huning, L.S., and AghaKouchak, A. (2020), Global snow drought hot spots and characteristics, <i>Proceedings of the National Academy of Sciences</i>, in press.
DOI: 10.6084/m9.figshare.12545093
title: Data supporting: A high-throughput method for measuring critical thermal limits of leaves by chlorophyll imaging fluorescence

description: Data supporting article "A high-throughput method for measuring critical thermal limits of leaves by chlorophyll imaging fluorescence". Contains measures of thermal tolerance (Tcrit from temperature-dependent basal fluorescence [T-F0]) assays of leaves of four species under different surface and ramp rate experimental conditions.
DOI: 10.5281/zenodo.3892975
title: RAW data_Symbiotic translocation supports acclimatization of primary coral polyps to decreasing seawater pH

description: Raw data of the work "Symbiotic translocation supports acclimatization of primary coral polyps to decreasing seawater pH"
DOI: 10.5281/zenodo.3892974
title: RAW data_Symbiotic translocation supports acclimatization of primary coral polyps to decreasing seawater pH

description: Raw data of the work "Symbiotic translocation supports acclimatization of primary coral polyps to decreasing seawater pH"
DOI: 10.21227/xk34-em77
title: test

description: A/Bi/LC128f100e0.0399all
DOI: 10.17632/rssn4kxw5y.1
title: Data for: Analysis of two heat wave driven ozone episodes in Barcelona and surrounding region: meteorological and photochemical modeling.

description: This dataset contains the information to access to the online data and the model outputs used in this work.
DOI: 10.17632/w5v43by8vw.1
title: Data for: Data on distribution of heat transfer coefficient and profiles of velocity and turbulent characteristics behind a rib in pulsating flows

description: Raw experimental data on heat transfer from the wall are presented in the form of electric signals (voltage in V). The data were collected from 42 measurement tracks; 1 reference resistor 226 Ω in the temperature measurement circuit with a resistance thermometer UTR; 1 signal from resistance thermometer UT; 4 signals from 4 reference resistors 5 Ω in circuits of measurement elements UR 10 1 - UR 10 4. Measurement elements Ri corresponded to the following channels: - R1 – R10 – channels 32 to 23; - R11 – R26 – channels 48 to 33; - R27 – R42 – channels 16 to 1; - UT – channel 17; - UTR – channel 20; - UR 10 1 – UR 10 4 – channels 18, 19, 21, 22. Elements R1 – R42 were placed on the wall with a streamwise spacing of 9.5 mm. Voltage drop measured across the reference resistors yielded the magnitude of electric current in the respective circuits. Each file contains the data for one regime of air flow. File names specify the volumetric flow rate (Q, m3/h) and forcing frequency (f, Hz). R0i file contains signals for estimation of reference resistances of measurement elements at 23°С.
DOI: 10.34740/kaggle/dsv/1611305
title: LifeStyle Courses - Udemy (39K+ course)
DOI: 10.14457/cmu.res.2000.48
title: ความสัมพันธ์ระหว่างดัชนีราคาหุ้นตลาดหลักทรัพย์กับเครื่องชี้เศรษฐกิจมหภาค
DOI: 10.14457/cmu.the.2000.243
title: แบบจำลองขีดจำกัดสมรรถนะของเทอร์โมไซฟอนปิดสองสถานะแบบเอียง
DOI: 10.14457/cmu.the.2000.244
title: ความเครียดในงานและความยึดมั่นผูกพันต่อองค์การของพยาบาลประจำการ โรงพยาบาลชุมชนเขต 10
DOI: 10.14457/cmu.the.2000.245
title: การศึกษาการกระจายบริการสาธารณะของกรุงเทพมหานคร : การประยุกต์ใช้ตัวแบบผสมผสาน
DOI: 10.14457/cmu.the.2001.375
title: การดำเนินธุรกิจชุมชนในชุมชนชนบทภาคเหนือ
DOI: 10.14457/cmu.the.2000.246
title: ปัจจัยที่เอื้อต่อการประกันคุณภาพการศึกษามหาวิทยาลัยเชียงใหม่
DOI: 10.14457/cmu.the.2000.247
title: การสนับสนุนทางสังคมและพฤติกรรมส่งเสริมสุขภาพของผู้สูงอายุโรคหลอดเลือดหัวใจตีบ
DOI: 10.14457/cmu.the.2001.376
title: ผลของการจัดโปรแกรมส่งเสริมสุขภาพ เพื่อควบคุมและป้องกันภาวะแทรกซ้อนในผู้สูงอายุที่มีความดันโลหิตสูง อำเภอบางกระทุ่ม จังหวัดพิษณุโลก
DOI: 10.14457/cmu.the.2000.248
title: พฤติกรรมภาวะผู้นำของผู้บริหารโรงเรียน สังกัดสำนักงานการประถมศึกษาอำเภอแม่แตง จังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.2000.249
title: ที่มาของความเจริญเติบโตของสาขาอุตสาหกรรมในประเทศไทยในช่วงปี พ.ศ.2529-2536
DOI: 10.14457/cmu.the.2001.377
title: การเลือกผู้นำท้องถิ่นในชุมชนชนบท
DOI: 10.14457/cmu.the.2001.378
title: กลยุทธ์การบริหารมหาวิทยาลัยในช่วงภาวะเศรษฐกิจถดถอย : กรณีศึกษามหาวิทยาลัยเชียงใหม่
DOI: 10.14457/cmu.the.2001.379
title: ความสัมพันธ์ระหว่างโครงสร้างจุลภาคและสมบัติทางไฟฟ้าของเซรามิกในระบบพีแซคที
DOI: 10.14457/cmu.the.2001.380
title: การประเมินผลการเรียนกิจกรรมชุมนุมคณิตศาสตร์โดยใช้แฟ้มสะสมงานชั้นมัธยมศึกษาตอนต้น โรงเรียนหอพระ จังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.2001.381
title: การติดตามการกระจายตัวของอลูมินาในสารละลายโซเดียมโดเดคิลซัลเฟต
DOI: 10.14457/cmu.the.2001.382
title: ความคาดหวังของสังคมกับพฤติกรรมและบทบาทความเป็นผู้หญิงในชุมชน
DOI: 10.14457/cmu.the.2001.383
title: การใช้ยาเสพติดของนักศึกษามหาวิทยาลัย
DOI: 10.14457/cmu.the.2000.250
title: การศึกษาขบวนการทางสังคมแนวใหม่ กรณีศึกษาเครือข่ายฮักเมืองน่าน
DOI: 10.14457/cmu.the.2001.384
title: การเมืองในการสร้างอัตลักษณ์ของกลุ่มผู้ติดเชื้อเอชไอวีในจังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.2001.385
title: ความสามารถในการวาดภาพระบายสีของเด็กที่มีความบกพร่องทางสายตา
DOI: 10.14457/cmu.the.2001.386
title: วิธีการวิเคราะห์ขนาดระบบโฟโตโวลตาอิกแบบแยกอิสระขนาดเล็กที่สัมพันธ์กับโหลดและระดับความน่าเชื่อถือในการจ่ายกำลังไฟฟ้า
DOI: 10.14457/cmu.the.2000.251
title: ปัญหาการรับพม่าเข้าเป็นสมาชิกอาเซียน
DOI: 10.14457/cmu.the.2001.387
title: ปัญหาที่เกี่ยวกับยาและผลของการแก้ไขปัญหาในผู้ป่วยนอกโรคเบาหวานในโรงพยาบาลนครพิงค์
DOI: 10.14457/cmu.the.2001.388
title: การวิเคราะห์ภาพถนนเพื่อหาขอบทางโดยวิธีวิชวลคิว
DOI: 10.14457/cmu.the.2001.389
title: ผลของการสนับสนุนด้านข้อมูลและอารมณ์ต่อความวิตกกังวลของสมาชิกในครอบครัวผู้ป่วยบาดเจ็บศีรษะ
DOI: 10.14457/cmu.the.2000.252
title: การนำเสนอความเป็นตัวตนของผู้หญิงบริการในสถานบันเทิงบาร์เบียร์ จังหวัดเชียงใหม่
DOI: 10.14457/cmu.the.2001.390
title: การพัฒนาโปรแกรมเอทีพีจีสำหรับวงจรดิจิตอล โดยใช้วิธีบูลลีนแซททิสฟายอะบิลิตี้
DOI: 10.14457/cmu.the.2001.391
title: การพัฒนาหน่วยบูรณาการการเรียนการสอน เรื่อง ป่าชายเลน สำหรับนักเรียนชั้นประถมศึกษาปีที่ 6 โรงเรียนบ้านแหลมมะขาม สังกัดสำนักงานการประถมศึกษาอำเภอสิเกา จังหวัดตรัง
DOI: 10.14457/cmu.the.2001.392
title: การสื่อสารเพื่อการเลือกคู่ของกลุ่มวัยรุ่นที่ชอบเที่ยวกลางคืน
DOI: 10.14457/cmu.the.2001.393
title: การพัฒนาวิธีการสกัดสารหอม 2-อะเซทิล-1-ไพโรลีน จากตัวอย่างพืช
DOI: 10.34740/kaggle/dsv/1611314
title: IPL 2020 Data
DOI: 10.34740/kaggle/dsv/1611406
title: BSTM_Project
DOI: 10.6084/m9.figshare.13175018
title: Effect of the Initial Vortex Vertical Structure on Early Development of an Axisymmetric Tropical Cyclone

description: This is an idealized simulated TC by axisymmetric, nonhydrostatic cloud model version 1 (CM1, release 19). The tangential velocity decays to 0 at <i>z</i> = 20 km for ZDD20 experiment and decays to 0 at <i>z</i> = 10 km for ZDD10 experiment. Time resolution is 1 h.
DOI: 10.22008/fk2/cvpujl/zdz6tb
title: RetMIP_CFM-Cr_Dye-2_16_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/hrvxzf
title: RetMIP_CFM-Cr_FA_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/7zpppz
title: RetMIP_CFM-Cr_Dye-2_long_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/0yioje
title: RetMIP_CFM-Cr_Dye-2_16_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/0b2ucr
title: RetMIP_CFM-Cr_Dye-2_long_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/n43hal
title: RetMIP_CFM-Cr_FA_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/ikekbl
title: RetMIP_CFM-Cr_KANU_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/sugfoj
title: RetMIP_CFM-Cr_Summit_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/glbhb1
title: RetMIP_CFM-Cr_Summit_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/h2gune
title: RetMIP_CFM-Cr_KANU_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/r63jbe
title: RetMIP_CFM-KM_Dye-2_long_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/thpkdj
title: RetMIP_CFM-KM_Dye-2_long_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/qywfeh
title: RetMIP_CFM-KM_FA_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/tgkm8g
title: RetMIP_CFM-KM_KANU_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/b5bolc
title: RetMIP_CFM-KM_FA_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/v47so9
title: RetMIP_CFM-KM_Dye-2_16_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/f1iekt
title: RetMIP_CFM-KM_Summit_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/ilnfw3
title: RetMIP_CFM-KM_Dye-2_16_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/vf7ski
title: RetMIP_CFM-KM_KANU_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/t7vzhr
title: RetMIP_CFM-KM_Summit_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/d5sfur
title: RetMIP_DMIHH_Summit_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/qlxfhc
title: RetMIP_DMIHH_Dye-2_16_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/0pbcgo
title: RetMIP_DMIHH_KANU_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/wgm6bx
title: RetMIP_DMIHH_Dye-2_long_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/1kth3u
title: RetMIP_DMIHH_FA_3hourly_values.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/nt3wem
title: RetMIP_DMIHH_Summit_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/utmkjg
title: RetMIP_DMIHH_Dye-2_long_3hourly_columns(1).nc

description: :unav
DOI: 10.22008/fk2/cvpujl/aundbi
title: RetMIP_DMIHH_FA_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/bfhirp
title: RetMIP_InfoCFM.docx

description: :unav
DOI: 10.22008/fk2/cvpujl/ow8eic
title: RetMIP_DMIHH_Dye-2_16_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/9zcacc
title: RetMIP_DMIHH_KANU_3hourly_columns.nc

description: :unav
DOI: 10.22008/fk2/cvpujl/h3grsw
title: RetMIP_IMAUFDM_Summit_3hourly_columns.nc

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DOI: 10.22008/fk2/cvpujl/gderbd
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